👋 Hi, it’s Gaurav and Kunal, and welcome to the Insider Growth Group newsletter, our bi-weekly deep dive into the hidden playbooks behind tech’s fastest-growing companies.

Our mission is simple: We help you create a roadmap that boosts your key metrics, whether you're launching a product from scratch or scaling an existing one.

What We Stand For

• Actionable Insights: Our content is a no-fluff, practical blueprint you can implement today, featuring real-world examples of what works—and what doesn’t.

• Vetted Expertise: We rely on insights from seasoned professionals who truly understand what it takes to scale a business.

• Community Learning: Join our network of builders, sharers, and doers to exchange experiences, compare growth tactics, and level up together.

Before We Dive In: Key Terms

A few terms come up repeatedly in this article.

Here is what they mean:

Genealogy - The study of family history and lineage. Genealogists trace who their ancestors were, where they came from, when they were born and died, and how family lines connect across generations. In practice this means hunting through historical documents like census records, immigration files, birth certificates, and military registrations to piece together your family’s story going back decades or centuries.

Genealogy expertise - The practical skills required to do that research successfully: knowing how to read and interpret old handwritten records, searching for ancestors whose names were spelled differently across documents, navigating conflicting information across multiple sources, and following family lines through migration patterns and name changes.

The Growth Lever Most Product Teams Overlook

There is a problem hiding inside many of the best consumer products today: they work beautifully for users who already know how to use them. The harder question is what happens to the millions of people who arrive curious but lack the expertise to get value out of it.

That question sat at the center of a major growth challenge at Ancestry. Every year, millions of people purchased an Ancestry DNA Kit, a simple saliva test that returns your ethnicity breakdown and connects you with genetic relatives. It is a delightful, shareable experience that resonated deeply with younger adults. After receiving their DNA results, many of these users entered the Family History subscription funnel, where Ancestry invited them to go deeper and explore historical records to trace their lineage.

The Family History subscription is where Ancestry’s core revenue lives. It gives subscribers access to billions of historical documents: census records, immigration files, vital records, military registrations, and more. For older users who had spent years building genealogy expertise, the product performed well, with healthy single-digit monthly churn after four or more months.

But for younger users arriving through the DNA funnel, something was not working. Their conversion rate from DNA Kit user to paying Family History subscriber was 83% lower than that of older, experienced users. Conversion rate here means the percentage of DNA users who entered the subscription trial and went on to become active paying subscribers.

With millions of DNA kit users entering this funnel every year, even a fractional improvement in that conversion rate would translate into tens of millions of dollars in incremental annual revenue. The opportunity was clear. The real question was why younger users were struggling so much and what could actually be done about it.

The counterintuitive part: younger users were not less motivated. They were often more excited after receiving their DNA results than older users who came in through organic genealogy interest. The problem was not enthusiasm - it was genealogy expertise. Ancestry had built a product that rewarded it but never taught it.

Today we break down the two-part playbook Ramesh and his team used to close that gap: an AI co-pilot that replaced the need for genealogy expertise, and a ground-up rebuilt search experience designed specifically for a new generation of users.

This post is written for product managers and founders who have a product with a meaningful learning curve, where the users who succeed tend to already bring relevant experience to the table. If you have ever looked at your conversion data and noticed that a newer or younger audience is underperforming relative to your core base, and you are not entirely sure why, this story will feel familiar.

Introducing Ramesh Krishnamurthy

Ramesh Krishnamurthy is a AI Product Lead at Meta with a career spanning some of the most product-forward companies in tech. At Ancestry, Ramesh led the Search & AI Products with the goal of evolving the genealogy subscription product to appeal to a broader audience, specifically younger adults. His mandate was to find out why younger users were not converting, and build the product interventions to fix it. Before Ancestry, he built products at Amazon and Glassdoor, developing a deep foundation in consumer product design, data-driven experimentation, and AI product development at scale.

Understanding Ancestry’s Two-Product Growth Challenge

To understand why this problem existed, it helps to understand how Ancestry’s two core products relate to each other.

The DNA Kit is a one-time purchase, typically around $99. You mail in a saliva sample and within a few weeks receive a detailed ethnicity breakdown showing your genetic origins by region, plus a list of genetic relatives who have also taken the test. The experience is immediate, visual, and highly shareable on social media. It consistently resonated with younger adults who were curious about their heritage but had no prior interest in genealogy research.

The Family History subscription costs ~ $40 per month and gives access to billions of historical documents. This is where the deep research happens: tracing your family tree back multiple generations, finding immigration records, reading century-old census entries, and piecing together the story of where your family came from. Traditionally, this product resonated most strongly with older adults who had spent years or decades developing genealogy expertise.

The natural growth engine Ancestry had built was to use the DNA Kit as an acquisition channel that fed curious new users into the Family History subscription. The emotional hook of seeing your ethnicity results would ideally translate into a desire to learn more, and the subscription would be how you scratched that itch.

For older users, this worked well. For younger users coming in through the DNA funnel, the gap between what they arrived expecting and what the product actually required of them was significant enough that most did not make it through.

The older cohort that converted well had not learned genealogy from Ancestry either. They had learned it elsewhere (through relatives and Youtube videos), years before ever signing up, and were arriving as already-competent users. Ancestry had been quietly benefiting from an invisible pipeline of pre-educated customers without ever realizing it. When that pipeline stopped being the primary acquisition source, the underlying product gap became hard to ignore.

CASE STUDY #1: Building the AI Co-Pilot That Replaced Your Expert Friend

How Ancestry built an AI assistant that acted like having a knowledgeable friend or family member sitting next to you, and drove 40% higher repeat visits plus a 17bps lift in subscription conversion.

The Discovery: What User Research Actually Revealed

When the team started digging into why younger users were not converting, the initial hypothesis was that the product was simply too complex. But complexity alone is not an actionable diagnosis. Ramesh and the team needed to understand what specifically was breaking down and when.

What emerged from that research was a pattern that changed how the team thought about the entire problem. Successful users, the ones who converted and stayed, had almost universally built genealogy expertise before they arrived at Ancestry. Not through the product itself, but through outside channels: a genealogist friend or family member who had walked them through how to interpret records, YouTube tutorials that explained census data, genealogy blogs that decoded the vocabulary of historical research. They had done the equivalent of taking a course before using the software.

Younger users had done none of this. They arrived curious and emotionally engaged after seeing their DNA results, but they had no frame of reference for what they were looking at inside the product. When they encountered their first census record and saw a handwritten entry with an unfamiliar abbreviation, they had no one to ask. When a search returned no results because they had spelled a name the modern way rather than the historical variant, they had no way to know that was the problem. When two records showed conflicting birth years for the same person, they had no framework for deciding which one to trust.

The aha moment from research: The team was not looking for a feature gap or a UX flaw. What they found was something more fundamental: the product’s entire value delivery mechanism depended on skills that lived outside the product, in YouTube videos and genealogist friends. Every user who succeeded had essentially onboarded themselves through a parallel, entirely invisible curriculum. Ancestry had never built an easy to digest curriculum. They had just benefited from users who consumed it off their platform.

The research surfaced three specific capability gaps: interpreting historical records, tracing relationships across generations, and verifying conflicting sources when documents disagreed.

But the deeper insight was not just that younger users lacked these skills. It was that they did not particularly want to develop them.

For older users, genealogy was a hobby. The research process itself was the reward - the hours spent hunting through records, the satisfaction of piecing together a family line, the sense of connection it created. In one user research session, a customer captured it perfectly: “Genealogy is the only thing I can talk to my dad about without us arguing.” The process had meaning independent of the outcome.

For younger users, that was not the job to be done. They were excited about their family history. They were not excited about becoming genealogists. They wanted the discovery - the moment of finding out where their family came from, who their ancestors were, what their story was. They just did not want to spend months acquiring expertise to get there.

That distinction changed everything about how the team thought about the solution. The product did not need to teach younger users genealogy. It needed to remove the requirement that they learn it at all.

What They Built and How They Validated It

The team built an AI assistant trained on Ancestry’s knowledge base, historical records corpus, and genealogical methodology. It was integrated directly into the DNA customer journey, appearing at the moment of highest user curiosity: right after someone received their DNA results and was beginning to explore what they meant.

Users could now ask questions in natural language and receive answers grounded in real genealogical knowledge:

• “How do I research my Irish ancestry?” would return guidance on the specific record types available for Ireland, the time periods most relevant to Irish immigration to the US, and which searches were most likely to yield results given that context

• “How are me and Brenda Coffield related?” would trace the shared ancestor path and explain the relationship in plain language

• “Where do my Norwegian genes come from?” would connect the ethnicity estimate to historical migration patterns and suggest which geographic regions of Norway might be most relevant to explore

The Ancestry AI assistant appears as a persistent panel on the right side of the homepage, surfacing suggested questions like “How can I trace my ancestors as far back as possible?”

Before launching broadly, the team ran a structured validation process with Ancestry’s in-house genealogy experts. Every AI response was evaluated across four dimensions before the product went live:

But the four dimensions were not just a pre-launch checklist. They became the foundation for an ongoing evaluation system because in a domain like genealogy, a single confidently wrong answer can destroy a user’s trust in everything that follows. If the AI tells someone their ancestor immigrated from the wrong country, or misidentifies a family relationship, the user does not just lose faith in that response. They lose faith in the product.

This is where evals became critical. The team needed a systematic way to continuously monitor AI response quality across the four dimensions at scale - not just sample-check it before launch. That meant building a feedback loop where flagged responses from real users fed back into the evaluation process, expert reviewers periodically audited live outputs, and the bar for accuracy was treated as a living standard rather than a one-time gate.

The broader principle for any team building AI on top of specialized knowledge: pre-launch expert review gets you to a trustworthy starting point, but it is the ongoing eval infrastructure that keeps you there. Without it, model updates, new record types, or edge cases that were not in your original test set will quietly erode the accuracy that trust was built on.

This expert review process was not just a quality gate. It was a trust-building mechanism. They were shipping something that users would rely on to understand real information about their real families. In high-stakes or emotionally meaningful domains, the user cannot fact-check you - which means the standard for accuracy has to be set by people who can, and maintained continuously long after launch.

Every model update is scored against expert-labeled Q&A pairs before it ships. Once live, real queries are continuously sampled, reviewed by both user behavior signals and genealogy experts, and fed back to raise the bar for the next release.

Impact

• 40% increase in repeat visits from DNA-funnel users who previously dropped off quickly after their first session

• 17 basis point increase in subscription conversion from DNA users into paying Family History subscribers

• Users who engaged with the AI assistant spent meaningfully more time in the product and explored more records per session than those who did not

Key Learnings

• AI’s highest-value use case in complex products is ability transfer, not automation. The AI co-pilot did not automate genealogy research. It transferred the ability to do research to users who previously could not. That is a different and often more valuable framing for where AI belongs in a product.

• The expert friend model is a design target: When you identify that successful users benefited from having a knowledgeable person in their corner, that is a concrete product spec. The goal is to replicate that experience at scale.

If you’ve gotten this far, scroll to the bottom for the full 🔥 IGG Playbook on how to apply this framework to your own product.

CASE STUDY #2: Smart Search

The AI Co-Pilot Solved One Layer. A Second Layer Remained.

The AI assistant helped users understand what they were looking at and know what to search for next. That addressed the knowledge barrier. But understanding alone was not enough. Users still needed to act: to run a search, interpret a results page, and find a record that meant something to them.

And that is where the team found a second, distinct failure mode. Even when users had the knowledge to run a search, the search experience itself was introducing friction that exceeded the motivation younger users arrived with.

The key insight: Two separate products addressing the same symptom. The AI co-pilot and Smart Search were not redundant. They solved different layers of the same problem. If the team had shipped only the AI assistant without fixing search, users would have understood what to look for but still struggled to actually find it. Solving the knowledge layer without solving the execution layer would have left significant conversion lift on the table.

Why the Search Experience Was Working for One Audience and Not the Other

Genealogy search is not like searching on Google, where partial or imprecise queries still return useful results. It requires structuring queries with partial or uncertain information, interpreting results that span multiple records across decades, and refining searches iteratively when initial results come back empty.

The existing search experience had been built over years for users who knew exactly what they were doing. Expert genealogists loved it. It was powerful, deep, and flexible. But for a younger user on their first or second search, the interface presented more parameters and options than felt approachable.

The team had tried to address this through incremental changes before. Each attempt ran into the same tension: any improvement meaningful enough to help a new user felt like too much change to the core user base that had built their research workflows around the existing interface.

The two audiences needed fundamentally different things.

The team also explored whether search was even the right mechanism. An internal prototype tested a more proactive AI-driven experience where the co-pilot would surface results directly rather than waiting for users to query. It did not hold up. Genealogy discovery is inherently iterative - users need to compare multiple results and progressively narrow down and a conversational interface was not built for that kind of back-and-forth. Search, rebuilt for the right audience, remained the better foundation.

The Decision to Build a Parallel Experience Rather Than Redesign

Instead of trying to find a compromise that would serve both audiences reasonably well, the team made a more decisive call: build a separate search experience purpose-built for new users, and preserve the existing experience as an opt-in choice for the core audience.

The team focused the new Smart Search experience around a specific insight about new user behavior. Almost universally, the first thing a new Ancestry user searches for is themselves, their parents, or their grandparents. This is the moment of truth: the search that tells a user whether the product actually works for them. If that first search succeeds and delivers a real result, the user’s confidence in the product spikes. If it fails or returns an overwhelming page of undifferentiated records, they are unlikely to try again.

Every design decision in the new experience was oriented around maximizing the probability of a successful first search for this specific scenario.

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Four Changes That Defined the Smart Search Experience

The Free Credits Decision: Letting the Product Earn the Commitment

New users received a small number of credits they could use to view actual historical records without starting a subscription. The intent was to let the product prove itself before asking for anything in return.

The value of an Ancestry record is not abstract. When a new user finds a census entry listing their great-grandmother’s name, age, and the address where she lived in 1920, something shifts. The subscription decision becomes straightforward. They have already experienced what they would be paying for. The paywall is not blocking the product anymore. It is the natural next step after the product has already delivered.

The lesson is not to give away your product. It is to sequence your friction so that commitment asks come after value delivery, not before. For products where the first meaningful outcome is emotional or discovery-driven, this sequencing can meaningfully change conversion rates.

Impact

• 25 basis point increase in subscription sign-ups from new users who went through the Smart Search experience

• Higher engagement at every stage of the funnel: more users initiating searches, more engaging meaningfully with results, more reaching the paywall with enough context to convert

• Zero disruption to the core expert user base, who retained access to the full-featured search experience as their default

Key Learnings

• When one experience cannot serve two audiences well, the answer is two experiences. The opt-in mechanic for existing users is the key enabler. It protects the workflows your best users have built while giving you complete design freedom for the audience you are trying to grow.

• Find the moment-of-truth action and optimize everything around it. For Ancestry, this was the first search a new user runs. Map the equivalent in your product: the first action that answers the question “does this actually work for me?” and treat that moment as the primary design target for your new user experience.

• Sequence friction after value for emotionally-driven products. Products where the first meaningful outcome is emotional or discovery-driven should place their paywall after that outcome, not before it. Let the product demonstrate its value before asking for commitment.

🔥 IGG Playbook: How to Diagnose and Fix a Conversion Problem Using AI

Most conversion problems are an ability gaps. This playbook walks through how to find yours and use AI to close it.

Step 1: Separate your conversion data by user cohort

Your overall conversion rate is hiding the real problem. Aggregate numbers flatten the difference between users who succeed and users who fail.

• Segment conversion by acquisition channel, user tenure, and any proxy for prior domain knowledge

• Look for a cohort that converts significantly below your average - that is your signal

Step 2: Interview the two populations - converters and churners separately

Do not just analyze the drop-off funnel. Talk to both groups and ask the same questions.

• What did you do in your first session?

• Where did you get stuck?

• What did you do outside the product to get unblocked?

The answer to that last question is where your AI opportunity lives. If churners say “I gave up” and converters say “I watched YouTube tutorials” or “I asked my friend,” the product has an invisible prerequisite it never built.

Step 3: Name the specific skills your product silently requires

Turn the qualitative finding into a precise capability list. Not “users found it confusing” - but exactly what a user needs to know to complete their first meaningful action.

For Ancestry this was three things: interpreting historical records, tracing relationships across generations, and reconciling conflicting sources. Each one was a discrete skill gap that AI could address directly.

The more specific your list, the more targeted your AI intervention can be.

Step 4: Build AI at the moment of highest intent, lowest ability - not as a help feature

This is the most important tactical decision. Most teams build AI as a support layer - something users invoke when stuck.

• Map the exact step in your new user journey where the ability gap causes drop-off

• Build the AI intervention there, as the primary interface for that moment

• The goal is not to answer questions. It is to get the user to their first meaningful outcome without them needing to develop expertise first

Step 5: Measure AI impact on cohort conversion

The wrong metric is AI usage rate or session length. The right metric is whether the cohort that was failing is now converting.

• Instrument conversion separately for users who engaged with the AI vs. those who did not

• Track repeat visits and paywall reach for the previously-failing cohort specifically

• If AI is working, you will see the conversion gap between your two populations narrow — not just overall conversion tick up slightly

Step 6: If the AI closes the knowledge gap but conversion still lags, look for a second layer of friction

Ancestry needed two interventions, not one. The AI co-pilot closed the knowledge gap. Smart Search closed the execution gap. Neither alone was sufficient.

After shipping your AI intervention, re-run your cohort analysis. If the gap has narrowed but not closed, ask: what is still making the product hard to use for this audience even when they know what to do? That second layer is usually a product experience problem, not a knowledge problem and it requires a product solution, not a better AI prompt.

Want help on any product growth challenges you have?

Let’s talk

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👋 Hi, it’s Gaurav and Kunal, and welcome to the Insider Growth Group newsletter, our bi-weekly deep dive into the hidden playbooks behind tech’s fastest-growing companies.

Our mission is simple: We help you create a roadmap that boosts your key metrics, whether you’re launching a product from scratch or scaling an existing one.

What We Stand For

• Actionable Insights: Our content is a no-fluff, practical blueprint you can implement today, featuring real-world examples of what works—and what doesn’t.

• Vetted Expertise: We rely on insights from seasoned professionals who truly understand what it takes to scale a business.

• Community Learning: Join our network of builders, sharers, and doers to exchange experiences, compare growth tactics, and level up together.

In the past year, companies like Klarna and Shopify made headlines by replacing large parts of their customer support teams with AI. But the backlash came quickly. Users felt abandoned, and internal teams felt undermined.

At LinkedIn, the approach is very different.

Instead of replacing agents, they’re scaling them.

Instead of removing the human touch, they’re using AI to deepen it.

The goal isn’t to deflect more tickets. It’s to transform support reps into trusted advisors. People who build relationships, surface upsell opportunities, and help users get the most out of LinkedIn’s Hiring product when engaging with customers.

A trusted advisor doesn’t just fix problems. They anticipate needs, offer proactive guidance, and create enough confidence that a customer is willing to pay a premium to work with them.

This matters even more at a company like LinkedIn, where trust is foundational to the brand and the Hiring product is used for critical business outcomes like filling roles, growing teams, and driving company growth. Customers aren’t just troubleshooting software — they’re making decisions that directly impact their ability to scale their company.

This post breaks down how LinkedIn rethought its support model using AI agents:

• Agents that resolve common workflows from start to finish without human involvement

• Agents that help reps evolve from copy-paste operators to advisors who can upsell and educate

• Agents that free up team bandwidth so support becomes a growth function, not just a cost center

• Agents that shift support upstream from a last resort when a user wants to churn, to catching users earlier when a customer is still open to learning, expanding, or re-engaging.

By rethinking not just what support does, but when it shows up, LinkedIn transformed it from a reactive cost into a proactive revenue driver.

Self-Serve Rate

Before we dive in, let’s define the KPI that keeps showing up in this post: self-serve rate.

Self Serve Rate = The percentage of users who engage with an AI agent or support agent and then return to the product without needing to escalate to another expert.

Sub-Metrics that roll up to Self Serve Rate:

• AI Engagement Rate: % of users who interact with the agent when it's surfaced

• Drop-Off After Engagement: % of users who abandon the session after talking to the agent (lower is better)

• Return to Core Product Flow: % of users who complete or resume the task they were working on after AI engagement

• Escalation Rate: % of conversations that still result in a human agent stepping in (lower is better)

• Resolution Confirmation: % of AI interactions marked as “resolved” by the user (via feedback, survey, or inferred from behavior)

• Repeat Contact Rate: % of users who return with the same issue within X days (higher repeat = lower quality self-serve)

Introducing

Jay is Group Product Manager at LinkedIn, where he leads AI strategy and execution across go-to-market products. He’s behind core initiatives like LinkedIn’s Virtual Chat Assistant and the GTM knowledge graph and Internal products for Reps, driving how AI agents scale support, sales, and onboarding. With deep experience in marketplaces and enterprise platforms, Jay is helping define what agentic workflows look like inside one of the world’s largest professional networks.

Jonathan is a Product Manager at LinkedIn focused on building AI agents that make go-to-market teams faster and more scalable. He led the development of LinkedIn’s multi-agent platform, GTM data graph, and Virtual Chat Assistant. His work blends AI, data, and product design to turn complex workflows into seamless, automated experiences.

Case Study #1: Reframing Support as a Value Moment, Not a Cancellation Trigger

Context:

Support at most companies has traditionally been buried in settings pages, billing menus, or at the bottom of help centers. By the time users reached out, they had often already made up their mind to cancel and were stuck.

That was exactly how LinkedIn’s Hiring product operated just a year ago. The chat function was hidden two or three layers deep, typically accessed only through billing, account settings, or subscription management.

These were low-leverage moments. The customer had already decided they weren’t getting value. Support was simply managing the exit.

Problem:

The support team noticed that too many of their conversations were reactive. Users came in already frustrated or ready to downgrade. And because of where the chat surfaced, it shaped the entire tone of the interaction.

Instead of asking:

“How do I get better candidates?”
They were asking:
“How do I cancel my plan?”

For LinkedIn, this was a missed opportunity especially for newer customers who hadn’t yet seen the full value of the Hiring tools. These users didn’t need a refund. They needed guidance.

New Solution:

The team redesigned where and how users encountered AI-powered support.

Chat was no longer buried in settings. Instead, it was surfaced throughout high-intent moments in the Hiring product:

• On the homepage dashboard after logging in

• Within the candidate search tool

• On the job posting setup screen

• On the main Help Center for Hiring Products

• And on pages where usage dropped off, like InMail outreach or saved search alerts
  

Rather than waiting for the customer to disengage, AI agents were placed right at the decision point offering real-time help, education, and suggestions.

For example, a first-time recruiter at a mid-sized staffing firm looking to fill roles in the electric vehicle (EV) industry might ask:

“How do I get more qualified candidates for EV roles?”

The AI agent would respond with targeted suggestions, like:

• “Try using the ‘green energy’ or ‘electric mobility’ skill filters”

• “Here are 3 job descriptions that worked well for similar roles”

• “You may want to expand your location radius to attract hybrid workers”

These were proactive, consultative responses that made the product feel smarter, and the user feel guided.

Chat surfaced in-product at key moments like job setup and search filters

Impact:

• 80% of customer questions are now resolved directly through chat, up from under 30% a year ago.

• Self Serve rate increased to 65% overall from 0% a year ago.

Learnings:

• Placement matters just as much as the agent itself. Putting support at the moment of curiosity instead of the moment of frustration dramatically improves tone and outcomes.

• First-time users are the best audience for proactive support. They have a much higher learning curve and need even more guidance than your power users.

If you’ve gotten this far, you may be ready to navigate to the 🔥 section and check out our Playbook now.

Case Study #2: From Copy-Paste Agent to Trusted Advisor

Context:

LinkedIn’s Hiring support team was designed to help users quickly resolve issues. But that also meant support agents spent most of their time jumping between tools — searching knowledge base articles, copying content, drafting emails, and following up. It was execution-heavy work.

These were skilled team members, but they rarely had time to think beyond the task in front of them.

They were helping users reset passwords, but not asking if they were on the right plan.
They were fixing campaigns, but not suggesting how to improve performance.

Support was responsive, but not proactive.

Problem:

Support agents had little time to act like advisors. They were bogged down by manual tasks:

• Searching for the right help article

• Copying it into an email

• Drafting a message

• Sending it

• Manually following up

Because of the workload, most interactions ended with solving the issue - not identifying expansion opportunities.

New Solution:

LinkedIn introduced AI sub-agents to automate the heavy lifting and free up reps to do higher-value work.

Each support agent was now paired with a series of purpose-built sub-agents that could:

• Summarize the support request

• Scan the full knowledge base instantly

• Draft a personalized response email

• Queue it for human review and send it

Instead of juggling tools, reps reviewed and personalized the response. With more time back, they could do something that wasn’t happening before: look deeper.

They started asking questions like:

“Is this customer actually on the right Hiring plan?”
“Have they activated the key features that drive performance?”
“Could they get better results on a different tier?”

One common example: A Paid user struggling to get candidate responses was automatically flagged for a product mismatch. The agent, now freed from execution work, could recommend moving to Recruiter Lite, explaining how additional InMail credits and smart targeting would improve their outreach.

Sub-agent case summary → auto-drafted next steps → advisor review + share with customer

Impact:

• Self Serve Rate increased by 15%+ by avoiding escalation to specialized reps for cases solved with AI as the AI Agent could now perform actions that would have needed escalating those cases.

Learnings:

• Giving support teams AI-powered assistants removes low-leverage work and lets them act more like product consultants.

• Upsells are much easier when reps aren’t stretched thin. They can actually spot the misalignment between a user's needs and their current plan.

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Case Study #3: Making Every Rep a Subject Matter Expert

Context:

In many support teams, there’s always “that one rep” who knows how to handle the complicated cases the one others ping when they get stuck.

At LinkedIn, the Hiring support team saw this pattern regularly. Reps were constantly turning to peers for help on hard questions:

• “Have you ever handled billing for a customer hiring in multiple countries?”

• “What’s the right policy for InMail outreach to candidates in Germany under GDPR?”

• “How do I explain Recruiter seat sharing rules for agencies with multiple brands?”

These weren’t simple knowledge base lookups. They required context, regulation awareness, and a deep understanding of LinkedIn’s product limitations, billing structure, and compliance policies.

This tribal knowledge slowed down resolution time, introduced inconsistency, and made scaling harder. Most of all, it created a ceiling on how confident any new rep could be.

Problem:

Reps spent too much time asking each other for help instead of resolving issues directly. Knowledge was trapped in Slack threads and institutional memory.

Even simple questions like “Can I use one Recruiter seat across two brands if the users work on different hiring pipelines?” could take 20 minutes to answer — with reps tracking down someone who’d “seen this one before.”

That limited both productivity and customer experience. It also made support too dependent on who was online and available to help.

New Solution:

LinkedIn rolled out AI agents designed specifically to surface expert-level responses and complete actions for complex, high-context workflows.

Instead of relying on peer support, reps could now query the AI directly inside their support console.

The system was trained on:

• Historical rep responses to edge cases

• Internal policy documentation

• Billing rules by product tier and geography

• Regulatory constraints like GDPR or Canadian anti-spam rules

For example:

• When a customer asked how to run a Hiring campaign across Canada and the US using one contract, the AI pulled cross-border invoicing policies and generated a detailed response tailored to their tier and product configuration.

• When asked if a customer could send InMails to candidates in the EU without consent, the AI flagged GDPR constraints and provided a compliant communication template.

• When reps needed to know if billing addresses could be split across departments in a single enterprise account, the AI pulled relevant contract language and populated the appropriate form for submission.

And in high-confidence cases, it completed backend actions like updating contract details or making billing or credit card edits — directly through integrations with internal tools.

Rep → AI Agent → Get context on issue → Taking action on behalf of rep

Impact:

• Internal rep-to-rep escalation requests fell by 20%, freeing up senior team members from repeat consulting.

Learnings:

• AI doesn’t have to replace human reps. In this case, it made every rep stronger by embedding institutional knowledge into their workflow.

• Most teams underestimate how much time is spent asking each other for help. Codifying that into an accessible AI layer unlocks scale.

• AI makes it possible for reps to act like trusted advisors without needing deep industry knowledge. Instead of memorizing best practices for every vertical, reps can rely on AI to surface relevant insights and suggestions in real time.

🔥 Insider Growth Playbook: Designing Multi-Step AI Agents That Actually Take Action

Most teams use AI to generate content or answer questions. But the next wave of product innovation isn’t about sounding smart it’s about building agents that do real work inside your product.

Whether you're running a vertical SaaS platform, a marketplace, or a PLG product, this playbook walks through how to identify and design AI agents that own entire workflows, from input to execution.

Here’s how to find your first (or next) agent use case:

Step 1: List All User Workflows That Span 3+ Steps

Map your product’s top 15–20 user actions across core flows like onboarding, billing, usage, and account management.

Look for:

• Repetitive processes

• Flows with structured inputs + logic-based decisions

• Work that users currently stall on or abandon midway

Examples:

• Submitting a compliance document

• Migrating data from another tool

• Setting up hiring campaigns across multiple geos

• Requesting financing or issuing a payout

Step 2: Flag the Ones With Friction, Drop-Off, or Back-and-Forth

Good agent candidates are workflows where:

• Users frequently bounce or get confused

• The task spans multiple tabs, systems, or approvals

• Users say, “I wish someone could just do this for me”

Step 3: Identify What the Agent Needs to Do (Not Just Say)

Ask: if an AI agent owned this workflow, what would it do?

Not just:

“Here's how to set up a campaign”

But:

“I’ve launched the campaign for you based on your past behavior, budget, and region. Here's what to review.”

Break down the agent's job:

• Interpret intent

• Collect missing inputs

• Trigger backend actions

• Generate outputs

• Confirm status and success

Step 4: Segment Workflows Into 3 Types

Focus your agent roadmap on structured and semi-structured work first.

Step 5: Find Data Bottlenecks and Breakpoints

Workflows break when:

• Users don’t know what to input

• They need to switch between systems

• You rely on humans to approve or push buttons

A great agent bridges those breaks:

• “I’ve found 3 files missing from your last import. Would you like me to reprocess them?”

• “Your invoice failed because the address format is invalid. I’ve fixed it based on your previous entries.”

Step 6: Score Each Candidate Workflow by Impact

Use a quick prioritization matrix:

Step 7: Design the Agent Like a PM — Not a Bot

Your agent is a new product experience. Treat it like a feature:

• What’s the entry point?

• What does the user see, say, or do?

• How do you handle fallback?

• How do you log actions and show transparency?

Think: trust, control, outcome.

Step 8: Build for Action First, Intelligence Second

You don’t need an LLM to start. Some of the highest-impact agents are:

• Auto-form fillers

• Plan switchers

• Config checkers

• Reminder or reschedule triggers

You can always layer in natural language or reasoning logic later. But start with delivering results.

If you’re thinking about where to start or how to go from prototype to production, we’ve helped teams build AI agents that handle everything from onboarding flows to billing automation.

Whether you're scoping your first agent or expanding to new workflows, we can help you:

• Identify high-leverage use cases

• Design multi-step agents that work across tools

• Build guardrails and fallback logic

• Track business impact beyond just deflection

Need help designing or implementing AI agents in your product?
Reach out. We’re happy to help.

Talk to us.

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• Actionable Insights: Our content is a no-fluff, practical blueprint you can implement today, featuring real-world examples of what works—and what doesn’t.

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• Community Learning: Join our network of builders, sharers, and doers to exchange experiences, compare growth tactics, and level up together.

The Pendulum Swings Back

For the last decade, the tech industry optimized for one thing above all else: scale.

In the pursuit of predictable, repeatable growth, we dismantled the creative heart of the “Product Trio.” We took Product Management, Design, and Engineering and split them so far apart that they became distinct islands with their own languages, their own incentives, and their own bureaucracies. We built “Assembly Lines.” In this model, the PM wrote a 20-page PRD, the Designer spent three weeks in Figma creating high-fidelity mocks, and the Engineer finally received a ticket to turn those pixels into code.

Velocity plummeted.

For years, we were told this was the price of success. We were taught to accept this as the inevitable process tax required to maintain massive cash cows and satisfy the demands of middle management. We convinced ourselves that “moving slow and aligning” was the only way to protect the core business.

But in what we now recognize as the specialization trap of the zero interest / cheap funding era, we traded competitive speed for administrative safety. When capital was free, you could afford to have five people doing the work of one. Today, capital has a cost, and in a constrained market, Speed is the only competitive advantage. The companies winning today aren’t the ones with the most robust “agile ceremonies” or the most beautiful internal documentation. They are the ones with the fastest loop from Idea → Live → Data.

We are entering a new era. The industry is moving away from the role of the pure coordinator—the person whose day is defined by aligning stakeholders, writing status updates, and moving cards on a board. The pendulum is swinging back toward the high-agency operator.

I need to be crystal clear: the bar has fundamentally been raised.

This doesn’t mean you need to hire a full-time builder or convert all your PMs to coders tomorrow. But you must accept that the status quo is gone.

To be fair, we have always advocated for lean validation. Frameworks like Litmus proved years ago that you don’t need a village of five specialists just to test an idea. We have long had the ability to gauge interest and willingness to pay using high-fidelity Figma prototypes or painted-door tests.

But idea validation is no longer enough. The definition of “validation” has shifted from the mockup to the implementation.

Previously, there was a massive chasm between getting a customer to say “I would buy this” (idea validation) and actually handing them a functional feature to see if they use it (implementation validation). Crossing that chasm required scarce engineering resources, which bottlenecked the entire process.

Now, the near-zero cost of building means there is no excuse for stopping at a prototype. You can build the actual, functional application and sell it directly to clients to get candid, behavioral feedback on the whole solution. It just requires one person with generalist skills who is dangerous enough to execute end-to-end.

We call this archetype the Product Builder. This post unpacks why this shift is happening, how it interacts (and clashes) with traditional structures, and how you can harness it to drive revenue.

The Collaboration: Two Perspectives on Velocity

To understand this transformation, we have combined two perspectives that span the spectrum of modern product leadership.

Justin Bauer (Ex-CPO at Amplitude; Founder, Calibre AI)

Justin was the Chief Product Officer at Amplitude, where he scaled the organization from a handful of engineers through IPO. He saw how specialization creeps in as organizations scale–not because anyone designs it that way, but because it’s the natural consequence of a product getting more complex and a team getting bigger. Now, through Calibre AI, he works with product teams to reinvent their development process for the AI era, cutting through the process layers that accumulated over years of scaling.

Gaurav Hardikar (Ex-VP Product & Growth at HomeLight, Co-Founder @ Insider Growth Group)

Gaurav represents the tactical, “in-the-trenches” execution of this shift. During his time at HomeLight, Gaurav fundamentally restructured how product and growth teams operated. He dismantled the traditional triad to build an AI-native organization, replacing “coordinating” PMs with “Product Builders” who own the outcome from the first prompt to the final production deploy.

Together, their experiences confirm a singular truth: The limiting factor in 2026 is no longer the ability to code or the ability to design; it is the agency to execute.

The Additive Evolution: How We Got Here

The Product Builder is not a role created in a vacuum; it is the logical evolution of the multidisciplinary operator. But it is not a universal replacement for every role at every company. Its relevance depends entirely on your market position and your need for velocity.

We see the history of the tech worker as a three-act play: the rise of the scrappy generalist, an over-correction into hyper-specialization, and now, the AI-fueled return to the generalist.

Phase 1: The Scrappy Generalist (The Growth Hacker) | 2010–2014

In the early 2010s, engineering teams were built for core infrastructure—stable and slow. Marketers, frustrated by the inability to run experiments, became “Growth Hackers.” They learned just enough SQL and HTML to be dangerous and still ship.

• The Intent: Drive growth. Ask for forgiveness, not permission.

• The Failure: It lacked production-grade quality. These hacks lived in “shadow IT” environments that engineers eventually had to delete because they were unscalable and insecure.

Phase 2: The Over-Correction (The Specialized Assembly Line) | 2015–2022

As capital became virtually free, specialization became a status symbol. We broke roles into tiny pieces: UXR, UI, Frontend, Backend, and Product Ops. When velocity died, we tried to fix it with “Bridge” roles like Growth or Data PMs.

• The Intent: Ensure “Enterprise Grade” quality at scale.

• The Failure: The Handoff Tax. Every decision required an alignment meeting. While “Bridge” PMs were more technical, they were still managing a roadmap rather than executing the solution. Velocity was sacrificed at the altar of risk mitigation.

Phase 3: The Return of the Generalist (The AI-Native Builder) | 2023–Present

This is our current reality. The Cost of Shipping has hit near zero. AI unlocks a return to the generalist by removing the technical barriers that forced specialization in the first place. Tools like Cursor, Claude Code, v0, and Figma-to-Code plugins allow one person to execute the work of the entire Trio.

• The Shift: As Gokul Rajaram recently noted, the “Traditional PM” is dying. The industry is shifting toward individuals who build the end-to-end product. LinkedIn has already leaned into this, rebranding its APM program to Associate Product Builder (APB).

• The Outcome: The new leader is a creator of work, not just a manager of it.

But it’s not a silver bullet. Context matters.

While the rise of the Builder is undeniable, it is not a universal replacement for every role at every company.

• Where it’s a clear win: If you are an AI-Native startup (like Perplexity or Midjourney) or a Challenger Brand, barriers to entry are zero and competition is infinite. You don’t have a brand moat yet. Your only survival mechanism is being first. You need Builders who can ship features daily to stay relevant.

• Where you should pause and think: If you are Airbnb, Affirm, or NerdWallet, trust and compliance is paramount to your stability as a company. If a builder “moves fast and breaks things” in a checkout flow or a loan application, you lose millions or face regulatory fines. Here, the assembly line of QA, Legal, and Security is a feature, not a bug. You need deep specialists to protect the core.

That being said, we would argue that every company is now in a race for velocity.

Even in trust-heavy companies, the Builder role will emerge in specific zones. For example, a Growth Team at NerdWallet focused on spinning up landing pages (LPs) or top-of-funnel experiments is the perfect home for a Product Builder. They can generate dozens of LP tests with some direction on hypotheses to a few AI agents, all building new marketing assets without touching the regulated core banking infrastructure or involving additional members of the team.

The Rule of Thumb: Hire Builders to find value (0-1). Hire Specialists to protect value (1-N).

The Archetypes of the Product Builder

The limiting factor is no longer the ability to code; it is having the taste and intuition to find Product-Market Fit (PMF). We’ve identified three dominant archetypes. While they all ultimately drive revenue, their method of attack (their leading indicator) is different.

The GTM-Facing Builder

The Philosophy: “The fastest way to revenue is to stop building things nobody wants.”

• The Old Way (The Spec): You write a PRD for a calculator tool. You wait 3 weeks for design. You wait 2 sprints for engineering. Time to Live: 8 Weeks.

• The New Way (The Build): You identify the need. You use an LLM to script the logic and v0 to build the UI. You deploy it to a sub-domain yourself. Time to Live: 48 Hours.

• Primary Lever for Revenue: Validation Speed. They save the company millions by killing bad ideas in days, not months. This is not limited to small features, this builder will build what it takes to build a business case with real customers.

The Craft-Facing Builder

The Philosophy: “The fastest way to revenue is to build trust through quality.”

• The Old Way (The Handoff): Design creates a beautiful interaction in Figma. Engineering simplifies it to save time. The final product feels “clunky.” Users churn because it feels cheap. This is the “Handoff Tax.”

• The New Way (The Build): The designer uses Figma-to-Code, Cursor, or Claude to write the actual Tailwind CSS. They submit a Pull Request, not a picture. The vision is preserved 100%.

• Primary Lever for Revenue: Perceived Value. They drive conversion and retention by ensuring the product feels premium and trustworthy.

Founder “The Ultimate” Mode Builder

The Philosophy: “The fastest way to revenue is to take more shots on goal.”

• The Old Way (The Committee): To build a feature, you need a PM (Strategy), an EM (Feasibility), and a Designer (UX). They spend 40% of their time in “alignment meetings.”

• The New Way (The Build): One person owns the stack. This doesn’t have to even be a past engineer. In fact, most times it’s not. They use AI to handle the “grunt work” of database migrations, allowing them to focus purely on which features to build, how the system should function, and user psychology. Zero communication overhead.

• Primary Lever for Revenue: Iteration Velocity. They out-learn the competition by shipping 10x faster.

When something like the below is possible, you know the base expectations have changed.

Case Study 1: The GTM Builder

The Problem:

During Gaurav’s time at HomeLight, the team identified a potentially massive market segment. However, in the standard quarterly planning cycle, the idea was deemed too risky. Without proven demand, the core product and engineering leadership could not justify allocating a full pod to build it.

Under the traditional assembly line model, testing this would have required a dedicated PM, a designer, and two engineers—a burn rate of $50k/month for a feature that might flop. The initiative was stuck in prioritization purgatory: valuable enough to discuss, but too unproven to build.

The Solution:

Instead of fighting for roadmap space, Gaurav deployed a single GTM Builder to bypass the queue entirely. The mandate was clear: “Don’t ask for engineers. Prove that this is WORTH building in a week.”

The Builder executed to earn the right to invest further:

1. The “Scrappy” Backend: They didn’t wait for the platform team to provision a scalable environment. They spun up a basic backend themselves to handle the core logic and data flow. It wasn’t “enterprise-ready,” but it was functional.

2. The Transaction (Stripe): Because modern tools make payments trivial, the Builder integrated Stripe directly. They didn’t need a “Billing Team” or a lengthy approval process; they just needed an API key to start capturing real credit cards immediately.

3. The Outreach: They launched a direct outbound campaign pitching the value proposition, driving traffic to a working prototype that could actually service the user.

The Impact:

In 3 weeks, the initiative had generated real revenue and a committed waitlist.

This data changed the conversation. The initiative went from a risky bet to a proven winner. When they eventually brought in the core engineering team, the spec wasn’t based on guesses—it was based on automating a workflow that was already generating cash. The Builder hadn’t just built a prototype; they had built the business case that unlocked the resources.

The Lesson:

In the 0-1 phase, you aren’t buying software; you are buying truth. The “GTM Builder” proves the check before the tech. Specialists are expensive waiting rooms for information; Builders are cheap iteration loops that get you to the truth faster.

If you’ve gotten this far, you may be ready to navigate to the 🔥 section and check out our Playbook now.

Case Study 2: The Craft Builder

The Problem:

As HomeLight expanded to serve a complex ecosystem of agents, buyers, sellers, and lenders, the engineering landscape fractured. The team was spinning up new applications and repositories to handle these distinct personas. It became critical to have a unified design system that could be easily distributed across every application to maintain brand trust.

In the legacy model, this was a logistical nightmare. Designers would create beautiful component libraries in Figma, but implementing them across three or four different repos was the antithesis of speed. For a backend-focused engineering team, manually porting pixel-perfect buttons to multiple codebases wasn’t just low priority—it was a misuse of high-leverage resources. This created roadmap entropy, where the design system lived perfectly in Figma but was fragmented and broken in production.

The Solution:

Instead of hiring a specialized “Design Systems Engineer,” Gaurav empowered an existing Product Designer to evolve into a Craft Builder. They weren’t a coder by trade, and they weren’t a new hire. They simply adopted the new toolset available to them.

This Builder was given access to the codebase and the autonomy to ship frontend changes directly. Instead of creating a “picture” of a component and hoping an engineer would build it correctly four times, they used Figma MCP to translate design tokens directly into Tailwind configurations. They used Cursor to prototype the actual React components and push them to the production repo.

The “Technical” Myth:

A common objection is: “Most designers aren’t technical enough for this.” But this designer didn’t need to learn complex computer science. The bar for what is considered “technical” has changed. Connecting Figma to an IDE via AI agents isn’t “engineering”—it’s modern design. While it required some initial collaboration with engineering to set up the environment, that partnership made the designer more confident, not less.

The Impact:

The “handoff” was eliminated because the design was the code. Frontend development time dropped by 70%. The distributed design system, originally slated to take six months of engineering coordination, was deployed across multiple apps in eight weeks.

The Lesson:

Designers and PMs have tricked themselves into believing that touching code is “too technical.” AI tools allow your team to bypass the translation layer entirely. If your designer can’t tweak a component and your PM can’t prompt a functional UI change, you are stuck in the old way . In the AI era, the handoff is a bug, not a feature.

Case Study 3: The Builder Reboot

The Problem:

An early-stage startup with a 20-person engineering team kept shipping large bets that missed the mark. The scope was bloated, customer validation was thin, and direction kept changing after launch. At their current pace, the next major product was projected to take 9-12 months to deliver.

The Validation Trap:

A common question here is: “Why didn’t they just use the Litmus Framework to validate this before building?”

The answer lies in scale vs. speed. The Litmus Framework is powerful for organizations (typically >100 people) where you need to coordinate distinct GTM and Product teams to ensure alignment. But in a 0-1 environment, coordination is the bottleneck. Even a “lean” process requires a meeting. When you are trying to find a vein of gold, you don’t need a meeting between the geologist and the miner; you need a geologist who can dig.

The Solution:

The CTO stepped back into an IC role to become the Ultimate Builder. He bypassed the entire organizational layer—no spec handoffs, no coordination meetings, no sprint planning.

He used AI tools to write production code for the first time in four years, working against a fresh codebase while talking directly to customers every day. The feedback loop wasn’t: Customer → Sales → PM → Ticket → Engineer. The feedback loop was: Customer → CTO → Code.

The Impact:

A single builder with high agency and AI tools delivered in two months what a 20-person team would have taken nearly a year to ship. The beta generated immediate customer pull because the person building it was the same person hearing the customer pain.

The Lesson:

Traditional validation frameworks (like Litmus) manage the handoff of signal between teams. The Builder collapses the stack entirely. In 0-1, the bottleneck isn’t engineering capacity—it is coordination cost. Every layer between the customer and the keyboard dilutes the signal. The Builder removes the noise.

Justin’s Proof Points: Real-World Manifestations

Justin sees these archetypes appearing across the most successful teams in his network, and they serve as proof that this isn’t just a theory—it’s the new standard for elite teams.

1. The Evolved Founder [Ultimate Builder]
   Justin worked with a former PM who co-founded a company right before the ChatGPT-3 boom. This individual didn’t hire a massive engineering team. Instead, he leaned into the AI tools. Today, he spends 80% of his time coding. He submits nearly as much code as his CTO co-founder while maintaining full product ownership. He isn’t a “PM who codes”—he is a Builder.

2. The Customer-Centric CTO [GTM Builder]
   Justin advises a startup CTO who has changed his hiring profile. He no longer hires “product engineers” who wait for a spec. He hires “PM Builders.” These individuals work directly with customers, identify the friction, and build the feature. If the feature gains traction and needs to scale to their entire user base, it is then handed over to a specialized Platform Engineering team to be rebuilt for scale. This ensures they never waste time “over-engineering” a feature that nobody wants.

3. The “Design Engineer” [Craft Builder]
   In Justin’s network, the most sought-after designers are those who write all the code necessary for both polishing features and making UX improvements. They don’t leave it to an engineer to “best effort” the animations or the transitions. They ensure the high-fidelity vision actually makes it into production. They are the guardians of the user experience, and they use code as their tool.

The Trap: When “Cheaper” Means “Worse”

This may be the most critical section of this entire article.

We are seeing a dangerous trend in teams that rush to convert PMs into Builders: The “Feature Factory” on steroids.

Because shipping is cheap, teams stop asking “Should we build this?” and start saying “It only takes 2 days, let’s just ship it.” The result? You get 10x the features, 10x the code bloat, and 10x the customer confusion—but zero increase in hits on goal.

The common concern brought up about the “Builder PM” is that they stop thinking and start tinkering. When the cost of construction drops to zero, the cost of Strategy and Taste skyrockets.

We argue that when this happens, the builder is not fitting into the role expectations of a builder, and instead starts trending towards being a traditional “growth hacker”. This is not the purpose of the role, but we acknowledge that there needs to be some framework to prevent and monitor this.

The New Gatekeeping: Protecting the Signal

In the old world, engineering capacity was the gatekeeper. You couldn’t build bad ideas because you didn’t have the engineers to spare. In the new world, that constraint is gone. You must manufacture artificial friction to replace it.

Here are the three gates we recommend to ensure Builders don’t just generate noise:

Gate 1: The “Write-Before-Code” Rule (Strategic Friction)

Just because you can build it in 48 hours doesn’t mean you skip the strategy.

• The Rule: No line of code is written (or prompted) until a 1-Page “Bet” is approved.

• The Content: It must define the Customer Pain, the Hypothesis, and the “Kill Criteria” (e.g., “If <10% of users click this in 3 days, we delete it”).

• The Why: This forces the Builder to solve the problem in their head before they solve it in the IDE. It prevents “tinkering.”

Gate 2: The “Silent Beta” (Brand Friction)

Builders can ship to production, but they cannot ship to everyone.

• The Rule: All Builder experiments launch to a <5% Segment or a “Lab” environment first.

• The Why: This protects your brand and your “core” users from the chaos. If the feature is a “miss” (which 80% will be), it dies silently without confusing your entire user base.

Gate 3: The “Trash” Default (Technical Friction)

This is the hardest cultural shift.

• The Rule: Every feature a Builder ships is Temporary by Default.

• The Mechanism: If a feature does not hit its success metrics within [X] weeks, it is automatically reverted. It does not “earn” the right to be refactored by Core Engineering until it proves revenue/retention.

• The Why: This prevents your codebase from becoming a graveyard of failed experiments.

The Takeaway: Velocity is a vector: it has Speed and Direction. The Builder gives you Speed. The Gate gives you Direction. Without the Gate, you can’t enforce moving fast with intention.

🔥 Insider Growth Group Playbook: Hiring and Empowering Your Product Builders

Transforming your organization into an AI-native, builder-first culture is not a matter of changing titles—it’s a matter of changing incentives and operations.

Step 1: Audit Your “Waiting Rooms”

Map out your current development process. Identify every point where a project stops for “approval,” “handoff,” or “alignment.”

• The Litmus Test: If a PM has to wait for an engineer to change a headline, a copy line, or a button color, you have a specialization problem. You are paying a “Process Tax” that is killing your velocity.

Step 2: The “Speed Test” Interview

Resumes and traditional interviews are useless for identifying Builders. Builders build.

• The Prompt: Give the candidate a real business problem and 48 hours.

• The Goal: Build a functional tool or feature using AI.

• The Evaluation: * Do they have “Taste”? Does the solution actually solve the user problem?

  • Can they navigate the “Last Mile”? Anyone can generate a “toy” with AI. Can they handle the latency, the error states, and the deployment?

  • Agency: Did they ask for permission/clarification on every detail, or did they make strategic assumptions and keep moving?

Step 3: Identify and Hire for the “T-Shape”

Stop looking for 10/10 mastery in a single silo. Look for the “T-Shape” that favors Agency over Depth.

• 9/10: Agency / “Getting sh*t done.” The ability to find a way around a blocker without asking for help.

• 8/10: Product Strategy. Understanding why we are building.

• 7/10: Visual Design & UX Taste. The ability to create a high-utility interface.

• 5/10: Technical Literacy. Using AI to write, debug, and push code. (Remember: AI bridges the other 5 points of code).

Step 4: Kill the PRD, Start with the Bet

The era of the 30-page requirement document is over. A document is a hypothesis; a build is a test.

• The New Standard: Force your PMs and Designers to move from writing about the product to building the product. The Spec is no longer a doc; it is a functional URL or a Repo that an engineer can actually use.

• The Constraint (Gatekeeping): Do not confuse building with tinkering. Before a Builder touches a prompt, they must write a 1-Page Bet defining the customer pain and the “Kill Criteria.”

• The Bar: If a PM can’t use an LLM to spin up a solution to validate a problem, they aren’t a Product Builder—they are a project administrator. The goal isn’t to replace engineers; it’s to stop wasting their time on unproven ideas.

Step 5: Know Your Battlefield (Where Builders Win vs. Lose)

The Product Builder is a “Wartime” role. They thrive where velocity is the only moat. They struggle where stability is the product.

• The “Velocity War” (Hire Builders):
  If you are an AI-Native startup or a Challenger Brand, you are in a market where barriers to entry are zero. You don’t have a brand moat yet. Your only survival mechanism is being first. You need Builders who can ship experiments daily to find value (0-1).

• The “Moat Optimization” (Hire Specialists):
  If you are Airbnb, Affirm, or a Bank, your moat is Trust and Compliance. A broken checkout flow costs millions; a slightly slower feature launch does not. Here, the “Assembly Line” (QA, Security, Legal) is a feature, not a bug. You need Specialists to protect value (1-N).

The Rule of Thumb: Hire Builders to find the truth (Innovation). Hire Specialists to scale the truth (Optimization).

Conclusion: Starting vs. Scaling

The core takeaway is simple: Specialization is for scaling. Generalization is for starting.

If you are in a 0-1 phase, whether that’s a new startup or a new department inside a public company, don’t hire a large team.

Hire a Product Builder. They are the high-leverage operators who turn AI into revenue by doing the work of three specialists in half the time. They are the ones who will navigate the uncertainty of the AI era and find the path to revenue while your competitors are still “aligning” on the spec.

Don’t Start from Scratch. We have already built the infrastructure for this transition. Stop guessing at the job description and stop using generic interview questions.

Access our gated Product Builder Hiring Kit to get:

• The GTM Builder JD: The exact “Forward Deployed Founder” template we use.

• The Craft Builder JD: The “Product Designer (Builder Track)” role definition.

• The “Speed Test”: The 48-hour take-home prompt that reveals agency instantly.

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Our mission is simple: We help you create a roadmap that boosts your key metrics, whether you're launching a product from scratch or scaling an existing one.

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• Actionable Insights: Our content is a no-fluff, practical blueprint you can implement today, featuring real-world examples of what works—and what doesn’t.

• Vetted Expertise: We rely on insights from seasoned professionals who truly understand what it takes to scale a business.

• Community Learning: Join our network of builders, sharers, and doers to exchange experiences, compare growth tactics, and level up together.

A pattern we keep seeing with AI companies is that things fall apart after the demo.

The product looks impressive in a controlled environment. The workflows are clean. The results feel obvious. But once the product hits real enterprise systems, real data, and real constraints, implementation slows down or stalls entirely. Integrations become brittle. Trust starts to erode. ROI gets fuzzy. Deals quietly die.

Enterprise buyers are tired of this.

There’s a real sense of AI fatigue right now. Buyers aren’t saying no to AI - they’re saying no to products that look promising but can’t survive real-world deployment. If an AI product can’t clearly own a specific P&L outcome - reducing cost, increasing revenue, or removing operational overhead it doesn’t make it past procurement.

We see this pattern over and over again. And in this post, we will break down what actually separates AI agents that demo well from those that are truly ready to be sold and scaled inside the enterprise.

If you’re building or selling AI products into enterprises, understanding this shift is critical. Enterprise buyer is rarely an individual but rather involves a group of stakeholders like business owner, IT, legal, procurement and finance. The primary contact may be leaders like CMO, CIO, COO based on whether the organization needs to grow the business or optimize costs.They like to evaluate products as investments which lead to improved business outcomes.

This article is for founders, product leaders, and GTM teams selling AI into enterprise who want to understand what has changed and how to build and sell AI products that actually win.

Introducing Manoj Kokal:

Manoj is a Director of Product at Automation Anywhere, where he leads the Agentic AI product focused on ITSM automation. Dynamic product leader with over two decades of experience in product strategy and leadership. Over the past eight years, he has specialized in building enterprise AI products, significantly increasing revenue for each of the products he’s owned and taking them from $5M to $30M in ARR.

A few terms we’ll use throughout this post

Before we go deeper, here are a few concepts that come up repeatedly. If you already know them, feel free to skim.

ITSM (IT Service Management)
How large organizations manage internal IT issues like password resets, software access, device problems, and outages at scale. Think of ITSM as the workflows and systems behind “my laptop isn’t working” inside an enterprise.

Agentic AI
AI systems that don’t just provide answers, but can make decisions and take action across workflows often autonomously and within defined guardrails.

System of Record
The authoritative system where data lives and actions must ultimately be written back (e.g., ServiceNow for IT, Salesforce for sales, ERP systems for finance).

Guardrails
Rules, thresholds, and controls that define what an AI agent can and cannot do when to act autonomously, when to escalate, and how risk is managed.

AI Observability
The ability to see how an AI system made a decision: what data it used, which rules were applied, when humans intervened, and how outcomes tie back to business metrics.

The Agentic AI Maturity Model for Enterprises:

Most enterprise AI journeys follow the same progression, even if teams don’t explicitly name it.

Early on, AI shows up as a feature. Then it becomes an assistant. Only later after trust, integration, and governance are earned does it become something enterprises are willing to rely on autonomously.

We’ve found it helpful to think about enterprise AI adoption across four stages:

Stage 1: Experimental AI (Insight Only)

AI is used to retrieve information, summarize data, or answer questions. These experiences often demo well, but they still rely on humans to interpret results and take action. Value is indirect and difficult to tie to a P&L line item.

Stage 2: Assisted AI (Human-in-the-Loop)

AI begins recommending actions - classifying tickets, scoring leads, drafting responses but humans remain in control of execution. This is where many enterprise pilots live today. Early ROI appears, but scaling is slow because people remain the bottleneck.

Stage 3: Guarded Autonomy (Enterprise-Ready)

AI starts executing workflows end-to-end within clearly defined guardrails. It integrates directly with systems of record, escalates only when confidence drops, and produces observable, auditable outcomes. This is the inflection point where pilots turn into rollouts.

Stage 4: Scaled Autonomy (Enterprise Infrastructure)

AI operates as part of core business infrastructure. Autonomy scales across teams, systems, and regions. Outcomes are measured, risk is managed, and expansion happens naturally.

Note: The majority of enterprises are transitioning between Stage 2 (Assisted AI) and Stage 3 (Guarded Autonomy).

They’ve moved beyond simple chatbots, but true end-to-end autonomy is still constrained by trust, integration depth, and governance requirements.

This gap between AI that assists and AI that can be trusted to act is where most enterprise deals stall. The rest of this article, and the case studies that follow, focus on how teams successfully cross that gap.

Why Stage 2 (”Assisted AI”) stalls in enterprise deals

Assisted AI Creates Insight, Not Outcomes

In Stage 2 (Assisted AI), systems analyze data and recommend actions, but humans still execute the work. This works in demos but breaks down in production. Enterprises don’t buy AI for better suggestions, they buy it to remove work entirely. Until AI can own an outcome end-to-end, value remains indirect and difficult to tie to a P&L line item.

Humans Remain the Bottleneck

Stage 2 (Assisted AI) AI still depends on human availability, consistency, and judgment. As volume increases, efficiency gains flatten because people - not systems - limit throughput. This is why many enterprise pilots show early promise but stall during expansion: AI speeds up decisions, but doesn’t eliminate operational drag.

ROI Is Hard to Defend at Scale

In Assisted AI deployments, ROI is fragmented - time saved here, productivity gained there. That’s hard for finance and procurement teams to defend during budget reviews and renewals. Stage 3 (Guarded Autonomy) changes this by producing measurable outcomes like higher auto-resolution rates, fewer escalations, and lower cost per transaction.

Trust Gaps Prevent Autonomy

Enterprises hesitate to grant autonomy without visible controls. Stage 2 (Assisted AI) systems often lack clear guardrails, auditability, and explainability, raising concerns about hallucinations, non-deterministic behavior, and security at scale. Without trust mechanisms built into the product, enterprises won’t allow AI to act independently.

How enterprises evaluate AI when moving to Stage 3 (Guarded Autonomy)

Understanding the evaluation criteria helps vendors position their solutions effectively:

Agentic Capability Is Now Table Stakes

Buyers look beyond chatbots and ask: Can the AI create workflows? Can it make decisions? Can it take actions in systems of record? Agentic capability - not just knowledge retrieval - is now a core evaluation criterion.

Increasingly sophisticated buyers also assess support for standard protocols like MCP (Model Context Protocol) or agent-to-agent communication, recognizing that flexibility to orchestrate agents across vendors may matter long-term.

Domain Expertise Dramatically Shortens Time to Value

One of the strongest differentiators today is domain specificity. Generic AI struggles in complex enterprise environments because it lacks the business context needed to operate effectively.

For example, an ITSM buyer will favor an AI product that understands incidents versus requests, knows IT workflows and escalation rules, and integrates with ServiceNow out of the box. Domain-specific models trained on authoritative business data dramatically reduce deployment time and improve accuracy.

Evaluation questions buyers ask: What domains are natively supported? How does this differ from generic AI? What business ontology is built into the model?

Integration Depth Matters More Than Breadth

AI must integrate easily with knowledge systems, transactional systems like ticketing or CRM platforms, and identity and access platforms. Buyers look for pre-built connectors, robust APIs, proven deployments at similar enterprises, and clearly defined SLAs.

Surface-level integrations that simply read data aren’t enough. Buyers need AI that can write back to systems, trigger workflows, and operate within existing approval chains.

Trust Mechanisms Must Be Visible

Enterprise buyers scrutinize how hallucinations are controlled, whether AI is grounded in business data, if responses are filtered by role and permissions, and whether full audit trails are available.

They also expect clarity on data usage policies, model training boundaries, and compliance certifications. AI observability is no longer optional - it’s how enterprises manage risk while scaling autonomy.

Case Study | ITSM from Stage 2 (Assisted AI) to Stage 3 (Guarded Autonomy)

This IT Service Management case study illustrates exactly how enterprises evaluate and approve AI systems when moving to Stage 3 (Guarded Autonomy).

Executive Summary

A large enterprise organization improved its IT Service Management efficiency by implementing a domain-specific agentic AI solution, achieving a 12% increase in auto-resolution rates and a 5-point improvement in employee satisfaction within six months.

Why the Stage 2 Approach Failed

As IT request volumes increased, the organization initially deployed a horizontal agentic AI platform and invested heavily in building custom automations. Despite early promise, the system stalled due to poor request-to-solution matching and high maintenance overhead. Each new use case required manual configuration, limiting scalability and ROI.

This mirrors a common Stage 2 (Assisted AI) failure mode: AI recommendations existed, but humans remained responsible for execution, tuning, and exception handling.

What Changed at Stage 3 (Guarded Autonomy)

The organization replaced the generic platform with a domain-specific ITSM agentic AI designed to meet Stage 3 (Guarded Autonomy) evaluation criteria:

Domain Intelligence

The AI models were trained on IT-specific terminology and concepts, enabling accurate interpretation of employee requests. The system’s ontology could be customized with organization-specific terms. For example, while “VPN” is standard IT terminology, the system learned that “Cisco AnyConnect” was the company’s specific VPN solution, allowing it to correctly route requests like “install AnyConnect” to the VPN provisioning workflow.

Integration Depth

The AI operated directly within ITSM systems of record, executing identity verification, account unlocks, credential resets, directory updates, and ticket logging without human intervention.

Pre-Built Workflow Library

The platform included ready-to-deploy templates for common IT requests such as:

• Password resets

• Account unlocking

• Laptop replacements

• Software installations

These templates could be quickly configured and integrated into existing AI agents.

Natural Language Workflow Creation

For unique organizational needs, IT staff could create custom workflows using natural language instructions, eliminating the need for extensive technical configuration.

Note: This interaction shows an AI agent resolving a high-frequency IT access issue end-to-end identity verification, account unlock, credential reset, directory updates, and ticket logging without human intervention.

Results

This is what it looks like when domain + workflows + guardrails make autonomy possible.

Within six months of switching from a horizontal agentic AI platform to a domain-specific ITSM solution, the enterprise achieved clear performance improvements:

• Auto-Resolution Rate: Increased by 12 percentage points

• Employee Satisfaction (ESAT): Improved by 5 points

The prior vendor plateaued at ~75% auto-resolution, leaving a quarter of requests to be handled manually. By contrast, the Stage 3 (Guarded Autonomy) solution pushed resolution toward ~87%, unlocking compounding benefits: lower operational cost, faster resolution, higher employee satisfaction, and critically higher client retention as IT teams could reliably scale without adding headcount.

Why This Case Crossed the Gate to Stage 3 (Guarded Autonomy)

In the Agentic AI Maturity Model, the transition from Stage 2 (Assisted AI) to Stage 3 (Guarded Autonomy) is not a technical upgrade, it’s a trust decision.

At Stage 2 (Assisted AI), AI is allowed to recommend. At Stage 3 (Guarded Autonomy), AI is allowed to act.

That permission only comes when enterprises can see, control, and audit how decisions are made.

The difference wasn’t just better automation. It was visible trust.

Make Trust Visible Throughout the Sales Process

Early adopters of AI also uncovered some trust issues. Enterprises learned that the solutions have to stand up against misuse (ex: toxic conversion) and abuse (ex: prompt injection) and be able to scale to business needs and volume so they do not loose business or impact their brand reputation.

Stage 3 (Guarded Autonomy) buyers expect vendors to show, not claim:

• Explain how responses are grounded in verified data.

• Incorporate levers and configurations in the solution that will allow customers to tune results based on their needs.

• How autonomy is constrained, escalated, or overridden

This is why AI observability becomes mandatory at Stage 3 (Guarded Autonomy). Buyers want to see why a decision was made, which data was used, what rules applied, and when humans intervened.

Explainability is the mechanism that converts probabilistic systems into enterprise software.

Don’t just tell buyers your AI is trustworthy, prove it by making the mechanisms visible.

In a financial operations use case, an enterprise used AI to assist with invoice validation and payment approvals. Early pilots revealed a critical concern while the AI identified anomalies effectively, finance leaders couldn’t explain why certain invoices were flagged or approved.

This lack of explainability stalled rollout. In regulated environments, “the model decided” is not an acceptable answer.

To move forward, the AI vendor had to expose trust mechanisms directly in the product. Each decision included an explanation trace showing which data points were used, what thresholds were applied, and whether any rules overrode the model’s recommendation. Finance teams could adjust sensitivity levels, enforce deterministic behavior for high-value transactions, and audit every action taken by the AI.

Note: Show’s the audit log of invoices, the rule on why it got rejected and the recommendation.

Key Takeaways

This case study isn’t just about better IT automation. It demonstrates how enterprises evaluate AI when granting autonomy. Domain expertise, packaged workflows, deep integrations, and visible trust mechanisms are not differentiators; they are requirements for crossing from pilot to platform.

If you’ve gotten this far, you may be ready to navigate to the 🔥 section and check out our Playbook now.

Case Study | Automotive from Stage 3 (Guarded Autonomy) to Stage 4 (Scaled Autonomy)

Once trust is established at Stage 3 (Guarded Autonomy), something important happens: autonomy stops being risky and starts becoming repeatable.

This is where companies move from Guarded Autonomy (Stage 3) to Scaled Autonomy (Stage 4).

Most AI-SDR products fail in enterprise sales for the same reason they stop at intelligence instead of owning the outcome.

Early versions of AI sales assistants could summarize leads, draft emails, or recommend follow-ups but they still required significant manual setup and human orchestration. That friction is manageable in SMB environments. In enterprise settings, it kills adoption.

This is where Manoj made a critical shift.

Instead of selling an “AI-powered SDR,” he redesigned the product to behave like an autonomous sales operator natively integrated into dealership CRMs, pre-configured for common automotive workflows, and capable of running end-to-end engagement without manual intervention.

This case study shows what happens after Stage 3 (Guarded Autonomy) works.

Executive Summary

A Midwest enterprise automotive dealership group achieved 5× pipeline growth and 10× ROI by deploying an AI digital assistant that operated as an autonomous sales operator - not a copilot.

After proving safety, reliability, and ROI in one showroom (Stage 3 Guarded Autonomy), the solution expanded across all four locations (Stage 4 Scaled Autonomy), becoming part of the dealership’s core revenue infrastructure.

Industry Context

Automotive sales representatives face the dual challenge of capturing and qualifying high-value leads while delivering exceptional customer service during major purchasing decisions. As dealerships grow, scaling human resources to maintain service quality becomes increasingly expensive and time-intensive.

Why the Stage 2 (Assisted AI) Approach Failed

Auto dealerships operate on thin margins and need to move inventory quickly to maximize revenue. While AI digital assistants promised to help sales teams engage leads through automated email conversations, early implementations faced significant adoption barriers:

• Data integration friction: Lead information had to be manually extracted from various systems and uploaded to the AI platform

• Time-intensive setup: Sales representatives needed to write initial email content before the AI could begin engaging prospects

• Low adoption: The complexity of these workflows made the tool burdensome rather than helpful, limiting actual business impact

Without effective AI assistance, dealerships defaulted to hiring additional sales representatives. While this approach provided some relief, it introduced new challenges:

• High recruitment and training costs

• Extended onboarding timelines

• Ongoing overhead from increased headcount

The Stage 3 (Guarded Autonomy) Redesign

The AI platform vendor redesigned the solution to eliminate friction points and accelerate time-to-value.

Here are the key components:

Seamless CRM Integration

Native integrations with automotive CRM systems (including Salesforce, VIN Solutions, and Tekion) enabled automatic synchronization of lead data. This eliminated manual data entry and positioned the AI assistant within the dealership’s existing technology ecosystem.

Industry-Specific Conversation Templates

Pre-built email conversation flows addressed the most common automotive sales scenarios:

• Website visit follow-ups

• Test drive scheduling

• Post-event engagement

• Inventory inquiries

Each template could be customized using simple natural language instructions, allowing sales teams to personalize communications without starting from scratch.

Automated Campaign Management

By connecting directly to the system of record, the platform enabled sales representatives to schedule recurring campaigns automatically, ensuring consistent lead engagement without manual intervention.

Notes: Screenshot to show how having your product integrate to the system of record is key to adoption and growth

Results

The impact was immediate and substantial. After the dealer implemented these enhancements at one showroom, the Midwest dealership group was so impressed that they rapidly deployed AI digital assistants across their remaining three locations.

Once autonomy proved safe and effective in one location, expansion was low-risk. The same system could be replicated across showrooms with minimal incremental cost.

This is the defining characteristic of Stage 4 (Scaled Autonomy): autonomy becomes an expansion engine.

Business Impact

Prior to the redesign, the AI-SDR product was capped as a low-scale SMB solution selling one-off licenses with limited expansion potential.

After adapting the product for automotive dealership groups, the company shifted to multi-location contracts and repeatable rollouts. This transformation enabled the business to grow from approximately $10 million in ARR to over $20 million in ARR, with automotive dealerships representing the majority of new revenue growth.

Key Insights

The real inflection point wasn’t better automation, it was proving that autonomy could be trusted and repeated. By integrating natively into dealership CRMs and packaging industry-specific workflows, the AI moved from assisting reps to owning outcomes end-to-end. Once that autonomy worked safely in one showroom, expansion across additional locations became a low-risk decision. This shift from insight to ownership unlocked a new growth model.

What Winners Do Differently

Lead with Packaged, Domain-Specific Solutions

The move from Stage 2 (Assisted AI) to Stage 3 (Guarded Autonomy) requires a fundamental change in how AI is packaged and sold. Stage 3 buyers aren’t looking for flexible platforms that can eventually be configured to own outcomes. They want solutions that are already opinionated, domain-aware, and ready to operate autonomously.

Winning vendors no longer sell generic “AI platforms” alone. Instead, they lead with domain-specific solutions (IT, HR, Facilities, Sales, Finance) that bundle domain ontology, pre-trained models, and production-ready workflows. These domain packs also include pre-built integrations into systems of record for that function. For example, an IT domain agentic AI understands concepts like incidents and service catalogs, ships with workflows for password resets and software requests, and connects natively to systems like ServiceNow or FreshService out of the box.

This kind of packaging is what allows enterprises to move from pilots to rollouts. It collapses time-to-value from months to weeks and reduces the operational risk of granting autonomy. At Stage 3, the biggest competitive advantage isn’t model sophistication - it’s how quickly and safely autonomy can be deployed.

Demonstrate Action, Not Just Intelligence

As AI capabilities have advanced, enterprise expectations have risen in parallel. Stage 2 (Assisted AI) systems retrieve knowledge and make recommendations. Stage 3 (Guarded Autonomy) systems take action.

In practice, this means AI demos must go beyond insight and show end-to-end task ownership inside real systems. A knowledge-based AI might explain why pipeline dropped last week by summarizing CRM data but a human still has to interpret the insight, decide what to do, and execute changes across multiple tools.

An agentic AI operating at Stage 3 (Guarded Autonomy) goes further. It detects a drop in inbound lead response time, correlates it with staffing gaps and routing rules, updates assignment logic directly in the CRM, triggers alerts to sales managers, and launches a corrective workflow — all within clearly defined guardrails.

Pilot or Trial offers

Because Stage 3 (Guarded Autonomy) introduces autonomy, pilots must be designed differently. These are not open-ended experiments to discover use cases. Stage 3 (Guarded Autonomy) pilots are scoped to prove trust.

In crowded AI markets where many vendors claim similar capabilities, successful companies reduce buyer risk through tightly bounded proof points: time-bound POCs, usage-limited trials, or department-specific deployments with clear success criteria. Examples include a 90-day POC running alongside an existing system or a capped-volume deployment limited to a single business unit.

The goal of these pilots is not learning - it’s validation. Enterprises use them to confirm that the AI behaves predictably, integrates cleanly, and delivers measurable outcomes before granting broader autonomy. Vendors that understand this distinction move through Stage 3 (Guarded Autonomy) faster; those that don’t remain stuck in perpetual pilots.

The Metrics That Matter

In the Agentic AI Maturity Model, metrics are not just performance indicators, they are control mechanisms. Enterprises use them to decide whether to grant more autonomy (move from Stage 2 AI Assist to Stage 3 Guarded Autonomy), expand deployment (move from Stage 3 Guarded Autonomy to Stage 4 Scaled Autonomy), or roll autonomy back entirely.

As AI products mature and enter real enterprise environments, leaders shift from asking “Does this work?” to “Does this create measurable value at scale, safely?” That question can only be answered through outcome-driven metrics tied directly to business impact.

Note: As AI systems mature, metrics evolve from feedback signals to control systems. In Stage 2 (Assisted AI), metrics help teams judge whether AI responses are useful. In Stage 3, those same metrics become gating mechanisms determining when autonomy is allowed, constrained, or rolled back. By Stage 4, metrics function as governance infrastructure, enabling enterprises to safely scale autonomy across teams, workflows, and business units.

Enterprises track a combination of effectiveness, containment, and experience metrics to understand how well an AI agent is performing and whether it can be trusted with greater autonomy:

As AI products mature and get deployed in enterprises it becomes increasingly important to understand if this product is creating measurable value at scale. Customers want to measure task effectiveness and outcomes to understand how AI is able to help their business

Some key product metrics that are used to optimize Agentic solution

• Auto resolution rate - AI agent was able to successfully respond to Employee or customer

• First Contact resolution (FCR) - User got response from Agentic AI without escalating to human agent and did not contact business again in the next 72 hours for the same request

• Response Accuracy rate - How well the response answers the user question. This may be measured via feedback (thumbs up/down) from end user

• Assisted Rate - AI agent was able to partially answer user query even though they may be escalated to human agent

• CSAT / ESAT - Customer or Employee satisfaction measured post facto to their contact experience usually via survey

Enterprises don’t look at these metrics in isolation - they use them to derive financial and operational impact. For example, a customer service organization may focus on increasing auto-resolution rate. If the AI resolves X additional requests per month and the average cost per human-handled contact is $Y, the business can directly quantify $(X × Y) in cost savings.

These metrics also influence expansion decisions. Sustained improvements signal that the AI can be deployed across more teams, higher volumes, or additional geographies - the defining move from Stage 3 (Guarded Autonomy) to Stage 4 (Scaled Autonomy).

The Bottom Line

Enterprise buyers are no longer buying AI because it’s impressive. They’re buying AI because it solves real problems, acts autonomously and safely, delivers measurable ROI, integrates cleanly into their existing ecosystem, and can be trusted at scale.

The vendors who win are the ones that combine agentic capability, deep domain expertise, strong packaging, and enterprise-grade trust and then prove it with real metrics in production. If your AI product can’t demonstrate these elements clearly and quickly, enterprise deals stall, pilots linger in Stage 2 (Assisted AI), and momentum fades.

The real challenge, the “so what?” - is this: getting to ~$10M ARR with AI isn’t that hard anymore. A strong demo, early adopters, and a compelling use case can get you there.

Getting past that is where most AI companies struggle. Just like in B2B SaaS, the next phase of growth is won by teams that know how to compete at the enterprise level crossing the gap from assisted AI to guarded autonomy across trust, integration, governance, and ownership of outcomes.

Enterprise buyers want AI to succeed.

They’re actively looking for vendors who understand their reality and can deliver value safely, repeatedly, and at scale. The companies that meet them there don’t just close deals they unlock expansion, standardization, and durable enterprise businesses.

🔥 INSIDER GROWTH PLAYBOOK: From AI Pilots to Scaled Enterprise Revenue

Everything we covered above points to the same reality: most AI products don’t fail because the technology is weak. They fail because teams don’t know how to cross the gap from interesting pilot to enterprise-scale deployment.

So we built this playbook to answer one question we get over and over again:

“What do we actually need to change to make our AI enterprise-ready?”

This playbook is a practical guide for founders, product leaders, and GTM teams who want to:

• Diagnose where their AI product sits in the Agentic AI Maturity Model

• Understand why pilots stall between Assisted AI and Guarded Autonomy

• Make the minimum upgrades required to unlock enterprise scale

This playbook is a practical guide for founders, product leaders, and GTM teams who want to turn AI pilots into scalable enterprise revenue.

Identify What Stage of Maturity You’re In

If you misdiagnose your stage, everything downstream breaks. Enterprise scale stalls when teams try to skip steps.

Below is how to correctly identify where you are today.

Stage 1: Experimental AI (Pilot-Only)

What This Looks Like

• AI features launched as copilots, chatbots, or assistants

• Primarily focused on knowledge retrieval or summarization

• Demos perform well; production usage is limited

• Value depends heavily on humans interpreting and acting on outputs

Why Enterprises Get Stuck Here

• AI creates insight, but doesn’t own outcomes

• High cognitive load on users

• No clear ROI story beyond “productivity”

Enterprise Buyer Signal

“Interesting, but we’d need to build a lot around this.”

Minimum Requirements to Move Forward

• Identify one business workflow the AI will fully own (not assist)

• Define a clear success metric (e.g., auto-resolution rate, lead response time)

• Limit scope intentionally breadth kills pilots

If your AI stops at recommendations, you are still in Stage 1.

Stage 2: Assisted AI (Human-in-the-Loop)

What This Looks Like

• AI analyzes data and recommends actions

• Humans approve, edit, or trigger execution

• Early pilots begin inside real teams

• Some ROI appears, but scaling is slow

Common Failure Model

• Human bottlenecks prevent scale

• Inconsistent usage across teams

• Enterprises struggle to justify broader rollout

Enterprise Buyer Signal

“It works, but it still depends too much on our people.”

Minimum Requirements to Move Forward

• Introduce decision boundaries (what AI can do autonomously vs. not)

• Reduce manual setup with pre-built workflows

• Show repeatability across teams or locations

If humans are required for every meaningful action, autonomy is capped.

Stage 3: Guarded Autonomy (Enterprise-Ready)

What This Looks Like

• AI executes end-to-end workflows autonomously

• Clear guardrails define where AI can act

• Integrated directly into systems of record

• Human escalation is the exception, not the default

Why This Is the Enterprise Inflection Point

This is where:

• Pilots turn into rollouts

• Single-team usage turns into multi-department adoption

• SMB motions become enterprise motions

Enterprise Buyer Signal

“We can deploy this safely across the organization.”

Non-Negotiable Capabilities

• Agentic execution (AI completes tasks, not just suggests)

• Domain-specific packaging (IT, Sales, Finance, etc.)

• Configurable trust controls (confidence thresholds, escalation rules)

• Observable decision-making (logs, traces, explanations)

If trust mechanisms aren’t visible, autonomy won’t be granted.

Stage 4: Scaled Autonomy (Enterprise Expansion Engine)

What This Looks Like

• AI operates as part of core business infrastructure

• Autonomy scales safely across teams, regions, and volumes

• ROI is measurable and visible

• Expansion happens organically

What Winning Companies Do Differently

• Sell outcomes, not features

• Package AI by domain and use case

• Design for expansion from Day 1

• Treat trust, observability, and governance as product features

Enterprise Buyer Signal

“This is now part of how we run the business.”

The 30–60–90 Day Enterprise AI Upgrade Plan

Days 0–30: Diagnose and Focus

• Identify your current maturity stage

• Pick one workflow to fully own

• Define one metric that matters to the buyer

Days 31–60: Package for Enterprise

• Build domain-specific workflows or templates

• Integrate natively into systems of record

• Introduce guardrails and confidence thresholds

Days 61–90: Prove Trust and Scale

• Ship AI observability (logs, traces, explanations)

• Run a scoped enterprise pilot with expansion intent

• Tie usage directly to ROI metrics

Gotchas: Why Enterprise AI Pilots Still Fail

1. Selling intelligence instead of ownership

2. Generic platforms forced into domain-specific problems

3. Trust mechanisms hidden behind marketing claims

4. No clear expansion path after the pilot

5. ROI discussed verbally, not measured in-product

If any of these are true, scale will stall.

The Enterprise AI Litmus Test

Before selling to enterprise, ask:

• Can our AI complete a task end-to-end?

• Can buyers see why decisions were made?

• Can risk be configured, not just avoided?

• Can success be measured without manual analysis?

• Is expansion designed or accidental?

If the answer is “no” to more than one, you’re not enterprise-ready yet.

If you’re building AI for enterprise and pilots aren’t expanding the way you expected, we’ve seen this pattern many times before. Happy to share what’s worked and what hasn’t.

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Most loyalty programs are lies. They are disguised discount schemes designed to get the average user to buy one more item they didn’t need.

But for leaders in high trust, high ticket industries, a points system isn’t just ineffective; it’s a distraction from your unit economic reality.

In these sectors, you aren’t playing a volume game. You are playing a “whale” game, where a small percentage of power users drive the vast majority of your revenue and often dictate your product roadmap.

Unlike the average consumer, these whales don’t trade engagement for badges. They trade capital for certainty. And that certainty looks different and expensive across every vertical:

• In B2B SaaS: You aren’t optimizing for DAU, you are optimizing for annual contract value. A single Enterprise client creates more revenue than 1,000 SMBs. But they demand SOC2 compliance, 99.99% SLAs, and dedicated account managers.

• In Fintech: Your revenue is concentrated in high-volume traders or high-net-worth individuals. They don’t care about streaks, they care that their assets are liquid and secure. That requires expensive KYC (Know Your Customer), fraud detection, and insurance.

• In Real Estate & Luxury: The buyer isn’t looking for a “fun” transaction, they are terrified of a counterfeit one. The cost here is physical authentication, white-glove logistics, and escrow services.

Providing this certainty costs money. It requires high-touch operations just to get a seat at the table.

This is the Trust Tax.

And this is all fine as long as your product’s whales have the high LTV you expect them to.

But here’s the trap: If you pay this tax but still suffer high churn, your business model breaks. You are burning cash on high-cost operations without the lifetime value (LTV) to back it up.

The highly competitive resale market of sneakers and watches

To understand the leak, you have to understand the player. The “whales” in this market aren’t just kids looking for a quick flip. They are sophisticated arbitrageurs and mega-collectors building portfolios that rival the value of a physical home. They treat sneakers as a serious asset class.

But for years, eBay forced these high-value users to operate in a “Wild West” environment. The platform offered massive inventory but zero trust-building tools. Every transaction was a gamble: Is the seller legit? Is the stitching real? Will I get a brick in the box?

This trust deficit was the existential threat. To fight the flood of counterfeits and win back credibility in their luxury verticals (sneakers, watches), eBay launched Authentication Guarantee.

The investment was massive: they built physical centers to verify every high-value item. And while it worked for securing general trust, such as bringing casual buyers back to the platform, the unit economics for their most important users were breaking.

The leak wasn’t the casual buyer. It was the whales, the top 1% of users spending ~$10k/year. Despite the new safety features, this cohort was churning at 45% YoY.

The mistake wasn’t assuming that whales wanted safety—they absolutely did. No one wants to spend $2,000 on fake Jordans. The mistake was assuming safety was enough.

For a casual buyer, safety is the product. But for a whale, safety is just a baseline requirement. The real product is the deal.

Beyond just the table stakes of authentication and safety, eBay was just another platform for them to price compare and hunt for their next pair in their collection in a very hot sneaker resale market. These users were driving the platform’s liquidity, yet they were treated exactly like a first-time guest. They needed a signal that their volume mattered. They craved an exclusive experience that rewarded their loyalty with access that the general public didn’t have.

The Insider: Ankit Agarwal

We collaborated with Ankit Agarwal, a Product Leader previously at eBay that led the strategy and growth for sneakers and watches. In this deep dive, we’ll showcase how this challenge was promptly addressed by creating the eBay Top Star program to generate the exclusivity the users craved.

The Case Study: Flipping the Negative Mindset

To understand the solution, you have to understand the psychology of the user. Ankit realized that retention isn’t about features. It is about emotional state management.

The Problem: The Consolation Prize Trap

eBay had an inventory problem, but more importantly, they had an emotional problem. Buyers arrived at eBay in a negative mindset.

In the sneaker world, inventory isn’t stocked. It is “dropped.”

A “drop” is a scheduled release of a limited-quantity product where demand outstrips supply by 100x. Because you can’t just buy the item, the primary market (apps like Nike SNKRS or Adidas Confirmed) operates on a lottery system. You enter a draw for the chance to pay $200.

99% of the time, you lose.

• The Loss: Users take a loss on the primary drop.

• The Resignation: Frustrated, they come to eBay as a last resort.

• The Cost: They are forced to pay 2x-3x retail price on the secondary market.

• The Feeling: They felt the platform was purely predatory. They believed eBay didn’t share their passion and just wanted their fees.

A frustrated customer combined with a high-friction experience creates the high churn.

The Solution: eBay Top Star

Ankit and his team realized they couldn’t just offer points. Points are for casual users. Experts want access.

They launched eBay Top Star, an invite-only program designed to change the user’s emotional state from exploited to insider.

1. Flipping the Economics (Loss → Win)

They identified the user’s moment of highest frustration, losing a drop on a primary app, and turned it into a moment of delight.

The Tactic: eBay secured inventory of highly coveted drops (Jordans, Yeezys) and sold them to Top Star members at MSRP (Retail Price) instead of the resale price.

The Math: eBay took a margin hit on these specific items because the CAC of re-acquiring a churned whale is higher than the subsidy on a pair of sneakers. They turned the last resort into the first victory and instantly gave the user hundreds of dollars in equity.

2. The Concierge Hunt

They provided direct access to human agents who acted less like support and more like brokers.

The Tactic: If a Whale wanted a specific pair of 1985 Chicago Jordans that wasn’t listed, the agent wouldn’t say “Sorry, out of stock.” They would go hunt for it by leveraging eBay’s massive data to find a seller or looking off-platform to facilitate the deal.

The Effect: This proved to the user that eBay wasn’t a utility. It was a partner in their collection strategy.

3. Experiential Exclusivity

They moved beyond digital transactions to physical validation.

Gifting: Instead of generic swag, they sent hyper-personalized accessories. Each member got a personalized welcome kit with an exclusively created collectors item, special shoe care accessories as a surprise in your regular order.

Events: They demonstrated that the sneaker passion was truly shared! They gave away tickets to SneakerCons all over the country. They hosted “Top Star” parties with performers like Zak Bia at industry events like ComplexCon. Only members and industry pros were invited. This created a visible status hierarchy where being in the room meant you were in the inner circle.

4. Strategic Ambiguity

Perhaps the smartest psychological lever was keeping the criteria opaque.

The Halo Effect: There was no progress bar saying “Spend $500 more to reach Gold.” This created buzz in the sneaker community. Users actually started transacting more just to prove their worthiness to the algorithm. Uncertainty drove engagement.

The Impact

The results of treating the top 1% like actual VIPs were immediate.

• Retention: Retention for the cohort hit 90% (cutting churn in half).

• Financials: The program delivered a 1.3x ROI.

• Behavior: It shifted users from “one-off” buyers to a “renew, renew” mindset.

The Lesson

Address a key pain for your users completely. Identify the exact moment your customer feels defeated by the market and design your offering to solve that specific pain.

If you’ve gotten this far, you may be ready to navigate to the 🔥 section and check out our Playbook now.

The AI Pivot: Building the White Glove Machine

Ankit’s program worked, but it was operationally heavy. It required headcount, manual sourcing, and high-touch management.

The Good News: Today, you can replicate the Top Star program using a stack of AI Agents without the bloat. You can scale white glove service to 10,000 users without hiring 100 account managers.

1. Universal Application: How to “Top Star” Your Industry

The eBay Top Star example isn’t just for sneakers. It is a universal loyalty loop for any high-ticket business. Here is how you can apply this concierge strategy to other verticals:

B2B SaaS

In enterprise software, your VIP clients don’t want swag; they want their feature requests prioritized. Instead of a generic “we’ll look into it,” imagine a system that automatically scans your internal product roadmap, identifies the exact Product Manager who owns that feature, and drafts a pitch to fast-track it. You aren’t just supporting the client; you are lobbying for them.

Fintech

In banking, the most valuable moment is a “liquidity event.” Imagine a system that instantly detects when a user hits a high cash balance and proactively reaches out—not with a generic newsletter, but with a direct invite to exclusive high-yield funds usually reserved for institutional investors. You turn a deposit into a status symbol.

Real Estate (The Off-Market Hunt)

In luxury real estate, the best deals never hit Zillow. Imagine a system that monitors private dealer networks and off-market forums 24/7. When a VIP buyer says, “I want a Mid-century modern in Silver Lake,” the system notifies them the second a rumor hits the network, giving them the first-mover advantage before the public ever sees the listing.

2. The Modern Blueprint: Rebuilding Top Star Today

If we were to rebuild eBay TopStar today, we wouldn’t hire an army of concierge agents. We would deploy a coordinated AI system to deliver the same high-touch experience at scale.

Here is the detailed end-to-end flow of that modern architecture, using a luxury watch buyer (The “Whale”) as our case study. Note how the system feels human and bespoke, despite being fully automated.

1. The Trigger: The User texts the dedicated VIP number: “I’m looking for a Rolex Submariner, birth year 1992.”

2. The Acknowledgment: The AI replies instantly, adopting the persona of a knowledgeable broker: “Great choice. 1992 is a tough year for Subs, but let me put the team on it. Give me 24 hours.”

3. The Hunt: In the background, the system activates. It scrapes 4 major marketplaces and 2 private dealer networks. It filters out damaged inventory and finds two perfect matches—one in Tokyo, one in NYC.

4. The Pitch: The AI texts the user with a curated selection: “Found two. One in Tokyo (Mint, Box & Papers) for $14k. One in NYC (Watch only) for $11k. The Tokyo one looks cleaner. Want photos?”

5. The Close: User says “Tokyo.” The AI generates a secure payment link and handles the checkout.

6. The Assurance: Three days later, the system detects a customs hold via the shipping API. It proactively texts the user to manage anxiety: “Quick update—watch is clearing customs in LA. Might be 1 day late, but it’s moving.”

The Result: The user feels like they have a dedicated personal shopper, but in the background this is all being coordinated by AI agents.

3. The Stack & The 4-Agent Architecture

To build this, we don’t use a simple chatbot. We use a Multi-Agent System (MAS) where agents have distinct roles and hand off tasks like a relay team.

The Tech Stack:

• Orchestration (The Brain): Relay.app (No-code) or LangGraph (Code). This acts as the traffic controller.

• Intelligence: OpenAI GPT-4o (for nuance and tone).

• Execution: Custom Python Scripts (for scraping and API calls).

The 4-Agent Architecture

1. Agent 1: The Gatekeeper (The Qualifier)

• The Job: Most programs fail because they only look at total spend. This agent scores users on Passion Metrics—search velocity, specific niche keywords, and dwell time.

• The Output: It acts as the bouncer. It tags the user in your CRM as Status: Top_Star_Eligible and triggers the Hype Man to send the invite.

2. Agent 2: The Hunter (Buyer Agent)

• The Job: The Concierge inventory hunt. Once it receives an order, it scans global listings across the web (StockX, GOAT, eBay, Private Forums) to find the item.

• The Output: It finds 3 options, formats the pricing and condition, and passes the data to the Hype Man.

3. Agent 3: The Hype Man (The Relationship Manager)

• The Job: This is the only agent that speaks to the customer. It handles all inbound requests and outbound hooks.

• Intent Detection: When a user texts “I need 85 Chicagos,” it uses NLP to extract the Intent (Buy), Item (Jordan 1 Chicago), and Condition (1985 Vintage).

• The Output: It polishes raw data from the Hunter into a conversational, high-status text message.

4. Agent 4: The Safety Net (Support)

• The Job: Proactively monitors shipping and authentication APIs. It wakes up only when a transaction is in flight.

• The Output: If it detects a delay at the authentication center, it drafts a message for the Hype Man to reset expectations before the user gets anxious.

The Human Moat (Where AI Fails)

While agents can handle the logistics, Ankit notes there is a limit. You cannot automate the Trust Tax entirely. There are three areas where you must keep humans in the loop:

1. Authentication (Sensory Verification): AI cannot verify the smell of vintage leather or the material feel of a $50k item. You still need human experts for the final stamp of approval.

2. The Gray Area Exception: AI follows rules strictly. Humans break them to save relationships. Sometimes you need a human to authorize a refund outside of policy to save a client with $100k LTV.

3. Physical Community: An agent can’t buy you a drink at Sneaker-con. The physical handshake remains the ultimate retention tool.

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🔥The IGG Playbook: How to Build the White Glove Machine

Most founders think they need more features to retain high-end customers. You don’t. You need a different operating model.

If you are running a high-ticket or high-trust business, here is your step-by-step roadmap to reducing churn with a white glove machine.

Step 1: Audit Your Trust Tax (The P&L Check)

You need to mathematically define if you have a retention problem.

• The Calculation: Calculate your Cost to Serve (CTS) for your top 1% of customers. Include “hidden” costs: manual onboarding hours, insurance premiums, verification fees, and dedicated support slack channels.

• The Trap: If your CTS is high, you cannot afford “standard” churn at 5-10%. You are paying a premium to acquire and serve these users.

• The Rule: If your Trust Tax exceeds 20% of the contract value, you must treat Retention as a product, not just a metric. You need to build your own Top Star program to defend that margin.

Step 2: Identify the “Whale” (The Matrix)

Most companies get this wrong. They define a VIP solely by Current Spend. This is a mistake.

• High Spend + Low Passion = “The Rich Tourist.” They have money but no loyalty. They will leave you for a competitor who is 5% cheaper. Do not over-invest here.

• High Spend + High Passion = “The Whale.” They spend and they engage (search history, support tickets, community presence). These are the only users who belong in your Inner Circle.

Remember that passion can be defined by metrics like search velocity, specific niche keywords, and dwell time.

Use this matrix to segment your user base today:

Step 3: Solve the “Impossible” Pain

Your value prop cannot just be better service. It must be an unlock—solving a problem the customer has accepted as impossible to solve in the market.

B2C Example (eBay):

• The Impossible Pain: “I will never win a Nike SNKRS lottery.”

• The Unlock: “We secured the inventory. You win here.”

B2B Example (Enterprise SaaS):

• The Impossible Pain: “Migrating my data from Salesforce will take 6 months and break my reporting.”

• The Unlock: “Our AI Migration Agent + Human Architect team will do it for you in 48 hours, guaranteed.”

Action: Find the one friction point your industry considers standard operating procedure and eliminate it for your Inner Circle.

Step 4: The Augmented Concierge Model

You cannot scale white-glove service with humans alone. You must use the 80/20 Augmented Concierge Model.

• 80% AI (The Legwork): Use the Agent Stack (Gatekeeper, Hunter, Hype Man) to handle the “drudgery”—sourcing inventory, qualifying leads, and monitoring shipping.

• 20% Human (The Glory): The human never does data entry. The human only enters the loop for the “Glory Moment”—the final confirmation call, the exception approval, or the physical handshake.

The Golden Rule: The AI prepares the gift. The Human hands it over.

Did this post make you think about how you can replicate exclusivity or incentives to your own product or company? It’s even faster now using AI. If so, let’s talk!

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Our mission is simple: We help you create a roadmap that boosts your key metrics, whether you're launching a product from scratch or scaling an existing one.

What We Stand For

• Actionable Insights: Our content is a no-fluff, practical blueprint you can implement today, featuring real-world examples of what works—and what doesn’t.

• Vetted Expertise: We rely on insights from seasoned professionals who truly understand what it takes to scale a business.

• Community Learning: Join our network of builders, sharers, and doers to exchange experiences, compare growth tactics, and level up together.

Introduction:

Everyone’s racing to add AI to their product. Few are using it to grow their product.

Over the past six months, we’ve studied dozens of AI launches from PLG teams at Notion, Monday.com, Retool, Figma, Linear, and Zapier.

What we’ve seen is AI is rewriting the rules of Product-Led Growth; not by creating new features, but by collapsing the time it takes to feel value.

What used to take hours – setup, configuration, and recreating a workflow – now happens in seconds.

• You tell Monday.com what you’re working on, and it builds your first project.

• You open Retool, and an app appears based on your role.

• You sign into Jira, and your first sprint is populated with sample tasks.

The shifts we are seeing are:

1. Before, personalization improved conversion rates. Now it defines trust. Users judge whether a product gets them.

2. Before, onboarding was about guidance. Now it’s about momentum. Users expect the product to start working for them instantly.

3. Before, value was implied. Now users expect it to be proven within the product immediately.

AI has turned PLG from self-serve education into self-serve execution. However, some teams are missing the mark. They’re layering AI into PLG without grounding it in the fundamentals that made growth work in the first place: personalization, frictionless UX, and context that connects the acquisition channel to the in-product experience.

That’s what our research explores. We’re documenting what teams are launching, while studying how to push these AI experiences further by applying the same proven growth levers that built the first wave of PLG success.

Here are three trends redefining Product-Led Growth by dramatically shortening the time to first value and how teams can extend them to drive revenue even further.

Introducing Stephany Yong:

Stephany Yong is a Director of Product at Atlassian, where she leads PLG teams across Jira, Confluence, and Loom driving acquisition, cross-sell, and onboarding. She previously led e-commerce at Glossier and built marketing automation tools at Attentive. Earlier in her career at Facebook, she helped scale Instagram Stories and grew In-Stream Video Ads into a billion-dollar business.

Trend #1: Personalized Previews Before Signup

Problem:

Traditional PLG experiences still make users “earn” the right to see value. You click “Get Started,” fill a form, confirm your email, and land in a blank workspace. By then, 60–70% of visitors are gone.

The Shift:

AI is now powering personalized pre-signup previews that let users experience the product instantly, tailored to their intent or role. It’s the next evolution of the interactive demo: live, dynamic, and personalized in real time.

Examples

• Monday.com’s “Monday Magic” builds a live project board the moment a visitor describes their goal (“Plan a product launch”).

Pre Prompt:

Post Prompt:

• Retool lets visitors manipulate mock data and connect example APIs directly on its site. It removes cognitive friction by showing that “building internal tools” isn’t as hard as it sounds.

Pre Prompt:

Post Prompt:

Both use a “Mad Libs” / fill-in-the-blank approach that makes customization intuitive while hinting at the product’s range. You can edit the natural-language prompt, refine your use case, and instantly see changes on-screen. They’re circumventing the issue of “Writer’s Block” where a new customer doesn’t know what to prompt in a tool they haven’t used before.

When the customer wants to go deeper, then they gate the product on signing up for an account, thereby creating a higher intent user entering the funnel.

Why it works:

This experience gives users a micro-win before asking for commitment. When someone sees a tailored result that mirrors their workflow, they internalize: “This product already gets me.”

It also reframes signup as a continuation, not a hurdle. The transition to moving from the website and into the product happens when users want to save progress or invite teammates. However, the magic only works when the experience is both performant and persistent. If the website AI preview takes too long to load doesn’t carry into the post-signup, that can lead to disappointment for the user.

When you try Monday Magic, the experience starts off strong. You’re asked, “What do you want to manage?” If you type something like “Plan a product launch” or “Track marketing campaigns,” the AI instantly builds a sample project board right there on the page. It configures columns like To Do, In Progress, and Done, with example tasks, colors, and owners. It feels like your board, created in seconds, without having to sign up or set anything up.

However, there’s room for improvement when you land in product. Instead of landing on the board you created from the website, the user ends up in a generic workspace that doesn’t match what you previewed. When you navigate the product from their side nav, you can find what you had created on the website. It’s unclear if all users will know to click there though.

That small disconnect breaks the momentum. As a user, you go from “Wow, this tool already knows what I need” to “Wait, where did my project go?”

What teams often miss:

• They stop personalization at the demo: They treat the signed-out preview as a website conversion play, and not a holistic part of the activation and onboarding funnel. The handoff between the web experience and the in-product state is critical.

• They ignore acquisition intent: Previews are often disconnected from the top-of-funnel traffic that drives them. Few teams map prompts or templates to non-branded search terms like “agile sprint board” or “marketing roadmap,” missing easy personalization wins.

How to push it further:

• Treat the preview as the first session, not a demo: Persist context from preview → signup → onboarding so the first thing a new user sees is the project they already started. For example, if they built a “marketing sprint plan” on the website, they should land on that same board inside the product, with light onboarding layered over it.

• Pre-seed prompts from acquisition data: Use pre-signup data (e.g., referrer keywords or copy in ad creative) to pre-seed the AI prompt automatically on initial landing.

• Extend the conversation post-signup: Layer a micro-loop: “Want to tweak this?” before signup. It turns curiosity into co-creation on a website, driving stronger intent signals that you action on in product onboarding.

• Treat preview sessions as retargetable objects: Store the preview output and re-engage drop-offs via triggered emails or remarketing (“We saved your workspace - pick up where you left off”).

If you’ve gotten this far, you may be ready to navigate to the 🔥 section and check out our Playbook now.

Trend #2: Turning Onboarding into Instant Progress

Problem:

The expectation from users during an onboarding is to learn before they can do and that’s where nearly everyone drops off. A typical flow looks like this: users sign up, land in a blank workspace, and are told to “pick a template” or “follow a quick tour.” In most SaaS products, 60–80% of new users never create their first project, document, or workflow.

The issue isn’t that onboarding is confusing; it’s premature. Teams ask for configuration (naming, integrations, role setup) before showing value. Tutorials explain features that don’t mean anything yet to the user. Template pickers offer too many choices and not enough guidance. Users don’t churn because they can’t figure out your product, they churn because they never produce anything with it.

The Shift:

AI is collapsing the gap between sign-up and set-up. Instead of asking users to build from scratch, leading PLG teams are using AI to create a working first draft the moment intent is expressed. The goal isn’t to explain what the product does, it’s to prove it works in seconds.

Prior to AI, a new user signed up for a product like Zapier and landed in an empty workspace. They’d be greeted with a checklist or tutorial: “Connect your apps,” “Add a trigger,” “Choose a template.” Dozens of micro-decisions cause most users to never make it past the first configuration steps.

Examples Now:

• Zapier Copilot is the clearest example. Type a sentence like “When a lead fills out a form, add to Airtable and notify Slack,” and it builds the automation instantly: connectors, logic, and triggers all pre-filled. Users can edit right away, but they never start from zero. It turns “show me what’s possible” into “this already works.”
  

• Linear uses a similar approach with its AI issue assistant. When new users describe what their team is working on (“I’m setting up sprint planning for a design team”), Linear auto-creates an issue board, sprint cycles, and sample tasks in seconds.
  

• Notion’s AI templates extend the same idea to documentation. Instead of dropping users into a blank page, Notion now prompts: “What do you want to create?” A user typing “project kickoff plan” gets an auto-generated doc with structure, sections, and placeholders already filled in.

Across these examples, the pattern is consistent: AI onboarding executes outcomes on a real use case, instead of just explaining what features do.

Why it works:

The first completed action is the strongest predictor of retention. AI’s role is to make that action happen automatically.

The key is that the first generated output acts as a trust contract. If it’s relevant, editable, and fast, users keep going. If it’s generic, wrong, or slow, trust collapses and churn accelerates. This is why Replit and Linear constrain what their AI creates: not every possibility, just the most contextually relevant one. It’s easier for users to refine something specific than to invent something broad.

What teams often miss:

• Over-indexing on generative setup without checkout output quality: Rather than making onboarding “feel magical,” focus on making the results useful. When the first draft feels generic, users assume the AI doesn’t understand their intent.

Example: A user types “marketing launch plan” into your AI onboarding and gets back a vague list of tasks (“Plan campaign,” “Post on social,” “Send emails”). Compare that to Linear, which pre-fills sprint tasks and cycles tailored to a “design team launch,” with due dates and owners.

• Treating AI as a replacement for UX scaffolding instead of enhancing it: Users still need a clear next action after the initial generation. Nothing can replace a simple, opinionated UI upon initial landing, not even AI.

Example: Notion AI gets this right by adding contextual prompts like “Add your teammates” or “Set a project deadline” right after generation.

How to push it further:

• Build an adaptive loop from user edits: As users edit the AI-generated setup, capture those edits as structured data to improve suggestions for their next best action. If 70% of users rename “Campaign” to “Launch,” you should start suggesting “Launch plan” templates by default.

• Personalize “next best actions” in onboarding based on what the AI built: For example, “Since you generated a launch plan, invite your design agency next.”

Trend #3: Proving ROI Inside the Product

Problem:

ROI tracking used to be a key enterprise software feature set: dashboards, quarterly reports, and value realization slides built for exec reviews.

As AI floods into everyday tools, everyone is building productivity features. For the first time, the ROI is clear enough to show users directly.

Most PLG companies still measure usage: logins, session counts, and documents created.

However, we’ll start to see a switch to measuring impact – time saved or higher work output. Speed has become the new currency of value: how quickly users can move from an idea to output, from draft to done. Without quantifying the value of paid features, it’s hard for customers to rationalize pricing and renewals.

The Shift:

Before, productivity tools had to imply ROI through usage: “You completed 10 projects,” “You collaborated with 5 teammates.” Now, they can prove it by quantifying time saved, steps automated, and friction removed, while surfacing that data right where users work.

Examples:

Notion AI tells you exactly how much time it’s saved you in a week (“4.5 hours saved using AI writing and summarization”). Those metrics live inside the workspace helping turn efficiency into visible proof of progress for every user, not just admins.

Figma and FigJam summaries show how many teammates contributed, how quickly feedback loops closed, and how AI-assisted brainstorming sessions shortened design cycles.

Across leading PLG products, this pattern is spreading fast to drive wins on retention and increasing the upsell rate.

Why it works:

Visible ROI drives two things every PLG company needs: retention and expansion. Users who see clear value renew instinctively, and teams that can quantify that value are far more likely to upgrade.

What teams often miss:

• Not tying ROI back to paid value: Many products showcase “time saved” without linking it to why the premium tier exists. If your free AI feature saves 2 hours, tell users how the Pro version saves 10. Visible ROI should ladder directly into your pricing story.

• Treating ROI as a one-time reveal, not a continuous story: Most products show “value” once at the end of a project or billing cycle. But users need reinforcement in-session and across time. ROI should build like a narrative: “You saved 2 hours this week,” → “10 hours this month,” → “20% faster quarter-over-quarter.” Without that continuity, users forget the gains as quickly as they appear.

Pushing it further

• Tie ROI to upsell messaging: Don’t stop at showing value, connect it to what more they could unlock.

Example: Notion AI could follow up with: “You’ve saved 12 hours using AI writing. Pro users save an average of 25 hours with bulk summarization.” It reframes upselling from cost to compounding benefit.

• Localize ROI by role or team context.

Example: A designer on Figma cares about shortening review cycles, while a manager cares about projects delivered on time. Tailor the phrasing and timing of ROI nudges based on persona metadata, not a single template.

The Bigger Picture:

AI is rewriting the entire PLG funnel compressing acquisition, activation, and retention into a single, continuous feedback loop.

Here’s how the shift plays out:

• Frictionless First Looks shorten acquisition. Instead of telling users what your product can do, you show them. A personalized preview replaces website marketing copy with a live, interactive demo of the product immediately, no account required.
  

• AI That Executes accelerates activation. The first moment in-product shouldn’t be a tutorial, it should be a working draft. AI collapses the “getting started” gap by doing the first task for the user.
  

• AI-Driven Value Visibility strengthens retention and expansion. When users see their results –hours saved, faster delivery, better collaboration – they stop needing to be convinced to renew or upgrade. Visible ROI is the new upsell.

The future of PLG isn’t about helping users do more. It’s about helping them do less, sooner and see more, faster.

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🔥 Insider Growth Playbook: Rapid Time to First Value Realization

Most teams optimize signup flows or activation checklists. Few measure how long it actually takes a new user to experience a moment of visible progress. This playbook helps you find, instrument, and shorten that moment using AI or automation.

Step 1: Identify the First Moment of Proof

Look beyond your onboarding checklist. Ask:

“What’s the first thing a user could see that makes them believe this product works?”

Examples:

• A designer previewing a generated mockup.

• A project manager watching a populated sprint plan appear.

• A marketer seeing an email campaign drafted in seconds.

That’s your first proof point.

Step 2: Measure Your Time-to-First-Value (TTFV)

Instrument the time between signup and that first visible proof.
Label it separately from Time-to-First-Action (TTFA).

Most teams find it’s 3–5× longer than expected.

Track:

• % of users who reach first proof in <5 minutes

• D7 retention for users who reached it vs. didn’t

Step 3: Remove or Automate One Setup Step

Ask: “What could the product do for the user before asking them to do it themselves?”

Then automate just that piece.

• Auto-generate the first project, draft, or report.

• Use AI to summarize the user’s input and show progress immediately.

Step 4: Turn the Moment of Value Into a Loop

Don’t just show users they’ve succeeded, use that proof to pull them forward. When someone reaches their first outcome, reinforce it and offer a logical next step.

Examples:

• “You saved 18 minutes by automating the setup. Want to apply this automation to your next project?”

• “This sprint plan was built 40% faster than average. Try adding teammates to accelerate delivery.”

Treat value moments as conversion triggers. Log who saw them, who clicked next, and how that affects activation or upsell rates.

Step 5: Reinforce Value Across Channels

The first proof of value should echo beyond the session. Use it to re-engage, retain, and upsell.

Examples:

• In-product: Show progress streaks (“You’ve saved 9 hours this month”).

• Email: Weekly summaries that celebrate outcomes, not activity (“You completed 3 projects 2× faster than average”).

• Pricing flow: Remind users what they’ve gained (“Pro users automate 5× more workflows — that’s 15 hours/month saved”).

Visibility turns into habit; habit turns into renewals.

Tactical tip: Use the same data powering your ROI messaging to personalize upsells. “You’ve already unlocked X benefit; Pro multiplies it.”

Going through this exercise shouldn’t take months of analysis. Most teams can complete it in under two weeks and start seeing faster activation immediately after. By the end of that sprint, you’ll know exactly how long it takes a new user to experience value and how to cut that time down using AI and automation.

Want help on how to leverage AI within your existing PLG growth engine?

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We give you an edge on what’s happening today: actionable strategies, proven frameworks, and access to how top operators are scaling companies across industries. This newsletter helps you boost your key metrics—whether you're launching a product from scratch or scaling an existing one.

What We Stand For

• Actionable Insights: Our content is a no-fluff, practical blueprint you can implement today, featuring real-world examples of what works—and what doesn’t.

• Vetted Expertise: We rely on insights from seasoned professionals who truly understand what it takes to scale a business.

• Community Learning: Join our network of builders, sharers, and doers to exchange experiences, compare growth tactics, and level up together.

Introduction

Anyone can ship an app in 48 hours with AI coding tools. In 48 days, fewer than 2–5%* reach $100 in revenue. The real failure point is after launch: most projects never get a single paying customer or sustained usage. The winners won’t optimize time-to-demo, they’ll optimize time-to-first customer, time-to-returning users, and time-to-repeatable growth. This piece shows where today’s tools help (and don’t) and what defaults platforms should ship next to close that gap.

The Missing Half

AI coding platforms have solved half the problem i.e., democratizing software creation. Cursor ($9.9B valuation), Lovable (unicorn in 8 months), and Replit ($3B valuation) collapse idea → code → deploy to hours. That’s the Prompt → Product win: usable code, live URLs, a real app you can click.

But to truly deliver on their promise, the platform that solves value capture will own this market. Code generation speed is table stakes; helping users make their first dollar is the unbreachable moat.

We call this missing piece PIE, the post-ship outcomes that turn demos into businesses. The framework comes from the idea that if we're enabling everyone to build, then everyone should get a piece of the PIE, not just the ability to bake it.

Introducing Aparna

Aparna Somaiah is a fractional PM who partners with early-stage teams on product strategy and growth. Previously at Airbnb (mobile developer experience) and VMware (enterprise cloud infrastructure), she uses AI coding tools to rapidly prototype and ship solutions. You can connect with her on LinkedIn.

Now, what is PIE?

PIE = Profit, Impact, Expansion. This is the outcome after you ship that determines whether your project becomes a business, reaches real users, or grows into something bigger. PIE is not a replacement for AARRR; it’s a platform-side lens for what must be defaulted so builders can cross the deploy → first user → first dollar barrier.

PIE is your early scoreboard for whether projects are on a path to sustainable growth. Instead of fuzzy concepts like “traction,” it defines three dimensions with clear, testable milestones:

Profit – Are users willing to pay?

• First paid user (>$1)

• $100 MRR

• $1k MRR

Impact – Is the product sticky and valuable?

• 10 WAU

• 50 WAU

• Day-30 retention > 20% on a core action

Expansion – Does usage spread or compound?

• Second project in the same account

• Teammate invite

• Channel attach (e.g.,>100 email list, referral loop enabled)

To unify these into one metric, track the PIE Rate:

PIE Rate per 1,000 projects = % that hit any milestone above within 60 days.

Think of this as the “DAU/WAU” equivalent for platforms. It gives your team a single, measurable scoreboard for whether projects are breaking through to real adoption and monetization.

It represents the core promise of democratizing software creation: if everyone can build, everyone should get a piece of the economic and social value that software creates. Today, most platforms drop users right after deployment without helping them achieve any of these outcomes.

The Distribution Desert

Every platform uses the same psychology trick: the cost-ladder. You start free, burn through credits fast, and hit limits right when you're excited about your project. You then upgrade to keep momentum. Across the category, the loop is the same: Lovable gives 5 free daily credits, then paid monthly credit buckets; Replit bundles $25 monthly credits and meters Agent/hosting; Cursor includes $20 of agent usage on Pro: each creates friction exactly when you want to iterate, and paying creates commitment.

This skin-in-the-game approach works brilliantly for getting users to finish and deploy their projects. But here's the problem: most platforms capture this commitment without bridging to PIE outcomes. This is where users hit the Distribution Desert.

Stage 1: Invest ~$100 + ~48 hrs to build a gorgeous app

Stage 2: Looks incredible. Deployed and integrations wired

Stage 3: No users beyond friends and family

Stage 4–8: Lack of traction → morale decay → churn

Platforms capture commitment, then abandon users at the hardest step: finding and converting the first 10 real users. That’s the Product → PIE gap.

Users stuck in the desert often build multiple projects hoping one will break through, but without PIE infrastructure, each new project hits the same distribution wall. Instead of one successful app, they end up with a graveyard of deployed demos.

Users who can't reach PIE churn within 3-6 months, regardless of their initial investment. The commitment ladder breaks when business outcomes don't materialize.

The distribution desert is not new; most apps die here. What’s new is that these platforms market the full journey to “ship a product”. If the journey stops at deploy, the promise is only half fulfilled.

Take Lovable's growth motion: it's a masterclass in growth hacking with high-signal leadership, back-to-back hackathons, free-token bursts, and relentless social proof. It's exactly what you run in a nine-month-old land-grab: compete on acquisition velocity and headline ARR. But with LLM costs still squeezing margins and most cohorts <12 months old, the real operating heartbeat is MRR and retention, not splashy ARR. The distribution desert is where those surges either convert or decay. (This is true across the category; Lovable just shows the acquisition playbook at its best.)

This isn't an argument that those tactics "won't work"; they work brilliantly for attention. The question is whether that attention should be routed into default business rails - the basic operating system every serious product needs to survive. Hence, acquisition spikes cross deploy → first user → first dollar: payments wired by default, SEO + analytics on, proof/trust loops, and a guided "first-10 users" checklist. Without these rails, you get short-cycle MRR and churn; with them, builders escape the desert and hit PIE.

If platforms shoulder the operating rails that move builders from deploy → first user → first dollar, pricing should shift to reflect outcomes, not just usage.

If you’ve gotten this far, you may be ready to navigate to the 🔥 section and check out our Playbook now.

The Partnership Patterns

The partnership patterns via integrations (Replit, Lovable, Windsurf), hackathon bundles (Bolt.new hackathon, Lovable Shipped, TPF Mosaic), and newsletter perks reveal exactly where platforms may be evaluating opportunity. Every platform has partnered with identical services:

• Deployments: Supabase across all platforms

• Payments: Stripe is also available across all platforms

• Authentication: Scalekit, Clerk, and Auth0 in every integration list

• Hosting: Vercel, Netlify, AWS, GCP, CloudFlare everywhere

• Marketing and Sales: Hubspot, Apollo.io, Google Ads, MailChimp

• Collaboration: Linear, Superhuman, Granola

• And more…

When every platform partners with Stripe, that's not a coincidence. It's acknowledgment that payment integration, a core Profit component, remains too complex for AI to generate reliably. These partnerships map directly to PIE gaps, revealing exactly where Prompt → Product breaks down and specialized infrastructure is needed.

Default rails ≠ default blast. The goal isn't to flood the internet with new applications; it's to default the plumbing (payments, SEO, analytics, proof) so serious builders can validate faster.

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Evidence the PIE Gap Exists: AI Coding Assistants Reality Check

Here's how the five major AI coding assistants and AI coding IDEs stack up on enabling PIE today.

Legend: 🟩 = Defaulted & guided · 🟨 = Available/turnkey · ⬜ = 0 Absent/DIY. Colors reflect defaulted, guided paths to first users/first dollar, not mere availability.

Indicative snapshot based on public signals and hands-on use; this changes fast. Achieving PIE is about moving to Green. This looks binary, but it isn’t. Green ≠ “better product,” it means the platform makes that step default. Yellow means the capability exists with a guided hand-off (often perfectly fine for technical users). Gray often reflects product philosophy (e.g., IDE-first tools like Cursor intentionally avoid bundling business rails). Note: execution quality varies - Replit excels at hosting, Bolt/Cursor at deployment. This scorecard measures enablement, not excellence.

Here's what's happening at each platform:

• Replit: One-click deploy + easy first-party hosting with transparent pricing; basic analytics. Missing: automatic payments, SEO defaults, and trust/proof loops.

• Lovable: Top-of-funnel masterclass; guided integrations and domains. Missing: first-party hosting, default analytics + milestone tracking; stronger design-to-code handoff.

• Bolt: End-to-end rails in browser (beta): integrated deploy/hosting, Stripe-ready starters, built-in analytics. Missing: consistent SEO defaults; mature trust loops.

• Cursor: IDE-first; elite iteration & MCP integrations; deploy/hosting via hand-offs. Missing: bundled business rails (domains, payments, SEO, analytics, proof).

• Windsurf: Agent-forward editor; strong Prompt→Product, thin Product→PIE. Missing: bundled domains/hosting/payments/analytics/SEO/proof.

The Coming Shift: Outcome-Based Pricing

As platforms start defaulting the rails that create outcomes: hosting, domains, payments, SEO, analytics, proof loops, pricing should evolve to match. There will be a rise in outcome based pricing models as a result: $0 platform fee until the project earns, then a small, capped share or a fixed operating fee after a clear threshold (e.g., first $1,000 cumulative revenue). This aligns incentives (the platform wins when builders win), improves margin health, and, crucially, rewards the work of turning Prompt → Product into PIE (Profit, Impact, Expansion).

This is consistent with adjacent ecosystems where builders accept revenue-aligned fees (e.g., commerce/payment platforms that combine a subscription with a small per-transaction take). For solo creators and small teams who'd otherwise spend tens of thousands with an agency to reach the same "operating product" (deploy, payments, analytics, SEO, basic GTM), the swap is compelling: predictable build-time subscription + light success fee once revenue begins = meaningful savings and a clearer path to sustain.

For example

• $0 platform fee until $1,000 cumulative revenue

• Then 2–5% take rate with a monthly cap (e.g., fee caps at $99/mo) or a flat $29–$49/mo operating plan once past the threshold

• Keep the existing subscription for build features; reserve the success fee for the operating layer (payments/billing/taxes, analytics, SEO, proof loops, compliance)

🔥 Insider Growth Playbook: 10 Vibe Coding Builder Experiments to Cross the Product → PIE Bridge

Shipping an app with tools like Cursor, Replit, Lovable, or Windsurf is easy. The hard part is what comes after: finding real users, earning your first dollar, and proving your idea can grow.

The playbook below is designed for you, the builder. These aren’t just feature ideas for platforms they’re joint experiments you can run right now using what the vibe coding platforms already give you.

The rule of thumb: the platform handles the wiring (payments, SEO, analytics, proof loops), and you supply the small but critical inputs (copy, tags, prices). Run these as lightweight tests, and you’ll dramatically improve your odds of crossing the bridge from demo → first user → first dollar.

Who's it for:

• Platform = AI coding tools (Cursor, Replit, Lovable, Windsurf, Bolt) that provide the infrastructure and defaults

• Builder = Anyone using these AI coding tools to create applications - from solo founders to PMs at companies validating new ideas

How to read this:

• Platform provides = what the tool should default/generate for you.

• Builder does = the small inputs creators add (copy, tags, prices).

• Success signal = the single number that means it worked.

• Defaults beat intentions. If payments, SEO, proof, and invites are on by default, builders cross deploy → first user → first dollar with less friction.

• Tiny inputs, big unlocks. Builders supply copy, prices, and tags; the platform handles wiring, compliance, and measurement.

• One scoreboard. If PIE Rate per 1,000 projects rises, the bridge from Product → PIE is getting real.

Why This Matters Now

Assume 2-5% of deployed projects ever reach $100+ monthly revenue. This represents a massive opportunity: platforms that can systematically improve Profit, Impact, and Expansion rates will dominate the market. The timeline is urgent. Over the next 12–18 months, Big Tech bundling will make Prompt→Product a commodity; PIE becomes the defensible surface.

The metric that matters isn't total deployments or daily active users. It's PIE stories per 1,000 users: how many users achieve meaningful Profit, Impact, or Expansion outcomes. This is the measure that will separate the platforms that create businesses from those that just make demos.

The Bottom Line

Prompt → Product is solved. Anyone can now build and deploy apps quickly.

The winners will be platforms that solve Product → PIE. They'll help users find customers, generate revenue, reach real users, and scale successfully.

Stop optimizing "time to demo."

Start optimizing "time to PIE."

The platforms that systematically enable users to get their piece of the pie, not just bake it, will own this market.

Want help turning your vibe coding demos into a revenue generating products?

Let’s talk.

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Introduction: The False Comfort of Product Narratives

Scroll through LinkedIn lately and you’ll see the AI victory lap refrain: “We shipped v1!” “Now with AI!” “AI-powered [insert noun here]!” Likes and buzz trample over substance and impact.

Take the Xbox job post. Principal Dev Lead Mike Matsel shared a recruiting image with Xbox branding, inviting submissions from “folks with experience in device drivers, GPU performance…”—right after mass layoffs. The AI-generated graphic that accompanied it was an embarrassment, with the monitor output hilariously flipped toward the viewer. Commenters didn’t hold back: “This is so tone deaf that I hope it is satire,” one wrote, while another noted the irony in using sloppy AI to hire designers just after cutting so many jobs.

Or Duolingo’s CEO declaring the company would become “AI-first”, with “AI proficiency” influencing hiring and promotions. On paper, it’s an ambitious vision—one that could transform the company if executed with care, transparency, and a genuine focus on user value. But more often than not, moves like this end up as blunt instruments: alienating teams, spooking customers, and reducing AI to a compliance checkbox rather than a catalyst for better outcomes.

Then there’s Figma’s Make launch—a headline-grabbing AI prototyping tool positioned as a leap forward. But compared to v0, it’s a step back. It doesn’t address the real opportunity in collaborative design—it just ticks the “we have AI too” box.

And here’s the thing—you hear about these launches, and then… nothing. No adoption stories. No systems evolving. Just another hype post fading into LinkedIn’s algorithmic abyss.

First-principles thinking remains our North Star—AI doesn’t change that. The point of Product isn’t just building quickly; it’s asking why we’re building. With AI, the hard work shifts toward the most fun part of the cycle—exploration, validation, system design—while execution becomes faster and cheaper. That means there’s also more junk flooding the world. Our job? Make products people actually use. That’s the real point of Product.

To help illustrate how fundamentally important this is, we partnered with Jerrell Taylor, an AI Strategist with Distly.ai to co-author this piece with us and offer each of you with a playbook on how to implement AI for lasting change.

What AI Can and Can’t Do

The first commercially successful steam engine, built in 1712, had one job: pumping water out of a coal mine. It was a miracle of engineering that solved a single, expensive problem. For decades, that's all steam power was. Just a better pump applied to an old process. No one looked at that engine and saw the Industrial Revolution. They saw a dry mine.

You are making the exact same mistake with AI today. You're buying a very expensive pump.

Most companies are celebrating AI for its obvious talents: faster pattern recognition, task automation, and summarization. But they are applying this incredible new power source to the same broken system that created their problems in the first place.

The real danger isn't that AI will fail; it's that applying it thoughtlessly will succeed just enough to create chaos. It speeds up one team while creating massive bottlenecks for the next. It generates reports nobody has capacity to act on. It automates a task without changing the broken incentive structure that governs it.

Unstructured coordination is the real bottleneck, and AI alone cannot solve it.

To work across a business, AI requires two things that most organizations completely lack:

A Unified Context: Without a single, shared understanding of reality, AI is just a powerful idiot, optimizing tasks based on fragmented and contradictory inputs. It can't generate system-wide coherence if you're feeding it organizational chaos.

An Architecture for Coordination: This isn't about plugging in a tool; it's about building a system. That requires:

• Composition: the blueprint for how pieces fit together and actors connect in shared workflows

• Governance: the rules, incentives, and metrics that dictate behavior within that system

• Decision-Making & Execution: the logic for how choices are made and executed

Look at what happened to trucking. For decades, a trucker’s job was a complex mix of driving, negotiation, and relationship management based on informal trust. Then platforms like Uber Freight and Convoy imposed a new architecture.

Uber Freight’s Transportation Management System Dashboard.

They started by changing the Representation of the work itself, reducing a complex, judgment-driven job into a few measurable metrics that the platform could track. Once reality was represented by their data, they seized control of Decision-Making; algorithms now decide pricing, routing, and load assignments, removing the trucker's ability to negotiate value. The driver's role was then reduced to pure Execution. The job itself was unbundled and rebundled—a new Composition—into an algorithmically managed workflow. Finally, they enforced a new form of Governance, defining performance through narrow metrics and removing the autonomy that came with informal reputations and trust.

The platform didn't help truckers do their old job better. It used the levers of coordination to make their old skills irrelevant, fundamentally changing the system and their role within it.

The question now isn't whether AI can help you work faster. The question is: does your current role have any right to exist when its foundational constraints are gone?

The Myth of the Linear Product Cycle

Most product teams still think in the same neat, linear stages: Discover → Build → Launch → Measure. It’s tidy, it’s intuitive—and it’s a dangerous oversimplification.

In reality, products live inside systems, and systems are recursive and layered. One release doesn’t mark the end of the work—it triggers ripples across user flows, data pipelines, integrations, and operational processes.

A new feature isn’t a self-contained unit. It sits inside an ecosystem of other features, workflows, and dependencies. Touch one part, and you inevitably affect others—sometimes in ways your roadmap never accounted for.

• Example 1 – AI lead scoring in a CRM: A sales org adds AI-powered lead scoring to prioritize outreach. Local performance looks great—top leads get called faster. But the scoring model relies on data marketing doesn’t capture, forcing marketing to rework forms and campaigns. Conversion improves slightly, but campaign costs spike.
  City planning parallel: It’s like adding a new freeway off-ramp without improving the connecting roads—traffic moves faster in one spot, then jams up elsewhere.

• Example 2 – Chatbot for customer support: A company launches an AI chatbot to handle FAQs and reduce ticket volume. It works—ticket volume drops—but now edge cases pile up for human agents, slowing resolution times for complex issues. Support quality metrics worsen even though the bot’s success rate is “high.”
  City planning parallel: Imagine building an express bus lane but routing all remaining traffic through a single narrow street. You solve congestion in one area but create bottlenecks in another.

• Example 3 – AI-assisted content creation tool: A newsroom adds AI to help reporters draft articles faster. Output volume rises, but editing teams are overwhelmed by fact-checking and style corrections. The bottleneck just moves downstream.
  City planning parallel: This is like constructing a massive new residential tower without expanding the water lines or power grid—the new capacity strains the infrastructure that supports it.

The problem? Most organizations optimize for local performance (“Did this feature hit its adoption target?”) instead of global throughput (“Did this change improve the whole system’s speed, resilience, and value delivery?”).

And because the system is interconnected, solving one problem often creates new friction elsewhere. That’s why adding yet another feature can slow onboarding, confuse users, or break downstream workflows.

What’s needed instead is governed, modular composition—designing like a city planner, not a developer dropping buildings at random. In a city, you don’t just plop a skyscraper wherever there’s space; you consider roads, utilities, zoning, and how it will connect to what’s already there. Product systems deserve the same level of intentionality.

Reframing the Why – Jobs to Be Done at the System Level

For years, “Jobs to Be Done” has been the playbook for product teams. You zero in on the user’s immediate goal — “When I’m a business traveler, I want to check in quickly so I can get to my meeting.” It’s a great way to make one step smoother.

But the problem is that in the AI era, that focus can blind you. You end up perfecting a single step in a process that’s broken end-to-end. AI blows up the old constraints those jobs were written for — which means the real opportunity isn’t shaving seconds off a task, it’s re-wiring how the whole system works.

Most companies default to making individual steps faster or cheaper. The smarter move is to design the system so every part works in sync, all coordinated toward a single outcome.

Case Study #1: Coordinate Operations Around Revenue, Not Just Efficiency

Problem
A Fortune 500 HVAC company Jerrell worked with was hemorrhaging money on onsite visits that were taking too long. This decreased the number of onsite visits a field tech could perform daily, which ultimately minimized potential revenue. The general concern: field techs were spending too much time looking up information related to the equipment they needed to fix.

The obvious JTBD seemed clear: “Help field techs find repair manuals and parts information faster.”

The team built an AI assistant for instant technical documentation lookup. It shaved a few minutes off each visit, but the bigger issues (low upsells, unplanned repeat visits, wasted truck rolls) remained untouched.

Solution: System-Level Coordination Powered by AI
Jerrell reframed the goal: “How can the entire service operation work together to grow revenue?”

Instead of just speeding up manual lookups, the team designed a coordinated service system where AI linked:

• Route planning with inventory availability

• Equipment diagnostics with customer purchase history

• Predictive maintenance schedules with seasonal demand windows

Now, every tech dispatched arrived knowing:

• Which equipment was near end-of-life

• Which upgrades the customer was most likely to approve

• Which parts were in-stock for same-day installation

Impact

• +40% upsell conversion

• Technicians consistently turned routine calls into high-value sales

• Repeat truck rolls dropped because jobs were completed on the first visit
  
  

Lesson: Don’t use AI to make today’s jobs faster — use it to redesign the job so every action directly drives the top business outcomes.

Case Study 2: Design Systems That Anticipate and Act, Not Just Respond

Problem
Another project Jerrell worked on was with a hotel chain that wanted to improve guest satisfaction. Bookings were down. Reviews hinted at the cause: “Staff was polite, but it felt like they just wanted to get me out of line.” Guests weren’t angry — they were disengaged.

The quick fix practically wrote itself: give front desk clerks an AI tool that could spit out pre-written answers to common questions, plus upsell scripts for the occasional room upgrade.

The team built it and it did exactly that. Clerks could respond faster, stick to the script, and check people in with military precision. And yet… nothing meaningful changed. Bookings didn’t bounce. Spend per guest stayed flat. The experience was still the same thin layer of transactional service — just delivered at speed.

Solution: AI-Driven Guest Journey Coordination
Jerrell shifted the question from “How do we answer faster?” to “How do we anticipate and maximize guest value?”

AI became the hub linking:

• Guest preference profiles from past stays

• Real-time room availability and staffing levels

• Local events, weather, and seasonal packages

This let the system proactively design personalized stays:

• Recommending spa treatments during rainy days

• Timing restaurant offers to guest schedules

• Suggesting local experiences aligned with guest interests

Impact

• +23% ancillary revenue (upsold amenities, experiences, and services)

• +18% lift in repeat bookings

• Higher guest satisfaction scores from personalized service flow

Lesson: Use AI to unify data, timing, and activities across every customer touchpoint so that actions compound into measurable value for your user.

Most product teams are still asking the wrong question. They're optimizing for how people navigate broken systems instead of asking what becomes possible when those systems are rebuilt from the ground up.

Old Approach vs New Approach

The Business Impact of Coordination

In most companies, the way the org is structured directly shapes the way the product behaves. If teams operate in silos, customers feel it — through disjointed workflows, inconsistent messaging, and features that don’t quite fit together. Poor internal coordination doesn’t just create inefficiency; it fragments the user experience.

Coordination is often mistaken for a tax on speed — extra meetings, endless Slack threads, more process. But in reality, it’s an investment. Done well, it compounds in value over time, paying dividends in faster execution, cleaner handoffs, and a product that feels whole. And critically — it’s not process for process’ sake. It’s about aligning the entire system around the end user and the core business problem you’re solving, so every decision moves in the same direction.

If you’re badly coordinated around the problem, you’re badly coordinated overall — and it will show. It will show in how slow you move, in how messy your execution feels, and in the cracks your users encounter. It also reveals a deeper issue: you don’t truly understand the problem. And when that’s the case, there’s a high chance the user won’t feel the difference you set out to make.

Take onboarding as an example.
The real problem: new users need to reach their first moment of value quickly and confidently. If they stall or get confused, they never experience the payoff your product promises — adoption drops, retention suffers, and all the money you spent acquiring them is wasted.

In a poorly coordinated org, each team makes changes in isolation:

• Marketing’s tweak: Adds three new questions to capture richer lead data.

• Product’s tweak: Inserts an interactive tutorial relevant to only one segment.

• Compliance’s tweak: Requires document verification earlier to reduce fraud.

• Support’s tweak: Adds a mid-flow help widget that opens in a new tab.

Each change makes sense locally — but together, they bloat the flow, turn 90 seconds into 7 minutes, double drop-off, and tank activation. Different product lines even run different versions, multiplying the work for marketing, sales, and analytics.

Here’s the contrast:

The takeaway: coordination isn’t about adding process — it’s about building a shared mental model of the problem, then attacking it in a way that makes every change push toward the same outcome. AI can supercharge execution, but only if the system is already aligned on what matters.

When systems are designed with coordination at the core, what used to be seen as a “cost center” — operations, support, enablement — starts generating growth. Shared playbooks, integrated tooling, and feedback loops enable repeatability. Repeatability enables scale. And scale, done right, drives margin.

In the AI era, where capabilities can be cloned in days, your enduring advantage won’t be what you build — it will be how well you coordinate. And the only coordination that matters is the kind that keeps the user’s problem at the center. That orchestration is the moat.

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IGG Playbook – Tactics for Designers, PMs, and Builders

Think of this as your operating manual for building in the AI era—small, intentional actions that compound into system-wide leverage. Tackling the system doesn’t mean doing everything; it means identifying the few nodes where change will create the widest ripple effect.

1. Daily Behaviors that Reinforce System Coherence

• Design for context reuse, not just feature delight
  Every new capability should plug into existing context flows so it strengthens the ecosystem. Avoid one-off “feature islands.”
  Example: A healthcare app’s new “upload insurance card” flow also populates billing, appointment, and pharmacy systems—so the same data improves multiple journeys.

• Evaluate with system-level metrics, not just local KPIs
  Measure improvements across the whole journey—time to complete a job, reduction in hand-offs—not just success of one step.
  Example: Instead of only tracking chatbot resolution rate, measure “time to resolution” across all support channels after deploying the bot.

• Prioritize interfaces that close feedback loops
  Build touchpoints that capture real user signals (errors, satisfaction, drop-offs) quickly so the system adapts in real time.
  Example: An AI writing tool flags when users undo an auto-edit, instantly retraining the model to avoid that change in the future.

2. Structuring Sprint Rituals Around System Health

• System Check-In at Sprint Planning – Before committing, review cross-team dependencies, upstream/downstream effects, and integration points.

• System Impact Review in Retro – Ask “Did we reduce friction in the broader flow?” and “What new dependencies did we create?”

• Cross-Functional Pairing in Grooming – Designers, PMs, and engineers jointly assess systemic impact—so decisions aren’t made in functional silos.

Example: An AI-powered real estate pricing feature is reviewed not just for algorithm accuracy, but for its effect on lead conversion, listing views, and sales team workflows.

3. Re-Center on JTBD in the AI Era

With AI, many jobs can be solved with smaller, more elegant system changes rather than heavyweight new builds.

• Always start with the Job to Be Done, not the AI tool.

• Ask: If this job could be done automatically or with less interface complexity, what’s the lightest system design that would enable it?

• Avoid overbuilding—system leverage means picking the smallest set of changes that create the widest ripple effect.

Example: Instead of building a new “AI deal room” for real estate agents, embed an AI summary widget directly into the existing listing view—solving the “need to brief clients quickly” job without another tool or login.

4. Standing Up a Coordinated System (12-Week Roadmap)

Start by finding the few points that matter most. Improve them. Measure. Repeat.

Quick Glossary for Builders

• Node – Any step, service, or data point in the user’s journey. Think of it like an intersection in a city: if it’s congested, everything slows down.

• Core Node – A node that touches many other parts of the system. Fixing it creates ripple effects everywhere.

• System Impact Section – A short write-up in your PRD or ticket that asks: How will this change affect the broader system, not just this feature?

• Leverage Mapping – The process of drawing out the end-to-end job (JTBD), marking all the nodes, and circling the few that have outsized influence.
  

Weeks 1–2 – Find the Core Nodes

• What to do:

  • Map the job-to-be-done from start to finish. Each step = a node.

  • Circle the core nodes — the ones that appear in multiple flows or feed many others.

  • Define 2–3 system-level metrics (e.g., time to complete the job, hops removed).

• Example (Telehealth):
  JTBD = “Complete a patient visit.”

  • Nodes: Appointment booking, Insurance verification, Doctor’s notes, Prescription.

  • Core nodes: Insurance verification and Doctor’s notes.

  • Metrics: Visit completion time, duplicate data entry, drop-off rate.

Weeks 3–4 – Wire & Ship the First Change

• What to do:

  • Add feedback capture to your first core node (e.g., “Was this correct?” prompts, undo signals).

  • Ship a thin, reversible improvement.

  • Measure against system-level metrics.

• Example:
  First node = Insurance verification.

  • Add a simple confirmation step when patients upload cards.

  • Improvement: Auto-fill insurance info into the patient profile.

  • Impact: Faster visits, fewer dropped calls.

Weeks 5–6 – Address the Next Node

• What to do:

  • Move to the second core node.

  • Add feedback wiring.

  • Ship a thin-slice improvement.

  • In every PRD, include a System Impact Section (describe ripple effects).

• Example:
  Next node = Doctor’s notes.

  • Capture when doctors delete or fix AI transcriptions.

  • Thin-slice: AI transcription embedded directly in notes screen.

  • Retire the old “upload dictation” step.

Weeks 7–8 – Close the Loops

• What to do:

  • Review both nodes together.

  • Tighten connections (share context between them).

  • Update your Leverage Map — the diagram of nodes, core nodes, and flows.

• Example:
  Insurance verification and notes now connect.

  • Verified insurance shows inline in the notes interface.

  • Doctors no longer ask patients for info they already entered.

  • Updated leverage map shows fewer hops for both patient and doctor.

Weeks 9–10 – Iterate & Scale

• What to do:

  • Identify the next core node.

  • Repeat the improve–measure cycle.

  • Expand feedback wiring to more touchpoints.

• Example:
  Add the Patient profile node.

  • Consolidate data flows from insurance, notes, and prescriptions.

  • Feedback loop: track when patients correct personal details.

  • Outcome: Faster visits and fewer pharmacy mismatches.

Weeks 11–12 – Institutionalize

• What to do:

  • Make leverage mapping and reviews part of sprint rituals.

  • Require a System Impact Section in every PRD.

  • Set a quarterly target: improve or retire at least one core node.

• Example:
  New JTBD = “Refill a prescription.”

  • Core nodes = Prescription data + Pharmacy integration.

  • Team now runs the same playbook: map → circle nodes → wire feedback → ship thin slice → measure → update map.

✅ By Week 12:

• You have a living leverage map

• 2–4 core nodes improved with feedback wired in.

• Redundant flows retired.

• A repeatable method for finding and fixing the next leverage point.

Questions to Ask Before Building with AI

AI gives us the power to build and ship faster than ever before. But speed creates its own traps. The old truth that not everything is worth building hasn't changed; what's changed is the catastrophic cost of getting it wrong.

When you build with AI, you're not just creating a feature; you're embedding logic deep into your company's nervous system. Building the wrong thing doesn't just create waste; it scales dysfunction at an unprecedented rate.

So, how do you ensure you’re building a scalable system instead of a scalable screw-up?

The good news is there's a way to solve this. It starts by asking four systemic questions before you go headfirst into development. To see this framework in action, consider the quietly devastating example of AI-as-coordination happening in your garbage can.

Waste Management didn't just add sensors to dumpsters. They turned the entire concept of "trash pickup" into an algorithmic system, and it provides a perfect playbook.

Here's how to apply that thinking:

1. The Representation Question: Are you creating truth or automating lies?

For decades, garbage collection was based on schedules and guesswork. "We pick up every Tuesday" regardless of whether bins were empty or overflowing. Waste Management eliminated the fiction. Smart sensors in dumpsters, trucks, and processing facilities create real-time "waste intelligence"—exactly how full every container is, what type of waste, optimal pickup timing, contamination levels. Mathematical precision about what actually needs collection.

Before you build, ask: Is your AI translating messy inputs, or is it enforcing a standard that creates unified context for reliable decision-making?

2. The Composition & Governance Question: Are you reducing friction or scaling chaos?

Waste Management didn't give garbage trucks better GPS. They made traditional waste management routes obsolete. Their AI coordinates drivers, processing facilities, recycling centers, and customer billing in unified workflows where trucks only visit containers that actually need emptying. No more driving predetermined routes to empty bins. Pure algorithmic coordination from sensor data to optimized collection.

Before you build, ask: Does this tool pull all actors into a shared process that reduces system-wide friction, or does it give them another place to hide, ultimately scaling your coordination problems?

3. The Decision-Making & Execution Question: Are you helping humans or replacing judgment?

Waste Management's AI makes decisions that used to require armies of route planners and dispatchers: which trucks go where, when to empty containers, how to optimize fuel consumption, which facilities can handle which waste streams. Drivers aren't planning routes. They're executing algorithmic instructions. The system coordinates thousands of micro-decisions across multiple cities simultaneously.

Before you build, ask: Where is decision-making authority moving? Are you helping humans do their old job faster, or are you embedding your best strategic logic into a system that executes at a scale no human team ever could?

4. The Feedback Question: Are you measuring tasks or system health?

Waste Management doesn't track "trucks dispatched" or "bins collected." They monitor the health of entire urban waste ecosystems: contamination rates, recycling efficiency, fuel optimization, predictive maintenance across fleets. Humans aren't managing individual routes; they're evaluating whether the entire waste-flow prediction engine is optimizing city cleanliness.

Before you build, ask: Is the human-in-the-loop designed to correct AI mistakes, or are they positioned to evaluate the holistic impact of the entire system, closing the feedback loop on what truly matters?

Waste Management succeeded because they understood that garbage collection wasn't their product. Urban efficiency coordination was their product. They built a system so superior that cities are redesigning their entire waste infrastructure around algorithmic intelligence instead of human schedules.

The challenge today isn't finding a use case for AI. It's having the organizational courage to ask these questions and act on the answers, no matter which legacy roles and processes become obsolete.

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🔥 System Design is the AI Era's Missing Leverage

In the AI era, speed alone isn’t a competitive advantage — coherence is.
The organizations that win are the ones that connect their efforts into systems that reinforce each other, rather than scattering into isolated projects that look good in slides but break in reality.

Strong coordination ensures that every improvement compounds — onboarding gets smoother, workflows feel effortless, and the value of your data multiplies over time. Weak coordination guarantees the opposite: local wins that leave the end user unchanged.

AI raises the stakes. It rewards teams who treat system design as a first-class discipline — governing interfaces, closing feedback loops, and making a small number of high-leverage changes that drive the most downstream value.

But here’s the nuance most miss: system design is not just an architectural or operational challenge. It’s a design challenge. The way your teams model workflows, shape interfaces, and define affordances determines how the system behaves, how easy it is to extend, and how seamlessly AI can plug into it.

That’s where we’ll focus next: approaching system design in AI from the designer’s point of view — how to create the cues, constraints, and interactions that make a system both adaptable and coherent, even as AI reshapes the way work gets done.

Want help on any AI-powered growth challenges you have? Let’s talk!

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👋 Hi, it’s Gaurav and Kunal, and welcome to the Insider Growth Group newsletter—our bi-weekly deep dive into the hidden playbooks behind tech’s fastest-growing companies.

We give you an edge on what’s happening today: actionable strategies, proven frameworks, and access to how top operators are scaling companies across industries. This newsletter helps you boost your key metrics—whether you're launching a product from scratch or scaling an existing one.

What We Stand For

• Actionable Insights: Our content is a no-fluff, practical blueprint you can implement today, featuring real-world examples of what works—and what doesn’t.

• Vetted Expertise: We rely on insights from seasoned professionals who truly understand what it takes to scale a business.

• Community Learning: Join our network of builders, sharers, and doers to exchange experiences, compare growth tactics, and level up together.

Introduction:

As companies mature, many are chasing larger customers—moving from small accounts to mid-market and enterprise deals.

And even in the mid-market space, what used to be a one-call close is now a multi-step process. Decision-makers want proof. Larger clients often start with a single department, team, or location. That means more and more companies are relying on pilot-led selling—where the question isn’t just “can we land them?” but “how do we expand them?”

Landing the first department or logo is only step one. The real challenge is designing an engine that converts that initial pilot into a company-wide rollout.

This type of execution is difficult to reliably achieve, but once done, it provides a level of scale and speed that becomes category-defining and a true competitive edge.

Without a system that unites product, customer success, and sales around customer outcomes, expansion fails. This leads to onboarding friction, retention risk, and missed revenue opportunities.

ServiceTitan built a repeatable expansion loop—one that turned pilots into high-value accounts by aligning value delivery, customer motivation, and enterprise readiness.

This post breaks down the playbook behind the engine that created $55M+ in annual expansion revenue:

ServiceTitan generated $739.5M in FY25 platform revenue—driven by 74.7% retained revenue, 17.8% from new customer acquisition, and 7.5% expansion through upsells, cross-sells, and increased usage.

Introducing Mark Weeks:

Mark Weeks is a product leader who helped scale ServiceTitan (TTAN) and Procore (PCOR)—two category-defining AI-powered vertical SaaS platforms—to successful IPO. As Director of Product Management at ServiceTitan, he led new market expansion, turning early-stage bets into eight-figure ARR engines. Through Multilogue.io, Mark shares systems thinking, frontier GTM strategies, and behind-the-scenes stories from building at the edge of product and growth.

You can connect with him on LinkedIn to learn more about his work, or reach out if you'd like to explore growth strategy, product-market fit, or building a category-defining business.

Tactic #1: Personalize Your Story with the Right Proof for Each Buyer

Problem:

Mid-Market and Enterprise buyers are skeptical of generic logos. Without highly relevant, peer-specific proof points, prospects question whether your solution fits their specific industry and business needs.

Solution:

ServiceTitan created deep segmentation across their customer proof.

Instead of showcasing the same universal case studies to every prospect, they map customer proof to specific segments like industry (e.g., Residential HVAC, Commercial Electrical), company size (mid-market, enterprise), and customer role (Owner, Operations Manager, CFO).

Each segment received customized stories that mirrored their business realities — not just in marketing pages, but across email follow-ups, paid ads, landing pages, and sales decks.

This made every proof point feel personalized and credible, minimizing doubts about whether the solution would work "for a company like mine."

• Where They Show Segmented Case Studies:

  • Marketing Landing Pages: Different market segments (e.g., Residential vs. Commercial, HVAC vs. Plumbing) have their own dedicated customer proof.

  • Email Marketing Campaigns: Prospects receive emails customized to their industry and size.

  • Paid Ads and Retargeting: ABM campaigns show customer testimonials specific to the prospect’s industry.

  • Sales Decks and Demos: AEs highlight case studies from precise peer comparables—size, geography, and vertical.

• Sales Enablement Materials:

  • A curated "reference library" for each segment, so AEs could pair tailored proof immediately after discovery.

ServiceTitan Targeted Marketing Landing Pages Featuring Segment-Specific Solutions

ServiceTitan’s marketing landing pages are tailored to the specific needs of each segment, helping prospective customers quickly discover solutions that match their workflows, challenges, and goals. Source.

Impact:

• ServiceTitan supports over 11,800 customers across 14+ verticals, each with 3+ additional segmentation dimensions. Tailored onboarding, proof, and expansion content drive success at a segment-specific level. Targeted case studies helped improve deal close rates and reduce sales cycle by double digit gains.

Learnings:

• Customize proof points to match the customer's specific industry, size, and use case. Showing a residential contractor proof for a commercial prospect breaks trust instead of building it.

• In late-stage conversations, buyers care less about how big your company is and more about how closely your success stories map to their exact situation.

Tactic #2: Remove Roadblocks Early to Speed Up Sales Cycles

Context:

At ServiceTitan, prospective buyers that are most motivated to purchase are often operational or financial leaders—business owners, operations managers, and controllers—who focus on revenue growth, efficiency, and service quality. Within enterprise-scale businesses with dedicated IT teams, those IT stakeholders responsible for procurement and technical due diligence are not necessarily the first contact point, but are critical in the decision-making process.

If IT and security teams are not engaged early in the sales process it can create deal risk. That is, if governance, compliance, or integration concerns aren't addressed proactively, they can introduce friction, delay deals, or even block deal closure entirely.

Problem:

The duty of IT and security teams at enterprise-scale prospective customers is to assess compliance, data governance, and risk. Without compelling answers and documentation, deals get delayed, procurement cycles drag out, or trust is lost. Even if the business case is strong, a missing GRC story can stop the deal.

Solution:

ServiceTitan identified a strategic opportunity to invest in building enterprise-grade GRC (Governance, Risk, and Compliance) infrastructure that would hold up in any enterprise buying process:

• SOC 2 and ISO certifications

• Role-Based Access Controls (RBAC)

• Detailed audit trails and compliance dashboards

The sales team proactively embedded it into the buying experience:

• Sales decks and demos included dedicated GRC references and citations.

• Competitive positioning featured security and compliance readiness, signaling ServiceTitan’s differentiation from less mature competitors and ability to compete with industry giants such as Salesforce and SAP.

• Marketing pages framed GRC as a strategic advantage, building confidence with procurement and IT leaders from the start.

ServiceTitan Trust Center: Security & Compliance Overview

ServiceTitan demonstrates its commitment to data protection and SaaS best practices through third-party audits and certifications, including ISO 27001, SOC 2, and PCI DSS. The Trust Center offers stakeholders access to detailed security documentation and transparency tools. Source.

By leading with compliance—not waiting for IT to bring it up—ServiceTitan earns internal champions early, reduces deal friction, and makes procurement cycles faster and smoother.

Impact:

• ServiceTitan's investment in compliance infrastructure helped secure contracts with large enterprise clients, landing over 1,000 customers with annualized billings exceeding $100,000, a number which roughly doubled from January 2022 to January 2024. These customers represented over 50% of annualized billings as of January 2024.

Learning:

• In any multi-stakeholder deal, anticipate where objections will come from and proactively address them early. Removing roadblocks before they appear keeps deals moving.

• Start multi-threading early in the sales cycle. Engage not just your primary buyer, but also IT, legal, and operations so they become champions, not blockers.

• Address the must-haves and table stakes early to speed up the sales cycle. By surfacing your GRC readiness, you give buyers confidence and remove surprises before they become issues.

If you’ve gotten this far, you may be ready to navigate to the 🔥 section and check out our Playbook now.

Tactic #3: Deliver ROI and Integration Clarity in the Pitch

Context:

Here’s how the current buying journey worked:

• A user would first submit a lead form

• An SDR (Sales Development Rep) would qualify the lead by asking about the company’s size, industry, and key challenges.

• The qualified lead would then be passed to an AE (Account Executive) who would lead a personalized demo.

• After the demo, the buyer would enter a structured communication sequence—emails, calls, and collateral—designed to reinforce the product’s fit and value.

Problem:

Most demos walk through product features in isolation—without showing how the software fits into the buyer's existing tools and workflows. That leaves prospects wondering how much they’ll need to change, how they’ll see a return on investment (ROI), and whether the value is worth the effort.

At ServiceTitan, this was especially risky because many customers had deeply embedded legacy systems for accounting, dispatch, or CRM. If the integration story wasn't handled early, prospects hesitated or dropped out completely.

But the challenge wasn't just about integration—it was about proof. Buyers needed to see how ServiceTitan would both fit into their existing workflows and deliver measurable ROI. They didn't just want a general-purpose tool—they wanted a tool that seamlessly connected with their current systems and paid for itself.

When buyers can't easily picture the ROI or the product working in their environment, they hesitate, and delay decisions.

This creates unnecessary friction in the buying journey and slows down sales cycles.

Solution:

ServiceTitan shifted early conversations to focus on integration blueprints and value modeling:

• Integration Blueprints:

  • SDRs gather key tech stack info during first discovery.

  • Detailed visual maps navigate how ServiceTitan integrates into the prospect's existing tools (e.g., Sage Intacct for accounting).

  • AEs tailored first-call demos to show the "New World" workflow—what stays, what’s replaced, and how systems sync.

  • In addition to robust integrations, ServiceTitan often replaced multiple legacy systems with a single platform—simplifying workflows, improving data visibility, streamlining vendor management, and reducing operational friction. For many customers, this was a key motivator to embrace change, with ServiceTitan de-risking the migration process via white-glove data services.

• ROI Calculators and Vertical-Specific Demos:

  • Prospective customers see benchmarked examples of KPI improvement such as time savings, margin growth, or cash flow improvement—rooted in realistic, peer-based data.

ServiceTitan’s Integration Partner Marketplace

ServiceTitan’s Partner Marketplace highlights strategic alliances with top-tier providers—like Scorpion, Lennox, Nexstar Network, and Powerhouse Consulting—to deliver expanded value across marketing, procurement, coaching, and digital operations. This gives customers confidence from the get go. Source.

ServiceTitan’s ROI Calculator Intake Page

ServiceTitan’s ROI Calculator offers a fast, personalized estimate of platform impact—enabling the sales team to quantify potential gains for their leads in efficiency, revenue, and profitability in under 30 seconds. Source.

Impact:

• ServiceTitan’s public-facing ROI calculator contributes to accelerating pipeline velocity and helping close high-value accounts faster by surfacing tailored ROI estimates early in the buying journey

• Existing customers were more open to upselling and add-ons because they trusted the platform could expand to solve more of their problems.

Learning:

• Show how your product integrates early in the sales cycle, ideally during the first demo. The earlier you connect to the buyer's systems and workflows, the easier it is for them to visualize success.

• Personalize your demo like you're configuring their new operating system. Use real tech stack references, mirror their workflows, and name the tools they'll be replacing or improving.

• ROI is the most universal metric that helps your customers understand how the cost of your software is not only offset by value delivered, but that the investment can meaningfully multiply returns.

• Make it clear that your solution can replace multiple legacy systems, reducing vendor management complexity and operational friction. This helped customers view your product as a single, all-in-one platform and more open to upselling down the road.

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Tactic #4: Make the ROI obvious Post-Sales, and the Upgrade will sell itself

Problem:

Land-and-expand motions may fail if pilots don't demonstrate clear, early ROI. Without measurable proof that the product improves outcomes, teams struggle to justify expansion to additional departments or locations.

Solution:

At ServiceTitan, every "land" was engineered to create customer-visible ROI from day one:

This meant that from the moment a new account was activated, the platform delivered a self-serve view into the business metrics that mattered most to that customer—technician efficiency, average job value, invoice speed, and revenue per tech among other key metrics.

• Client-Facing ROI Dashboards:

  • Customers tracked live technician job completion times, average ticket sizes, and cash flow improvements.

  • Dashboards embedded inside the product made performance gains visible daily—no waiting for month-end close.

• Gamified Success:

  • Leaderboards ranked technicians by key metrics.

  • Teams naturally competed to drive better outcomes, boosting feature adoption without needing heavy CS involvement.

• Sales Enablement Loop:

  • CSMs used dashboard data to drive upsells: "Imagine if your entire fleet performed like your top 10%."

  • Expansion discussions were framed around proof, not promises.

Real-Time Business Metrics Dashboard in ServiceTitan

ServiceTitan’s real-time user-facing dashboard surfaces critical business metrics—including revenue trends, booking rates, and customer satisfaction—empowering trade contractors to make data-driven decisions without waiting for month-end reports. Source.

Technician Performance Dashboard: Gamifying Excellence with Real-Time Insights

Screenshot of ServiceTitan’s technician performance dashboard showing side-by-side metrics for multiple technicians, including revenue generated, opportunity conversion rates, and customer satisfaction scores. The dashboard enables real-time comparison of technician performance across key metrics. Source

Once a customer consistently saw increases in key metrics like job completion rates or revenue per technician, ServiceTitan's CS and sales teams stepped in to connect those results to specific upsell paths:

• If a company was using only basic tools, they were introduced to more advanced functionality.

• When customers reached usage thresholds the following logic was taken:

  • In mid-market segments, usage thresholds often trigger upgrades into next-tier SKUs with enhanced reporting and automation.

  • In enterprise accounts, the motion focused more on increasing usage through account expansion as well as introducing add-on modules like Marketing Pro, Contact Center Pro, Pricebook Pro, and others—each increasing wallet share and consolidating tech stacks under a single platform.

These upgrade motions were grounded in evidence. Sales didn’t need to pitch hypotheticals—the customer’s own data proved that expanding their use of the platform would drive even stronger ROI.

Impact:

• Low churn (95%+ gross dollar retention)

• Increased wallet share (110%+ net dollar retention)

Learnings:

• Build trust through transparency. By embedding ROI dashboards directly in the product, customers can validate the platform's impact themselves—without needing a CSM to explain it and allowing them to focus on cross-sell and upsell.

• Make the value so clear that it creates demand. When customers see performance gains tied to product usage, they initiate upgrade conversations proactively.

• Use in-product data to trigger upsell playbooks. Clear metrics give Sales and CS the leverage to recommend relevant, higher-tier features based on real behavior—not hypothetical benefit.

Tactic #5: Show your customers how to constantly improve with your product through the Customer Success team

Problem:

Most Customer Success teams focus only on fixing problems. They wait for customers to raise issues instead of helping them get more value out of the product.

Because of that, customers only use a small part of what the product can do. They don’t realize how much more value they could get — and they don’t expand.

Without a plan to guide customers to deeper usage, expansion opportunities are missed.

Solution:

ServiceTitan redefined Customer Success from a support-focused team into a proactive growth driver. Instead of only helping customers solve problems, the CS team focused on showing customers how to unlock more value from the product over time.

The CS team gave customers a clear reason to engage regularly by anchoring conversations around a common language: the TitanAdvisor Score.

• TitanAdvisor: The Customer Health Score

  • Each customer received a live "TitanScore" visible inside their account.

  • TitanAdvisor provides personalized feature recommendations and focused, step-by-step guidance to promote user adoption and engagement.

• Benchmarking Against Peers:

  • TitanScores aren't static—customers can see how they rank against similar businesses.

  • Leaderboards inside accounts make TitanScores competitive and aspirational, driving a desire for end users to increase their score.

• CS Playbook to Increase Scores:

  • During check-ins, CSMs review the TitanScore and offer specific actions such as:

    • Use images to enhance your pricebook, or

    • Use Chat to stay connected with your customers

  • These actions tie directly to the TitanScore. Higher scores in turn correlate to stronger value realization through increased adoption and engagement using best practices, which in turn provide motivation to upgrade to higher packages and adopt add-on products to further enhance value realization.

• Conference Viral Loop:

  • Customers treat their TitanAdvisor Scores like a badge of honor and compare scores with each other at user conferences.

  • High scores spark peer-to-peer conversations about best practices and which features drive the most improvement.

  • These organic discussions motivate customers to adopt more features on their own—without needing direct sales or CSM pressure to do so.

Track Progress with TitanAdvisor Benchmarking

The TitanAdvisor Score offers a dynamic benchmark, allowing customers to track their progress and compare performance against peers—fueling accountability and continuous improvement. Source

TitanAdvisor Actionable Recommendations to Drive ROI

Screenshot of ServiceTitan’s TitanAdvisor dashboard displaying recommended actions to improve platform usage. Recommendations include using images to enhance the price book, enabling online estimates, maximizing reporting, and using chat to stay connected with customers. Each action shows progress status and associated TitanAdvisor point values. Source.

By centering the customer journey around the TitanAdvisor Score, ServiceTitan created a natural funnel:

• Customers became aware of their opportunities by seeing how they ranked against peers.

• They engaged with CSMs to improve their score through adopting more features and engaging more deeply.

• As they adopted more, they naturally expanded into higher packages, higher attach rates of add-on products, and deeper platform usage.

It became a roadmap for customers to continuously upgrade their operations and their investment in ServiceTitan, without needing aggressive sales or CSM pushes.

Impact:

ServiceTitan data shows that, in general, companies with higher TitanAdvisor scores are correlated with higher performance such as growing revenues at a faster rate as compared to cohort peers with lower scores. This leads to natural expansion and higher net dollar retention through organic platform usage growth.

Learnings:

• Create a tangible, social success metric your customers want to optimize.

• Benchmark success against peers to motivate natural expansion conversations.

• Tie upsells to customer’s own goals, framed through benchmarks and clear next steps.

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🔥 Insider Growth Group Playbook: How to Build a Repeatable Expansion Engine

Most companies land a pilot. Few turn it into a machine.

Here’s how to operationalize Land → Expand → Repeat.

Stage 1: Set Up Expansion From the Start (Pre-Sale + Early Pilot)

1. Define Expansion KPIs Early
   Identify 2–3 value metrics (like revenue growth per user, accelerated cash flow speed, increased customer satisfaction) that expansion will hinge on—and embed them into your early conversations.

2. Segment Your Ideal Expansion Targets
   Classify prospects by vertical, company size, and buying persona. Expansion paths are easier when your pilot is a perfect fit.

3. Embed Early Outcome Visibility
   Make real-time success metrics (ROI dashboards, user productivity, etc.) visible inside the product from Day 1, not locked inside point-in-time reviews.

4. Create a Clear "New World" Blueprint
   Show how your product integrates and fits within the customer’s tech stack with visual blueprints during the discovery phase.

5. Identify Your Expansion Champion
   Early in the pilot, align to a user/manager who will care deeply about broader rollout success, not just pilot success.

Stage 2: Build Compounding Customer Motivation (Post-Land)

6. Launch Customer Health Scores Publicly
   Expose how customers are progressing against platform adoption and engagement benchmarks inside the app (e.g., TitanAdvisor).

7. Tie Health Scores to Actionable Playbooks
   Make it dead simple for customers to know "how to increase their score" via feature adoption checklists, preferably in-app as with ServiceTitan.

8. Create Peer Benchmark Leaderboards
   Show companies how they rank vs. similar businesses in their peer cohort to tap into healthy competitive instincts and promote deeper usage.

9. Segmented Proof Everywhere
   Equip Sales and CS with vertical-specific case studies, reference customers, and marketing materials for each major expansion persona.

10. Use Customer Success Reviews to Surface Expansion Readiness
    Base Customer Success reviews on success metric performance and TitanScore-style data—not just renewal status—to open upsell conversations naturally.

Stage 3: Remove Friction and Win Company-Wide

11. Standardize Expansion Playbooks Across Teams
    Create reusable scripts, decks, and training so CS and Sales provide consistent expansion messaging.

12. Invest in GRC and Procurement Readiness
    Build compliance one-pagers, readiness documents, and landing pages proactively—don't wait for IT/legal to ask.

13. Treat Each Department Like a New Customer
    Every expansion motion (new location, business unit, project) should have its own mini-discovery, onboarding, and success plan.

14. Score Expansion Timing Based on Engagement Data
    Monitor internal engagement signals (feature usage, dashboard check-ins, team login rates, seasonal trends) to predict when expansion windows are open.

15. Operationalize Cross-Sell and Upsell Campaigns
    Launch specific, time-based campaigns targeting already-successful accounts with relevant add-ons, integrations, or premium offerings.

Stage 4: Scale Expansion Across an Industry

16. Launch Industry-Specific Training and Onboarding
    Create training modules, webinars, and onboarding guides tailored to each industry. For example, a CSR sees a customized workflow for using dispatch tools, while a field technician learns how to use mobile invoicing. Personalized training leads to faster adoption and deeper product usage.

17. Create a Vertical-Focused Sales and CS Team
    Assign dedicated Sales and Customer Success reps to each industry vertical. These team members become experts in the specific pain points, use cases, and language of their assigned vertical. This leads to faster deal cycles and more effective upsell conversations.

18. Develop Vertical-Specific Success Metrics and ROI Calculators
    Customize ROI calculators to reflect industry-specific metrics. For HVAC, this could mean time saved per job and revenue per technician. For plumbing, it might focus on invoice speed and payment collection rates. Make these calculators public-facing to accelerate lead generation.

19. Showcase Industry Peer Success Stories as Social Proof
    Create case studies, customer testimonials, and video success stories specific to each industry. Showcase these on your website, in email campaigns, and in sales decks. Use these stories to drive both initial sales and expansion.

20. Build an Industry-Focused Expansion Playbook

    Document the most successful configurations, best practices, and high-ROI workflows for each industry. Share this playbook with new customers to help them adopt proven strategies faster, driving higher retention and expansion.

Want help on any product and revenue growth challenges you have?

Let’s talk

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👋 Hi, it’s Gaurav and Kunal, and welcome to the Insider Growth Group newsletter, our bi-weekly deep dive into the hidden playbooks behind tech’s fastest-growing companies.

Our mission is simple: We help you create a roadmap that boosts your key metrics, whether you're launching a product from scratch or scaling an existing one.

What We Stand For

• Actionable Insights: Our content is a no-fluff, practical blueprint you can implement today, featuring real-world examples of what works—and what doesn’t.

• Vetted Expertise: We rely on insights from seasoned professionals who truly understand what it takes to scale a business.

• Community Learning: Join our network of builders, sharers, and doers to exchange experiences, compare growth tactics, and level up together.

Introduction:

More companies are realizing that to truly shift deeply ingrained behaviors—it requires deep coordination with the operations side of the house.

This expanded collaboration introduces new complexities: more internal stakeholders to align, more processes to streamline, and new challenges in figuring out how an online product can effectively support people working on the ground.

When Engineering, Product, and Design (EPD) teams integrate tightly with Operations, they can deliver end-to-end improvements that actually stick in the real world—driving higher adoption, better retention, and more sustainable growth.

It’s not enough for a PM to just ship digital features anymore. Product and ops used to be siloed—product owned the build, ops owned the rollout and post-launch. But with the rise of large-scale, complex, multi-market products, those boundaries are blurring.

Product teams must work closely with customer success, product operations, and on-the-ground users to ensure that every new feature or process improvement truly addresses real-world needs. This partnership becomes a key part of the Go-To-Market of any product launch.

Today we’ll explore how Uber brought together its EPD teams and operations groups—including drivers, bikers, product operations, and communication operations—to lower the defect rate for Uber Eats.

We’ll dive into three strategic bets that Roy took while at Uber and how he partnered with the Ops team. Without their seamless collaboration, these initiatives wouldn’t have been nearly as successful—perhaps not successful at all.

What exactly is the defect rate for Uber Eats?

Defect Rate is the percentage of orders that run into issues. For Uber Eats, this had a ~7% baseline—meaning 7 out of every 100 orders had some form of problem.

These issues came from two main buckets:

1/ User-Reported Issues

• Missing or Wrong Items: Orders with missing items (e.g., that soup was missing) or the entire order wrong (driver delivered wrong bag)

• Food Taste or Quality: Spilled soup, a cold burger, or fries that taste stale.

• Late Delivery: Couriers who run behind schedule due to high traffic or other delays that lead to a cancellation.

• Never Received Order: Eaters who never get their food (courier mix-ups, incorrect drop-offs, etc.).

2/ Non-User-Reported Issues

• Driver Courier Shortage: The order is placed but can’t secure a driver or biker, leading to cancellations.

• User Didn’t Intervene: Sometimes, an order is canceled or refunded before the eater notices because of internal checks or system flags.

Why does the defect rate matter?

If an eater experiences a defect, they become 10 times more likely to never order again.

> If Uber Eats could improve this metric by 1 basis point this would lead to millions in revenue.

Introducing Roy Frenkiel:

Roy Frenkiel is a seasoned product leader known for turning complex challenges into scalable solutions. As a Director of Product at Uber Eats, Roy owned all restaurant-related technology (onboarding, pricing and packaging, the order tablet, APIs, back-office tooling, payments, and more), part of the consumer app experiences (store page, checkout, fulfillment), and cross-functional efforts (defect reduction). Prior to Uber, he held product leadership roles at Houzz, SoFi, and Medallia, as well as a McKinsey consultant.

Case Study #1: Time-of-Day Menu Adjustments

Context:

Uber Eats noticed that certain restaurant items—particularly those requiring more complex preparation (e.g., McFlurries, fish entrees, soup dumplings)—were more prone to errors during specific shifts. The team identified that high defect rates are correlated to the time of the day the order came in, and the item itself.

The late-night crews faced higher defect rates because of reduced staffing, which created issues during spikes. In addition, certain items faced higher defect rates. The intersection of the two sometimes created dramatic spikes in defects.

Problem:

• High Defect Rate on Complex Items: Items like McFlurries and fish entrees had disproportionately high error rates at night.

• Operational Strain: Night-shift crews often lacked the support or processes to produce these items consistently, leading to mistakes in preparation and packaging.

Current Solution:

Every item stayed on the menu all day—from restaurant open to close—with no adjustments for time of day or complexity.

New Solution:

To reduce late-night defects, the team introduced time-of-day menu adjustments, removing or hiding complex items during high-risk periods.

They tested this in two phases:

1. Phase 1: Manual Item Selection

   • Ops-Driven: Operations teams looked at defect data and identified troublesome items (e.g., McFlurries, certain fish fillets, soup dumplings).

   • Restricted Availability: These items were removed from late-night menus to minimize errors.

   • Quick Validation: By limiting the number of advanced items at night, Uber Eats quickly tested whether removing them would lower defect rates.
     

2. Phase 2: Model-Driven Automation (Scalable approach which is still in progress)

   • Predictive Modeling: An ML algorithm identified which items had the highest probability of defects during certain times.

   • Real-Time Updates: Menus were dynamically adjusted based on shifts in historical data, staffing levels, and ongoing performance metrics.

   • Scalability: This system allowed for thousands of restaurants to be managed without manual intervention, continually refining itself as it gathered new data.

Roles and Collaboration:

Role Product Played in Phase 1:

• Developed and deployed the technical solution to quickly toggle item availability in the app.

• Adjusted the ordering system so that menu changes could be implemented in real time.

• Coordinated with Ops to ensure that the manual selections could be smoothly integrated within the existing platform.

Role Ops Played in Phase 1:

• Analyzed defect data to identify high-risk items (e.g., McFlurries, certain fish fillets, soup dumplings) that were problematic during late-night shifts.

• Manually selected and communicated which items should be removed from the night-time menus.

• Worked directly with restaurants to give them a heads-up and create targeted training on the new process, ensuring a smooth transition.

Leverage that the Product<>Ops Relationship Provided:

• Rapid testing of the hypothesis

• Battle-tested Ops by ensuring that restaurants were prepared and supported, setting the stage for a significant reduction in defect rates.

Impact:

20 bps Reduction in Defect Rate: Removing high-risk items during night shifts significantly lowered total defects.

Reducing the number of items on the menu led to a small drop in basket size and conversion rates, but the substantial decrease in defect rates more than made up for that loss.

Learnings:

• Pilot ideas with the Ops team before investing EPD resources: Leveraging Operations teams to validate the concept before investing in an automated, scalable solution can be a toolkit for a PM.

• Context is Everything: The same menu item can perform flawlessly at noon but fail at midnight. Tailoring product availability to operational realities—like staffing or traffic patterns—helps you move metrics.

• Defining Data Definitions Upfront: Agreeing on what “complicated” means at the beginning of a project prevents confusion and misalignment across teams. Additionally, relying solely on human judgment from operations can introduce selection bias. Establishing a standardized, randomized decision framework ensures it’s consistency throughout the project.

If you’ve gotten this far, you may be ready to navigate to the 🔥 section and check out our Playbook now.

Case Study #2: Optimizing Multi-Location Restaurant Selection

Context:

Uber Eats aims to provide users with the best combination of availability, price, and speed. In dense urban areas, a single restaurant brand (e.g., McDonald’s, Starbucks, Chipotle, etc.) may operate multiple locations within short distances. The Uber Eats app would always default to the closest restaurant within that chain to the user, minimizing delivery time.

Problem:

• Uneven Quality Across Locations: Not all locations within a restaurant chain are the same—sometimes the closest one may have higher defect rates or slower service. Previously, Uber focused solely on proximity, but to truly improve the customer experience, orders should come from the best-performing location rather than just the nearest one.

• Overemphasis on Distance: By focusing on proximity alone, the system ignored the quality metrics that could significantly reduce defect rates.

Current Solution:

Default to Nearest Location: Because all locations in a given area share similar menus and pricing, Uber Eats prioritized minimal travel distance to get it to the end user as fast as possible.

New Solution:

The team introduced quality-based store selection, factoring in both distance and historical defect data to guide users to a more reliable location—even if it wasn’t the absolute closest.

Algorithmic Changes

• The app evaluated each potential store on delivery time and quality metrics (e.g., missing item rate).

• A small, additional travel time could be justified if the store offered a significantly lower defect probability.

Power of the Default

• For the eater, the restaurant with the best composite score (distance + reliability) became the default choice.

• Users could still opt for a different location, but most stuck with the default, naturally improving overall defect rates.

Operations:

• Partnered with product operations to gather defect data, pinpoint the root causes behind higher rates, and then share these insights with HQ—enabling them to revise processes for underperforming restaurant chains.

How the algorithm changed before vs. after:

Roles and Collaboration:

Role Product Played:

• Developed and integrated an algorithm that combined distance with quality metrics to default orders to the best-performing location.

• Updated the user interface to highlight the optimal location while still allowing users to select alternate options.

Role Ops Played:

• Collected and analyzed defect data from various locations and provided real-world insights on local performance differences.

• Standardized quality metrics and communicated key operational factors to headquarters for process improvements.

Leverage that the Product<>Ops Relationship Provided:

• This real-time integration not only optimized the ordering process and reduced defect rates, boosting revenue for Uber, but also empowered HQ to address systemic issues at underperforming locations by tackling the root causes of recurring problems.

• Ops standardized and validated quality metrics by analyzing real-world performance. This enabled the product team to fine-tune the composite scoring system so that it balanced both proximity and reliability, ensuring that orders were routed to the best-performing branch.

Impact:

5 bps Reduction in Defect Rate: By nudging users toward more reliable locations, missing-item complaints and delivery errors decreased.

Optimizing store selection based on quality resulted in a slight increase in cost per trip and a minor uptick in late arrivals but the significant reduction in overall defect rates outweighed the increase in costs.

Learnings:

• Power of the Default: Changing the pre-selected location drove a measurable reduction in defects, underscoring how defaults significantly influence user behavior.

• Leaning on Operations Teams for Early Research: By going beyond raw data, operations teams can share nuanced, local insights—such as changes in management or on-site training improvements—that help pinpoint the true causes of higher defect rates.

Case Study #3: Improving Visibility and Communication for “BYOC” Restaurants

Context:

In large parts of Europe, many restaurants use their own delivery couriers (like Domino’s in the US), to deliver food, which they use for Uber Eats orders as well. This is referred to as “Bring Your Own Courier” (BYOC). Under this model, the restaurant prints out order delivery details, including the eater’s address, and its in-house courier then manually inputs the address into a separate navigation tool (e.g., Google Maps). These couriers aren’t integrated into the Uber system, so Uber can’t track their location or facilitate direct communication with customers.

Problem:

BYOC orders had 2x higher “Never Received Order” defects than couriers using the Uber app. Because these external couriers operated “offline,” neither Uber nor the eater knew where the courier was in real time. Couriers also couldn’t directly message eaters to confirm delivery details, or enjoy the guidance from Uber such as on building entry points

Current Solution:

When an order came in, the workflow was fully offline:

1. Order Print-Out: The restaurant’s in-house system printed an Uber Eats order ticket with the customer’s details and address.

2. Manual Address Entry: The courier read the printout and typed the address into a separate mapping service (e.g., Google Maps).

3. Order Drop-off: Courier would drive to the user’s address and drop it off.

New Solution:

To address these pain points, Uber introduced a QR code that integrated external couriers into the Uber platform alongside operations incentives to encourage adoption.

1. QR Code Integration

   • Each order receipt printed by the restaurant would have a unique QR code.

   • Scanning it launched the Uber driver app with the address pre-loaded.

   • Couriers gained real-time tracking and could communicate with customers directly.

2. Operational Incentives

   • Marketplace Fee Discounts: Restaurants that adopted QR scanning were rewarded with lower fees, offsetting any perceived hassle of changing their courier workflows.

Jobs to be Done:

Roles and Collaboration:

Role Product Played:

• Developed and integrated a QR code system that printed unique codes on order receipts, allowing couriers to automatically launch the Uber driver app with preloaded delivery details.

• Built real-time tracking and in-app messaging features to bring offline BYOC operations into the Uber ecosystem, streamlining route navigation and issue resolution.

Role Ops Played:

• Worked directly with restaurants to roll out the QR code solution, providing training and support to ensure their in-house couriers adopted the new workflow.

• Implemented change management initiatives, including operational incentives such as marketplace fee discounts, to drive adoption and boost confidence in the new process.

Leverage that the Product<>Ops Relationship Provided:

• The product team’s technical solution required Ops’ on-the-ground insights to tailor the QR system for real-world BYOC challenges (i.e printer compatibility) —ensuring that external couriers could transition smoothly into the digital workflow.

• Ops’ proactive engagement with restaurants and couriers turned a low initial adoption (1.5%) into a successful rollout, by creating clear, tangible incentives and providing hands-on training.

Impact:

• 35 Basis Point Defect Reduction in BYOC merchant adopting the solution: With more couriers scanning and using the Uber app, defect rates dropped significantly for these merchants.

Learnings:

• Operational Incentives are Vital: The initial release had a very low adoption rate of 1.5% but combining it with incentives that the on the ground operations teams rolled out it created a tangible motivation for restaurants to train couriers on the new workflow.

• Bridging Offline and Online: Incorporating more of your users’ offline activities into your product benefits both the company and the users themselves, creating a seamlessly connected workflow.

🔥 Insider Growth Playbook: Leverage the Product-Ops Relationship to increase your Go-To-Market Velocity and Confidence

When a feature release involves multiple operational steps or offline processes, it is essential to partner with your Ops team. If your feature is part of a multi-step process spanning various operational functions, collaborating with Ops is critical to determine its true impact and resolve potential issues.

Step 1: Evaluate Your Process Complexity

• Identify areas where your feature relies on coordination among multiple operations teams or involves offline processes.

• Recognize that these components require Ops involvement to ensure smooth execution and accurate measurement of outcomes.

Step 2: Understand the Impact of Not Partnering with Ops

• Without Ops collaboration, you risk missing key insights and may not be able to gauge whether your feature delivers the intended impact.

• Lack of operational input can leave root issues unaddressed, negatively affecting overall performance and revenue.

Step 3: Extended User Research & Validation

• Partner with Ops to conduct comprehensive research with internal operator personas and front-line teams.

• Use their on-the-ground insights to understand the full scope of the process, including manual data entry, physical workflows, and inter-department handoffs.

Step 4: Run Pilots Led by Ops

• Implement targeted pilots managed by Ops to test how the feature performs in real operational conditions.

• Use these pilot tests to refine the solution before scaling up, ensuring the process is validated and optimized.

Step 5: Alpha and Beta Tests During Release

• Combine technical tracking from the product side with operational feedback during controlled alpha and beta tests.

• This dual approach ensures that any issues are identified early and adjustments can be made in real time.

Step 6: Post-Launch Analysis & Continuous Feedback

• Conduct a thorough post-launch review in partnership with Ops, analyzing how the feature performed against operational metrics.

• Establish a continuous feedback loop to integrate Ops insights into ongoing improvements, addressing root causes and enhancing overall outcomes.

How Each Function Leads & Owns Their Part:

• Ops Responsibility:

  • Leverage their deep understanding of internal operator personas and real-world processes to identify friction points and standardize workflows.

  • Provide critical, actionable feedback on on-the-ground operational challenges.

• Product Responsibility:

  • Use Ops insights to define feature requirements and balance enhancements across the user journey with technical feasibility.

  • Make data-driven decisions that ensure the solution is robust, seamlessly integrating operational needs.

By following this playbook, you not only improve the impact of your feature but also bridge the offline-online gap, solving root issues and driving significant revenue growth.

Want help on any product growth challenges you have?

Let’s talk

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This was before Apple had Find My Friends, before Snap Maps took off, and before Zenly built a following. Life360 had the early mover advantage in location sharing, and founder Chris Hulls was one of the first to prioritize app store optimization (ASO). His early ASO strategy helped Life360 dominate app store rankings and outlast a wave of copycat competitors—buying the company time to focus on differentiation.

But a decade in, they faced a problem: install growth alone wasn’t going to drive long-term revenue. People loved knowing where their friends or family were, but that didn’t translate into a strong willingness to pay. Casual users didn’t see the need to upgrade.

What unlocked the next wave of growth was a shift in how they defined their core user. Life360 realized that their most loyal and valuable users weren’t casual friend groups—they were families, specifically parents trying to keep their kids safe. Unlike open social platforms that thrive on large, public social graphs, Life360 was always more intimate—centered around small, trusted family units. That intimacy made it harder to follow traditional social network monetization models like ads or viral growth. But it also opened the door to something more powerful: a membership model grounded in real-world services.

By narrowing their focus to these high-intent users, they were able to design features, messaging, and pricing that aligned with a very specific set of needs—and ultimately build a product that parents trusted enough to pay for.

These changes allowed Life360 to go public on the Australian Stock Exchange (‘360’) in 2019, and then on Nasdaq (‘LIF’) in 2024. You can see the growth in market cap below.

This shift in mindset catalyzed a rethinking of their product and go-to-market strategy. Life360 evolved from a simple location-sharing tool to a safety service, focused on protecting families across driving, home, and online.

Here's what changed:

• Their ICP (Ideal Customer Profile) : No longer "anyone who wants to share location" but parents with kids of all ages, especially teenage drivers and toddlers.

• Their use case: From "Where is my kid?" to "Reassurance that my child is okay." The emotional trigger shifted from simple curiosity to a deeper need for peace of mind and confidence in their child's safety.

• Their monetization model: They introduced a Good/Better/Best membership structure, ranging from $8 to $25 per month. Higher tiers bundled real-world services like emergency dispatch, stolen phone protection, and family safety support.

• Their benchmark: They stopped comparing themselves to Zenly or Snap Maps and started positioning their service as a digital replacement for home security systems like ADT or AAA memberships.

Let’s break down how they went after a new ICP—through four tactical plays around pricing, packaging, and personalization that increased ARPU while deepening product-market fit.

Introduction:

Introducing PJ Loury, who led Safety Products at Life360. Before that, he worked on product at Uber and Jawbone. At Life360, PJ played a key role in transforming the app from a location-sharing tool into a full family safety platform.

Tactic #1: How a Good/Better/Best Model Helped Life360 Match Different Family Needs

Problem:

Life360 initially offered two membership options: a freemium plan and a single paid upgrade called Driver Protect. But this setup created an 'all-or-nothing' dynamic—users either had access to only basic features or had to upgrade straight into the full suite of premium protections. There was no middle step. For families who wanted some added peace of mind without committing to the highest plan, this left a gap.

Without a flexible set of protection tiers, Life360 was inadvertently pushing users away by making it feel like the only way to get real safety value was to go all in.

As a result, their average revenue per user remained flat despite growing adoption.

The challenge was how they could increase ARPU while still appealing to a broad audience of families who had differing needs when it comes to protection.

Solution:

Life360 launched a new three-tier membership structure—Silver, Gold, and Platinum—to better match the needs of different types of families. The goal was to create clear, emotionally resonant levels of protection that appealed to different personas.

At the top, they introduced the Platinum Plan ($25/month). This plan was intentionally built for high willingness-to-pay (WTP) users, offering the most comprehensive suite of real-world services. But strategically, it also served as a pricing anchor to make the Gold Plan ($15/month) feel like the smartest, most balanced choice.

The Platinum Plan bundled together high-perceived-value features that delivered peace of mind without requiring heavy engineering investment. Many were offline services supported by partnerships or operational teams:

• Identity Theft Protection: Monitors if personal information or passwords have been exposed publicly and offers restoration services if identity theft occurs.

• Travelers Hotline: A medical advice line staffed by travel nurses to support families navigating prescriptions, diseases, or care in international destinations.

• Disaster Response: 24/7 emergency response through ambulance dispatch for car accidents and police dispatch for personal safety incidents.

• Roadside Assistance: A service that connects families with emergency help—like towing, jump-starts, or flat tire support.

The Gold Plan layered on top of Silver by adding mid-cost, high-impact services like crash detection dispatch—features that mattered deeply to the mass market. This tier became the most popular upgrade path because it struck the right balance between price and perceived safety value.

By building for emotional resonance across multiple safety tiers, Life360 created a structure that served everyone—from cautious multi kids households to families with a toddler.

Impact:

+20% increase in ARPU through better tier positioning and packaging.

Key Learnings:

• You don’t need everyone to choose your highest-priced plan. It's okay if most users land on the middle tier—as long as it feels like a great deal. The top tier's job is to anchor value and make the mid-tier feel like the smart, balanced choice for peace of mind and affordability.

• Don’t go straight to dev resources—leverage marketing or operations to build human-powered workflows that simulate premium features quickly and cost-effectively.

• Building a higher-priced, high-WTP tier can help elevate the perceived value of your overall product, even if most users don’t adopt it.

If you’ve gotten this far, you may be ready to check out our Playbook 🔥 now.

Jump to the Playbook

Tactic #2: Redesign the Free Package

Problem:

Crash detection, a feature many parents expected to be included by default, was initially locked behind a paywall.

The feature itself was actually made up of two distinct parts:

1. Detection – monitoring driving behavior in real-time and identifying potential crashes.

2. Alerting – notifying family members and initiating emergency workflows like contacting roadside assistance or dispatching help.

Parents would install the app expecting a baseline level of safety, only to realize they couldn't access any part of crash-related services without paying. In the data, they noticed many users uninstalled the app on Day 0. When the team dug into churn survey feedback, they uncovered a consistent theme: Parents came to Life360 in a moment of heightened concern, often after hearing about teen driving risks or local accidents. The model required Life360 to nurture more of these users prior to asking them to convert.

Solution:

Life360 made the decision to move the crash detection component into the free tier.

To guide this decision, they leaned on an internal framework that considered:

1. Cost to Serve – Was the feature expensive to deliver at scale? In this case, the detection layer was software-based with minimal cost to the business.

2. Perception: Downgrading from safety feels emotionally wrong—no parent wants to tell their child they're "less protected." Very little risk of losing out revenue from an escalated amount of downgrades.

3. Growth Loop: Offering this high-value feature for free created a powerful onboarding hook for new user acquisition.

The real-world response services like ambulance dispatch and tow trucks that had a high cost to serve remained paywalled, allowing for natural upsells.

Here’s how Life360 split the feature into paid vs. free:

To help convert free users who came in for crash detection, Life360 used a combination of emotional triggers and smart timing. They added contextual nudges like: This drive is not protected. These messages were timed to appear exactly when a parent was checking in on their child mid-drive—a moment of elevated concern and attention. By prompting users with the right message at the right time, Life360 dramatically increased urgency and conversion. The copy emphasized peace of mind and positioned the upgrade as a protective action, aligning perfectly with the emotional reason parents downloaded the app in the first place.

Results:

• 15% increase in new installs from word of mouth and rave around this new feature

• 10% increase in monthly upgrades

Key Learnings:

• Giving away just the right part of a feature—like crash detection—can drive top-of-funnel growth, especially when it's tied to a clear upgrade path.

• Upgrade prompts are most effective when they appear at emotionally charged moments—like checking in during a live drive—when concern is already high and parents are most motivated to act. Timing the message with the user’s real-time intent dramatically improves conversion.

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Tactic #3: Change Who You Compare Yourself To

Problem:

Both users and internal teams saw Life360 as a mobile app. The natural comparisons were to Snap Maps or Find My Friends. But those comparisons capped perceived value and made it hard to justify a $15/month or $25/month plan.

Solution:

Life360 realized that continuing to position themselves alongside other location-sharing apps was limiting their growth. Instead, they redefined their category. They weren’t just a mobile app—they were a digital replacement for traditional home safety services like ADT for home security, AAA for roadside help, and insurance products covering phone theft and identity protection.

They leaned into this by introducing "Home Safety Services" into their top-tier plans and rolling out new messaging focused on ROI—showing families how bundling these services through Life360 could save money while offering round-the-clock protection. The membership started to look and feel more like a Costco-style bundle: comprehensive, cost-effective, and built for families, not individuals.

Impact:

+10% ARPU increase by reframing value and anchoring pricing to higher-ticket services.

Key Learnings:

• How users perceive your product directly affects how much they'll pay for it.

• Anchor your pricing and positioning to what your customer would otherwise spend on comparable real-world services. Don't limit your comparisons to other apps—your real competition might be a home security contract, a AAA membership, or an insurance premium.

Tactic #4: Go After Your User’s Users and Personalize your Packaging

Problem:

Many of the families coming to their site had multiple kids spanning different age groups, but Life360 had primarily built features for teen drivers. That segment was already retaining and converting well. Meanwhile, parents of younger children—who were just as motivated by safety concerns—had limited tools available to them. The team recognized they were leaving money on the table by not serving the full family unit.

Solution:

They launched Jiobit, a hardware GPS tracker designed for younger kids without smartphones. Many Life360 families had multiple children, but the product was only built for the older kids. This move allowed Life360 to reach parents earlier—starting the relationship when kids were still in preschool or elementary school.

By bundling Jiobit (hardware) with the Life360 platform (software), they created a new entry point into the ecosystem and added a new revenue stream that didn't rely solely on app subscriptions. It also helped deepen retention by keeping families engaged over a longer life cycle.

This is an example of a vertical pricing strategy—a way to grow revenue within your existing market by creating new SKUs or products that appeal to specific, underserved segments.

Life360 saw that families weren’t monolithic—parents with toddlers had different needs than parents with teens. So rather than trying to force one solution to fit both, they introduced differentiated products tailored to those stages.

Results:

• 15% increase in ARPU from hardware/software bundling

• Extended LTV by 10% engaging families earlier

Key Learnings:

• Vertical pricing lets you go deeper in the same market without disrupting your existing base. It allows you to serve different user personas without cannibalizing your core product. For example, Uber initially offered only UberX, but when they launched UberPool, they attracted a new, more price-sensitive audience. Not only did they grow the user base, but they also increased rides per existing user by giving them more situational options—making the service more versatile and habitual.

• Your user’s user matters: In Life360’s case, the kids are the ones being tracked, but the parent is the one making the purchase. By building features that solve problems for the child—like Jiobit for toddlers—they created more reasons for the parent to pay. Understanding this dynamic helped them design more relevant, high-converting offerings.

• Offer solutions that grow with your user: Families evolve. A product that can serve a parent when their child is in grade school and still serve them when that child becomes a driver at sixteen becomes even more valuable.

🔥 Insider Growth Group’s Step-by-Step Guide to Identify and go after your ICP audience:

Going after a new Ideal Customer Profile (ICP) isn’t just about identifying a new user segment—it’s about a coordinated play across product, marketing, and sales.

Here's our detailed framework for identifying and winning over your next best customer.

Step 1: Analyze Your Current User Base

• Segment users by engagement, retention, and conversion using analytics or an LLM.

• Identify groups with strong feature usage, coming through direct / organic channels, or high NPS who aren’t converting. These might be high-intent users underserved by your current offering.

Step 2: Qualitative Deep-Dive

• Talk to customers in these segments to understand their context, needs, and pain points.

• Review sales and support conversations for patterns that might explain drop-off or high intent.

Step 3: Expand Onboarding and Attribute Data

• Add targeted questions to onboarding flows to confirm hypotheses—like user role, intent, team size, or industry, and so on.

• Combine this with product analytics and survey feedback to map retention and conversion rates to specific sub-segments.

Step 4: Validate With Metrics

• Cross-reference onboarding data with downstream metrics—retention, activation, expansion.

• Confirm if this segment converts at lower rates due to misalignment or untapped need.

Step 5: Build a Smart Account List

• Use tools like LinkedIn Sales Navigator, Clay, Apollo, Keyplay, or ZoomInfo to identify accounts and contacts that fit your refined ICP.

• Enrich with funding signals, tech stack, roles, or recent hiring activity to increase intent relevance.

Step 6: Create a New Offering and Observe Conversion Rate

• Design bundles or solutions that solve this ICP’s immediate friction. Think small add-ons, new tiers, or usage-based pricing to see if you can improve conversion rates.

• Check out our pricing and packaging deep-dive here.

• Take Life360: launch features like Jiobit for parents with younger kids.

Step 7: Launch Targeted Campaigns

• Combine email, paid media, and LinkedIn outreach for coordinated exposure.

• Use micro-campaigns targeting real-world events (like back-to-school, driver’s ed season) to add urgency.

Step 8: Build ICP-Focused Landing Pages

• Create dedicated pages for your new ICP with tailored messaging, case studies, and social proof.

• Use data to personalize based on company type, buyer role, or trigger (ex: visited pricing page).

Step 9: Invite to Engage and Self-Educate

• Offer demos, free trials, or gated content that builds trust with your ICP.

• Run intent-based retargeting using tools like Metadata or 6sense.

Step 10: Track, Learn, and Scale

• Monitor behavior post-campaign: Are they converting? Retaining?

• Document learnings in an internal ICP playbook that includes key pain points, feature preferences, and messaging wins.

Want help identifying or converting your next ICP or want to talk more about Pricing or Packaging?

Let’s talk.

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But there’s been far less discussion on how AI is being used to drive real business impact: increasing revenue, improving retention, and optimizing in-product experiences. This is the type of AI investment that Insider Growth Group wants to focus on as growth levers to apply within your own organization.

Today we will explore how three companies leveraged AI to refine their user segmentation, create new pricing strategies, and unlock a new growth lever to increase revenue.

Case Study #1: How a Well-Known Transportation App Used AI to Increase Revenue and Retention

Context:

This transportation app serves over 2 billion users worldwide, offering commuters and travelers real-time navigation and transit insights.

The core use case of this app is helping users get from point A to point B efficiently using public transportation, ride-sharing, or other mobility options.

One of its key monetization levers is a premium subscription tier that provides users with an ad-free experience and features designed to improve their transit journeys. These features include Safe Ride, Live Location, Compare Routes, Arrival Updates, and Live View, all aimed at offering an elevated riding experience.

Problem:

The transportation app had hit a revenue plateau—growth from traditional upsell strategies had maxed out, and they needed a new way to drive conversions to their premium tier.

Current Solution:

Before leveraging AI, the company took a one size fits all approach to upselling:

• Every user saw a personalized upsell based on the entry point they came in from (i.e the user was interested in live location they would see an upsell talking about the benefits of live location)

• The upselling experience was based on intent in the moment

New Solution:

Their current upsell framework had a few problems:

• One-Size-Fits-All Approach: The upsell strategy was generalized and applied uniformly across all users, without considering the diverse needs and usage patterns of different personas (e.g., commuters vs. occasional users), resulting in missed opportunities for personalized engagement.

• Lack of Timing Precision: Upsell prompts were not strategically timed based on user behavior or activity, meaning users might have been presented with offers when they were less likely to convert, reducing overall effectiveness.

• Manual and Resource-Intensive Process: The upsell tactics relied on manual segmentation based on static data and engineering driven roll-outs which required slower roll-outs.

Each of these users has different needs and behaviors, which requires tailored engagement strategies.

A tailored engagement strategy means personalizing each user’s experience based on who they are, how they use the app, and identifying the right moment to reach out. For example, a frequent commuter might need a reminder about real-time delays or alternative routes when they're on their way to work.

Step 1: Auto-Segmentation using AI

Using Coho AI, the app automatically segmented users based on who they are, feature adoption, and usage patterns. Through this AI-driven process, over 100 different user segments were identified.

Narrowed it down to a few key personas based on the proportion of users that fell into the top segments.

Key personas included:

• Worker: A user who primarily uses the app for commuting to work.

• Weekend User: A user who primarily uses the app for weekend travel.

• Night User: A user who predominantly uses the app in the evening.

• Sporadic User: A user with no distinct pattern of usage.

The transportation app had previously identified Worker and Sporadic users, but Night Users and Weekend Users were new personas for them. These new personas emerged because the company had primarily relied on static onboarding data to create user segments, rather than incorporating recent usage and feature adoption data.

Step 2: Determining the Next Best Action

Once users were segmented, the next step was identifying the right action to take for each group to drive conversions.

The transportation app was able to create 16 distinct strategies for engaging different user types at the right time.

A prime example was offering Night Users a trial of the “Live Location” feature when they were using the app at night. This personalized approach enabled the company to match the right feature to the right user at the right moment.

Here are a few example strategies that were generated with AI:

Step 3: Execution and Communication

With Coho AI integrated into the company’s communication platform, personalized messages were delivered automatically and in real-time. This meant that the right action could be taken for each user, based on their behavior and needs.

Introducing a new communication feature like a pop-up, modal, or chat is a time-consuming process. It requires alignment across multiple teams—product, engineering, design, and marketing—along with significant development costs but in this case, the process was instantaneous.

New User Flow:

The Impact

• User Conversion increased by 22% because they were prompting them at the right time with the right message.

• User Retention rose by 43% because users had higher intent at the point of conversion and a clear reason for subscribing.

• Feature Adoption grew by 27% as more subscribers converted and because a lot more of them expanded their use case because they were getting prompts at the right time for new features to try.

These gains demonstrate the power of AI in creating hyper-personalized user experiences at scale, something that would be incredibly difficult to achieve today.

Key Learnings

• Segmentation at Scale: AI’s ability to create granular, dynamic user segments was crucial at allowing them to target users more effectively than ever before.

• Real-Time Prompt: Identifying the “next best action” for each user type led to a substantial increase in conversion and retention. It’s not just about personalizing the experience, but also about delivering the right feature at the right time—timing is key to ensuring that users take action when they are most likely to engage.

• Integration with Communications Platform: Allowed for automated, personalized interactions while also acting as an “air traffic control” system. This ensures that messages are timely, relevant, and impactful, reducing unnecessary or repetitive communication and improving overall user experience.

Case Study #2: How Asana Used AI to Improve Enterprise+ Renewal Rates

Context:

Asana, a leading work management platform, helps teams organize, track, and manage their work. Asana charges its customers on a price-per-seat basis, with different tiers depending on the number of users and feature access.

Problem:

Asana’s Enterprise pricing model required organizations to pay for full licenses for every active user, leading to the following problems:

• Collaboration Limitations: Power users were unable to share projects with other team members because they lacked the required paid licenses. They observed a slower intra-company growth rate.

• Cost Resistance: Non-power users and occasional users didn’t see the value in paying for a full license, leading to limited adoption across large teams and noticed projects were moving at a slower rates.

• Renewal Concerns: Enterprises were hesitant to renew their contracts, concerned about paying for broad organizational adoption when only a few users were truly active on the platform.

Current Solution:

Asana’s Enterprise pricing model required organizations to pay for full licenses for every active user.

Here’s how power users had to navigate collaboration with non-power users on Asana-driven projects:

1. Sharing Projects Across Teams: Power users spent considerable time scheduling meetings with other team members or sharing their screen during live sessions to walk them through projects. ~1 day a week

2. Frequent Pings: Stakeholders would ping the power users within these organizations to get updates on the project and they had to maintain a separate spreadsheet with deliverables and timelines. ~1 day a week.

New Solution:

Asana leveraged AI to analyze thousands of data points from unstructured sources such as customer support interactions, renewal conversation calls, and meeting notes between Customer Success Management (CSM) teams and Enterprise customers. Through this in-depth analysis, Asana identified recurring themes: customers wanted to share work more freely but were restricted by the cost of additional licenses.

Enterprise clients are different in that they typically have many more stakeholders, teams, and internal processes to navigate. This creates significant friction in collaboration, as multiple departments often need access to the same projects.

By analyzing the feedback, Asana was able to segment their insights into multiple personas that represented different types of power users and non-power users. This segmentation helped Asana understand where the bottlenecks were and identify where the collaboration process was breaking down. For example, many enterprise users reported they were forced to use external tools (e.g., email, spreadsheets) to collaborate, because Asana’s rigid licensing structure limited access.

Based on this insight, Asana launched free, view-only seats exclusively for its Enterprise+ plan.

Key aspects of this new solution included:

1. Free View-Only Seats: These seats allowed enterprise+ customers to share project information and collaborate without requiring additional paid seats.

2. Pricing Structure: Asana kept the Enterprise+ pricing the same but added the free view-only seats as a value-add. This feature was designed primarily to retain existing customers and serve as a long-term strategy to convert users to paid seats.

3. Rolled Out to Existing Users Up for Renewal: The new view-only seats were initially offered to existing Enterprise customers whose contracts were up for renewal. This gave them a compelling reason to stay with Asana and expand its usage across their organizations.

With the new solution here’s how power users had to navigate collaboration with non-power users on Asana-driven projects:

1. Sharing Projects Across Teams: With the introduction of view-only seats, this process was eliminated. Power users could now easily share projects with non-power users without the need for live sessions or meetings. ~1 hour a week

2. Frequent Pings: View-only access allowed stakeholders to independently track the project's progress in Asana, reducing the need for frequent updates. This eliminated the need for manual tracking in spreadsheets.. ~1 hour a week

Impact:

• Increased Paid Seats: By offering free, view-only seats, more employees within the enterprise were able to use Asana. This increased the chances that they would upgrade to full licenses after trying out the platform, leading to a double-digit increase in the number of paid seats.

• Higher Renewal Rates: The introduction of view-only seats contributed to a single-digit increase in renewal rates for Enterprise customers, helping to reduce churn and retain customers longer.

• Increased In-Product Usage: By enabling more people within the organization to use Asana, there was a noticeable increase in the adoption of enterprise features. This helped prevent customers from downgrading to lower-tier plans.

Learnings:

• Use AI to Empower Data Science Teams: Use AI to empower data science teams to analyze unstructured data that were previously too complex or time-consuming to evaluate.

• Treat Your High-Value Customers Well: Ensuring that high-value customers are given attention and value leads to better retention rates.

• Giving Something Free Can Increase Overall Value: By offering free view-only seats, Asana significantly increased its value within organizations. This strategy helped drive broader adoption and usage, making the product stickier.

Case Study #3: How a European based e-commerce company used AI to compete with Amazon on Prime Day

Context:

A leading European electronics and household appliance retailer sought to compete with Amazon during its biggest annual event: Prime Day. With the market being saturated and Amazon leading the charge, the retailer needed a solution to stay competitive, attract customers, and increase sales during this highly lucrative period.

Problem:

The retailer faced the challenge of constantly monitoring over 20,000 products across the electronics and household categories, ensuring they remained competitive with Amazon's pricing.

The key issues were identifying which products would feature in the Prime Day promotion, tracking price changes, and ensuring their prices remained attractive throughout the event and could match Amazon’s prices.

Without a proactive and automated solution, they risked losing out to Amazon’s aggressive pricing strategies.

Current Solution:

Price monitoring refers to the process of continuously tracking the prices of products across Amazon and then making updates to your own platform.

The retailer relied on manual price monitoring, which was time-consuming and inefficient, especially given the massive scale of products they needed to track. The process was largely reactive, making it difficult to stay ahead of price changes during critical events like Prime Day.

Here’s how the tasks played out before automation:

• Tracking Prices Across Platforms: The team spent 4-6 hours per day manually checking Amazon’s prices for 20,000 products.

• Updating Pricing on Own Platform: After tracking the prices, they manually adjusted prices on their platform, taking 3-4 hours per day.

• Identifying Price Trends and Competitive Gaps: The team spent 2-3 hours per day analyzing price trends and identifying competitive pricing gaps, which required them to predict pricing shifts and proactively adjust their strategy for upcoming sales events.

New Solution:

AI improved the retailer's ability to monitor and adjust prices in real-time by tracking Amazon's prices and enabling quick price adjustments to stay competitive.

The system automated monitoring of 20,000 products, checking prices multiple times a day. With four updates daily, the retailer could quickly respond to price changes from Amazon and competitors, ensuring competitiveness.

This automation reduced manual tracking, making the process faster and more efficient.

With the AI solution, the process changed dramatically:

• Automated Price Monitoring: AI automated the monitoring of 20,000 products, checking prices multiple times a day. Time spent: 0 hours per day

• Real-Time Price Adjustments: The AI system recommended prices so all that was needed was to review it and submit the new price to production: Time spent: 1 hour per day.

• Identifying Trends & Adjustments: AI now identified price trends and competitive gaps automatically, freeing up the team to focus on strategic decisions. Time spent: 30 minutes per day.

Impact:

• Revenue Impact: Increased YoY revenue by 10% on critical event days.

• Profit from the Prime Day Campaign Outside Amazon Marketplace: By offering competitive prices on their own platform, the retailer benefitted from the Prime Day campaign without having to sell through Amazon’s marketplace and give them a cut.

• Protected Brand Image: The retailer maintained its reputation as a company that always offers the best price, reinforcing customer loyalty and trust.

Learnings:

• Automate Manual Processes as a Growth Lever: Identify manual processes at your company and as a result it can have a big impact on key metrics.

• Use AI to Outperform Bigger Competitors: AI can be your advantage as an underdog, enabling faster, smarter responses to market changes and helping you stand out in a competitive landscape.

🔥 Insider Growth Group’s Step-by-Step Guide to Identify Ideas on how to use AI:

Who This Is For:

Product leaders looking to leverage AI to drive growth, retention, and optimize user engagement.

1. Research AI Tools Across Categories:

• Research AI tools relevant to your product in these categories:

  • General Assistance

  • Task Automation

  • Audience Building

  • Data Analysis and Structuring

  • Product Building

• Understand the limitations and strengths of each tool to identify which ones can solve specific challenges within your product.

2. Interview Users to Understand Workflows:

• Interview your users from the perspective of their workflows. Identify where they struggle and what parts of the process are time-consuming.

3. List Your Jobs To Be Done (JBTD):

• Identify and list all the jobs your users or team need to complete. These jobs are tasks or objectives that your users are trying to achieve within your product or workflow. Examples might include:

  • Manual segmentation of user groups

  • Repetitive data entry tasks

  • Price monitoring and adjustments

  • Analyzing user behavior in-app

  • Keeping track of a high volume of pricing page versions

• Map the research you’ve done in step #1 (AI tool capabilities) with the jobs your product is helping users or teams accomplish. This will help you identify where AI can most effectively be applied to automate or enhance these tasks, and which ones will have the greatest impact on your metrics.

• A great example of this in action is our Case Study #3

4. Assess AI’s Potential to Enhance User Experience:

• For each manual use case, assess how AI could improve the user experience:

  • Can AI automate this process for the user?

  • Can AI provide personalized or timely insights?

  • Can AI improve decision-making within this use case?

• Identify the areas where AI can make the most significant impact on the user experience.

5. Define Clear, Measurable Goals:

• Set specific, measurable objectives for the AI-driven solution:

  • Increase conversion rates by X% with personalized upsells.

  • Improve retention by Y% through more relevant notifications.

  • Reduce time by Z% through task automation for your users.

• Ensure your AI goals are aligned with business metrics and provide a way to track success.

6. Create a Growth Loop to unlock new use cases:

• Set up recurring meetings to discuss AI’s impact on customer experience and internal processes:

  • Track AI’s effectiveness in driving business metrics.

  • Foster a culture of AI fluency across teams.

• Ensure there is a regular feedback loop to improve and scale AI use cases.

• Share cross- learnings across your team and company which will spark a ton of new ideas on how to integrate it in.

Want to explore how AI can optimize your in-product experiences?

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These markets have typically operated in a high-touch, people-dependent environments given the need for explicit trust during purchase decisions. We believe that the most effective growth levers to disrupt them are "Power of Defaults" and "Don't Make Me Think" - principles that simplify decision-making while maintaining user confidence.

The "Power of Defaults" isn't just about pre-filling information. It's about identifying the best starting point for users to achieve their desired outcome.

"Don't Make Me Think" focuses on eliminating friction in user journeys and anticipating needs before they arise. actually went deeper on this one in the linked article above with .

Through multiple case studies spanning different industries, we'll explore how product teams have successfully leveraged these principles to build trust into their core user experience.

Case Study 1: Reimagining Life Insurance Enrollment

Background

Nora Hughes, who has driven growth in highly regulated industries, encountered a fascinating challenge in the employee benefits space. When employees joined a company, they received basic life insurance coverage as part of their benefits package. However, the process of upgrading that coverage was unnecessarily complex.

The traditional model was greatly undeserving its target customers and a high friction process. Despite life insurance upgrades generating 5-10 times higher commissions than other insurance products, the user experience was driving away potential customers. To even learn about additional coverage options, employees had to:

1. Click through four separate pages in their benefits portal

2. Call a third-party enroller (an independent call center)

3. Coordinate their request through this separate entity

4. Navigate back to their benefits portal to complete the process

This disjointed process created several issues:

• Low life insurance enrollment rates due to friction ($$ Revenue)

• Even when insurance enrollment occurred, poor user experience led to abandoned premium upgrades ($$ Revenue)

The Opportunity

Market analysis suggested that a streamlined digital enrollment process could triple the monthly run rate for the life insurance business. The team saw two key opportunities:

1. Eliminate the need for third-party call centers

2. Create a seamless, digital-first experience that built trust through transparency

The Change

The team redesigned the entire journey using both growth levers.

Power of Defaults:

• Created an unauthenticated landing page that presented insurance options upfront

• Pre-selected recommended coverage levels based on salary and life stage

• Automatically included "guaranteed issue" offers that wouldn't require medical testing.
  
  Note: Typically if you were to sign up for life insurance on your own, you have to go through medical testing to qualify you for certain coverage. Because this is through the employer, there is the option to be automatically eligible for insurance without getting additional medical testing and be eligible for offers that you may not otherwise, called “guaranteed issue.”

• Defaulted to digital completion rather than phone calls

Don't Make Me Think:

• Reduced the four-click journey to a single, coherent flow

• Simplified complex insurance terms into clear, actionable choices

• Provided clear value propositions for each coverage level

• Integrated directly with insurance providers for instant completion

The Impact

Nora’s team doubled the annual recurring revenue for the life insurance vertical and drove 30% of total ARR growth that year.

In addition, they also:

• Achieved >90% preference for digital enrollment over call centers

• Reduced operational costs by eliminating third-party call center revenue sharing

• Significantly improved user satisfaction scores

While this may seem like a straight forward change for internet companies in less regulated environments, remember that Nora and her team had to combat pre-conceived notions about digital enrollment and multiple rounds of legal approval.

Case Study 2: Trulia's Rental Resume

Background

In competitive rental markets, renters often need to apply to multiple properties and stand out from other applicants. Traditionally, this meant repeatedly filling out applications and hoping to catch the landlord's attention. Trulia recognized this pain point in their rental marketplace.

The Opportunity

Trulia saw an opportunity to streamline the rental application process while helping renters present themselves more professionally to landlords. The goal was to increase both the efficiency of applications and the likelihood of success for renters.

The ultimate goal was to increase the % of landlord response rates upon a property reachout by a prospective renter.

The Change

Trulia introduced the Rental Resume feature using both growth levers.

Power of Defaults:

• Automatically created a professional rental profile from user information

• Pre-filled future applications using the stored resume data

• Auto-attached the rental resume to every property inquiry

• Defaulted to including like self-reported income and employment

Don't Make Me Think:

• One-time creation process for a reusable rental resume

• Automatic formatting and presentation of rental history

The Rental Resume transformed a typically stressful, repetitive process into a one-time setup that made every subsequent application both easier and more professional.

It was built into the core experience and made part of the post-lead form journey for a renter where they would be able to answer simple questions with one-click and then have it save to their profile and attach to their current reachout.

The Impact

The Rental Resume was able to increase response rates for Trulia renters in the following ways:

• Users who created and shared a Rental Resume were 10% more likely to hear back from landlords

• Renters who completed at least five of the nine fields saw an additional 10% increase in landlord response rates compared to those who filled out fewer fields

• When combined with a customized message, the Rental Resume increased chances of landlord response by 30%

In addition, the product showed extremely high adoption given its simple UX and integration in the core Trulia rental search flow:

• Achieved 80% completion rate across all resume questions, showing strong user adoption

• Reached scale with tens of millions of renters using the feature within a few months

These metrics demonstrated that reducing friction through defaults while maintaining high-quality information created measurable trust between renters and landlords.

Case Study 3: Brilliant's Live Photo Album on the Wall

Background

Brilliant Smart Home creates smart home control systems that require deep integration with various third-party devices like lights (ex. Lifx, Lutron, Hue), locks (Kwikset, Yale, Schlage), security systems (Resideo), cameras (Nest, Ring), and music players (Sonos). The unique part about Brilliant was that the devices were installed in the place of a light switch and offered a natural way for households to interact with the central control directly from the wall, not just their mobile phone.

When the flagship product, the Brilliant Smart Home Control, was released — it became very clear that the secret to unlocking unit sales wasn't building third party integrations or utilizing smart sensors - it was building trust across the household in a system that controls your entire home.

The Opportunity

The team recognized that while technical features were important, building trust in day-to-day interactions was crucial for adoption and retention.

Early sales and customer feedback indicated that the default screen - what users see first after installing their device was the biggest “aha” moment for a household, and the upload of personal photos was the biggest sign for additional purchases in the household.

Cementing the personal photo upload as part of the core FTUX (first time user experience) for a Brilliant Smart Home user became a key hypothesis to grow unit sales for Brilliant.

The Change

Brilliant applied power of defaults to transform a potentially utilitarian control panel into a familiar, trusted part of the home - something users would interact with even when not controlling smart devices.

• Made the smart photo album the default screen state, which in turn got every household wanting to replace it with their own personal photos.

• Pre-configured optimal display settings for different times of day

The Impact

Brilliant ended up having 80% of its households upload personal photos, and the Brilliant team noticed a very interesting trend. Most households (60%) started out with only one device, and ended up adding another Control one or two months in.

There was a post-purchase survey done for these users that showed that the most common reason was to have a digital photo album in their commons rooms and hallways without detracting from their existing decor.

This played a huge role in the first year of launch for the company with tens of thousands of units sold, and influenced its marketing and sales strategy significantly.

🔥 Insider Growth Group’s Step-by-Step Guide to Building Trust in your Product

1. Power of Defaults Builds Trust: In high-consideration decisions, smart defaults should focus on achieving the user's desired outcome, not just saving time. This might mean pre-selecting the safest option, not the most profitable one.

2. Reduce Cognitive Load: The "Don't Make Me Think" principle is especially crucial in trust-sensitive markets. Every extra decision point is an opportunity for doubt to creep in.

3. Make it Natural: Design experiences that mirror how people naturally make decisions. This often means breaking complex choices into smaller, more manageable steps.

4. Context Matters More Than History: Current needs and immediate context should drive defaults and experience design more than historical data.

5. Think Long-Term: While traditional growth tactics might drive short-term conversion, building trust through thoughtful defaults and seamless experiences creates sustainable growth.

When building products that require significant trust, the fastest path to growth often isn't about speed—it's about removing uncertainty and friction while maintaining confidence in every interaction. The power of defaults and reducing cognitive load aren't just UX principles—they're trust-building tools that drive sustainable growth.

Looking for a new catalyst for growth?

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Many tech companies, such as Evernote and Basecamp, have struggled with this shift. Evernote faced challenges due to a lack of enterprise-grade features and a failure to align its product with IT and compliance needs, while Basecamp's simplicity-first philosophy made it difficult to cater to complex organizational requirements. These examples highlight that transitioning to serve enterprises is no small challenge.

If you’re wondering whether your organization is ready to make this leap, or how to navigate the transition, this case study breaks down when and how Asana moved to focus more on enterprise customers.

Context

Asana is a work management platform that originally grew through a product-led growth (PLG) model, focused on small teams and offering simple and easy-to-use tools for collaboration, such as task management, team communication, and project tracking. In 2011, Asana launched its PLG approach which allowed teams to start using the product organically through a free or trial plan. Over time, as teams within large organizations adopted Asana, the company recognized the opportunity to leverage that organic adoption to become an enterprise-wide tool. In 2020, this realization led to a shift in strategy, combining its PLG foundation with enterprise-focused features and motions to serve a larger, more complex customer base.

Introduction

Introducing Lawrence Han, former Enterprise Product Lead who guided the Asana product through its evolution to the enterprise grade work management platform that it is today.

How Asana drove growth & adoption of Enterprise through PLG

Most of Asana’s early growth was driven through the Land, Expand, Explode PLG model.

1. Land: Champions acquired through organic or paid channels

Asana gets adopted by a team lead who has grown tired of work falling off track and sets out to find a work management solution for their team. These team leads find Asana through its website (driven through paid advertisements, word of mouth, etc.) and sign up for a free trial. The goal is to enable the team lead to institute Asana as a work management system on their team within that trial period and convert to a paid offering for their immediate team.

2. Expand: Cross-Team Adoption

Once a team lead successfully drives value with their team, they cross-pollinate the product across the organization. For example, a marketing team within one division may collaborate with their sales team through Asana, creating an a-ha moment for the sales team, who then picks it up for their own workflows. That sales team’s efficiency inspires another sales team in another division, who then picks it up themselves, continuing the flywheel.

3. Explode: Company-Wide Adoption

As Asana becomes prevalent across many disparate teams within an organization, CIO and IT leaders take notice and consider standardizing on it as an enterprise-wide platform. In this phase, the goal is to take the momentum of the product across several teams, and bring it to the forefront for a centralized buyer to make it the tool of choice across the organization.

Enterprise Readiness: Shifting to a world of many Explodes

As product traction accelerates in the "Explode" phase, CIO offices begin considering enterprise-wide deployment, effectively pulling the product upmarket themselves.

This shift demands an evolution across organizational functions, particularly in product development.

How much to support that evolution, at any given time, depends on a few factors:

• Customer deployments: Over time, Asana could see the Expand motion working effectively, at scale, at its large customers. Asana could observe this in high net retention rates, especially when zooming in on the large customer segment.

• Macro events: Every so often, a large macro trend kicks in and boosts certain categories of software. Over the last 2 years, it has been AI. In 2020, there was a massive and sudden shift to remote work. Asana could observe this through collaboration managers or IT departments entering the funnel, looking to deploy across the organization.

• Category momentum: Over time, the entire work management category became more prevalent, with teams across the world looking for work management solutions across players. You could see this momentum across the category, from brand marketing (i.e. billboards, paid ads) to multiple players hitting IPO scale.

When Asana reached this point, the product organization had to think much more about value it could provide to the Explode stakeholder:

• Building features aimed at organizational value, like Portfolio management and OKRs make Asana more useful for company-level operations.

• Building security and compliance capabilities so IT could have peace of mind about their company’s data flowing into Asana.

• Hooking into an enterprise ecosystem via integrations with other collaboration products (Slack, Microsoft / Google suites, etc.), so companies can create a “best-in-class” SaaS stack that interplays seamlessly.

• Standardization capabilities so that enterprises can match the product with their processes, and do a well synchronized rollout across an organization.

Each of these topics could have its own blog post. But we’ll dive into the one evolution that was needed across the entire product organization: driving a culture of enterprise readiness across the product development process.

Driving Enterprise Readiness across the product

Driving a product that had traditionally run off of PLG into one that could also support the needs of the enterprise, required a shift in how Asana developed product. Some examples of the evolution and expectations when serving PLG vs. Enterprise that the Asana product team had to incorporate in:

Asana’s Enterprise Readiness Review Process

Process changes can be a growth lever by itself, driving significant impact on your metrics. As a product leader, mastering the art of designing and implementing effective processes is essential for advancing your career. Many people think that introducing process will slow down iteration cycles. While that can happen if process exists purely for process' sake, the outcome of a well-designed process change is often the opposite: increased speed, higher quality deliverables, or greater impact on company metrics. The key is to anchor each change in first principles, ensuring it has a direct and lasting effect on the outcomes that matter most.

To help the organization through this transition, Asana created the Enterprise Readiness Squad – an advisory board across Product, Engineering, Design, Legal, Security, Product Marketing and Solutions Engineering that could be a one-stop consultative shop for every product team to get advice on making their features perform in this enterprise context.

Asana’s process was as follows:

• Every week, the Enterprise Readiness Squad held a live forum where teams could submit their specs for an “Enterprise Readiness Review”.

• The cross-functional team spent 15 minutes at the beginning reading the spec and thinking through feedback through their lens. Privacy counsel would consider the privacy risks, solutions engineers would think through how the solution would fit in a customer’s context, PMM would consider how to roll it out to the field.

• The Squad and the working team would come back together and discuss feedback. Asana found this worked best actually having the working team in the room as they could clarify and discuss on the fly.

• Enterprise Squad would then summarize findings in a single slide to hand back to the team as an artifact. The Squad would label things as Do / Try / Consider based on how strongly they felt, but ultimately put it to the feature team to decide what they wanted to do: accept the risks or implement the advice.

Asana tried to make this as low lift as possible, so Asana selected reps from across the company who were accustomed to advising product teams. For example, product counsel who regularly advised teams on new features or sales engineers who were the primary point of contact for product teams. In every scaling company, one thing that creeps up is the number of reviews a team must go to ship. Asana tried to catch teams right around the spec phase, where requirements were solid enough to put on paper, but not so much had been invested (in design, in code) that Asana were sending teams to rework.

Here’s the slide template Asana used with a sampling of findings Asana would recommend.

The Impact

It’ll take some time for a process like this to show results, both internally and with customers.

For the team that instituted the Enterprise Readiness Review, they saw the following success over time:

Quantitative Observations:

• Increased success in the large customer growth rate. For example, in the second quarter of FY23, Asana grew at 51% overall, but those spending over 100K grew at 105%.

Qualitative Observations:

• Features were enterprise ready from the get-go, with less rework or last minute touches to ensure that they landed well with large customers - the sales team could then use these new features in their sales pitch at a faster rate to increase conversion rate.

• Privacy, legal and security teams consolidated their review processes into the Enterprise Readiness Review. This sped teams up, not having to navigate numerous review processes.

• Leaders praised the enterprise readiness review process as a cross-functional collaboration rhythm that was unique and differentiated in the industry.

The TL;DR

• Profitability through Enterprise: Moving upmarket is a critical strategy for SaaS companies aiming to achieve profitability. Larger clients offer scale, long-term value, and increased ARR.

• PLG Seeds Enterprise Growth: Asana’s growth was driven by its “Land, Expand, Explode” PLG model, which started with small team adoption, spread across departments, and culminated in enterprise-wide standardization.

• Enterprise Readiness is a Cultural Shift: Transitioning to serve enterprise customers required Asana to rethink product development, including building security features, meeting compliance needs, and aligning releases with change management processes.

• Collaborative Product Development: Asana created an Enterprise Readiness Squad—a cross-functional advisory board that streamlined reviews and ensured product features met enterprise needs without stalling development timelines.

• Process as a Growth Lever: Asana used process innovation to unlock their team’s potential. By knowing which levers to pull and when, they drove faster execution, better quality, and increased metrics.

• Impact: Asana’s enterprise strategy resulted in a quicker feature adoption by large customers, faster internal review cycles, and efficient cross-functional collaboration.

🔥 Insider Growth Group’s Step-by-Step Guide to Winning Enterprise Clients

Who This Is For:
Product leaders in a SaaS company transitioning from SMBs to enterprise customers.

1. Define your ICP:

   • Your ideal customer persona that understands the value of the product offering

2. Evaluate Readiness:

   • Analyze customer data for demand (e.g., security, compliance, integrations).

   • Identify shifts in user base (e.g., IT involvement, enterprise interest, high value users coming through the funnel).

3. Build Cross-Functional Support:

   • Create an Enterprise Readiness Squad with reps from Product, Design, Legal, Security, Marketing, and Solutions Engineering.

4. Enterprise Readiness Review:

   • Weekly forums for teams to submit specs for feedback.

   • Cross-functional review categorized into Do, Try, or Consider.

5. Deliver Enterprise Features:

   • Focus on enterprise-grade features such as advanced security (SSO, SCIM), compliance (SOC 2, GDPR), admin controls (granular permissions, audit logs), and deep integrations with popular tools.

   • Align roadmap pacing with GTM capabilities.

6. Pricing and Packaging:

   1. Create an enterprise package tailored for larger companies to maximize ACV.

   2. Include features like SSO, SCIM, admin and role permissions, deeper integrations, customized training, and dedicated account managers.

   3. Keep in mind that enterprise buyers often have less price sensitivity but expect volume discounts and procurement wins. Balance flexibility with maximizing account value.

7. Segment the Sales Team:

   • The sales team needs to match the skills of the sales team with the buyer (SMB or Enterprise). And that’s where segmentation comes into play.

8. Monitor Success:

   • Track growth in enterprise customer adoption and time to close.

9. Scaling Enterprise Accounts:

   • Champions of the Product: Leverage intra-company growth by identifying power users using telemetry data and usage analytics.

   • Create Relationships: Assign Dedicated Support Reps to ensure power users' success and foster strong internal advocacy.

   • Sales Tooling: Customize demos and create contextual environments that align with each client’s workflow to showcase the product effectively.

Looking to go upmarket?

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We launched this newsletter in 2024 with the goal of sharing actionable insights and proven experiments to help product leaders and growth professionals drive results. The response has been incredible, and we’re so grateful for this community. As 2024 comes to a close, we’re looking back at the highlight of the year. These experiments are more than just wins; they’re blueprints for sustainable growth in the year ahead.

#1: Uber Eats segmented the experience when searching for a restaurant

Uber Eats noticed two main types of users: high-intent users who came to the app searching for a specific restaurant and exploratory users who browsed to discover options. Initially, when driver demand exceeded supply, unavailable restaurants were hidden from everyone. This frustrated high-intent users who received error messages when searching for their desired restaurant, leading to a poor experience. The team tested a new approach by personalizing results based on user intent. High-intent users were shown their searched restaurants, even if they were unavailable, while exploratory users only saw restaurants that could fulfill orders. The experiment highlights the value of understanding user behavior and tailoring experiences to meet specific needs.

#2: Understanding Multi-Intent User Needs Boosted Headspace Conversions by Nearly 10%

Headspace aimed to boost free-to-paid user conversions by improving its onboarding flow. They discovered many users had multiple wellness goals, such as reducing anxiety, improving mindfulness, or managing stress. Testing two approaches, the team compared a single-intent query (asking users to select one goal) with a multi-intent query (allowing users to choose multiple goals).

The multi-intent approach resulted in nearly a 10% increase in conversion rates. Users who could express multiple needs felt the app better understood and addressed their goals, building trust and driving engagement.

#3: Inside Upstart’s Journey to Improve Customer Conversion Rates

When Upstart noticed declining conversion rates for their auto lending product, the initial analysis did not reveal the root cause. The team decided to dig deeper by visiting dealerships and mapping the product's workflow to real-world usage.

They discovered that dealerships were creating workarounds for customers with lower credit scores, who wanted financing clarity before choosing a car. Upstart’s original workflow performed credit checks later in the process, misaligned with dealer and customer needs.

To address this, the team introduced a "Credit First" workflow, allowing dealerships to check credit eligibility upfront. This simple change resulted in a 40% increase in conversion rates within five months.

This experiment underscores the power of understanding not just your users, but their customers as well.

#4: Trulia’s 1-Click Request Feature

Inspired by Amazon's "1-Click Purchase" model, Trulia implemented a "1-Click Request" feature, allowing users to save their Personal Identifiable Information (PII) and related details in their profile. This feature, enabled by default, allowed renters to contact landlords for more information or to schedule time for a tour with a single tap or click. By reducing the steps required to make an inquiry, Trulia hoped to increase the number of leads generated.

The implementation of this feature also resulted in a 20% increase in leads and revenue. The simplicity of a single click to contact landlords reduced friction and encouraged more renters to submit inquiries, thereby driving higher engagement and revenue.

#5: Using Scroll Depth to Personalize your User Flow at Betterment

The Betterment team noticed a correlation between the location of the CTA on the page and the user's fund rate. There were 3 segments: 1) Users who clicked the top CTA, had a high sign up rate, and high fund rate 2) Users that immediately bounced 3) A group that engaged with the page but wanted to do more research.

The team structured an experiment by altering the onboarding process based on the depth of the sign up CTA on the page, identifying users who scrolled as having lower intent.

There was a statistically significant improvement in funded customers. Many people think only to use signed-in engagement data for personalization, but visitor entry point data can also enhance your metrics.

#6: Instagram Story Sharing for Fundraiser Growth

Instagram’s data showed that the closer a user was to the person creating the fundraiser, the more likely they were to donate. Fundraisers often reflect deeply personal causes, and Instagram understood that users would be more likely to contribute to a friend’s campaign rather than one from a distant acquaintance.

As part of the fundraiser creation process, Instagram introduced a feature where the person who starts the fundraiser can immediately post it to their story with one click. This minimized the effort required to spread awareness and created a viral loop where fundraisers could gain more visibility through stories and social networks.

Instagram as a whole has a few users who generate the most impressions (ex: 1% of users are responsible for 55% of all impressions), and this principle applied to fundraisers as well. By aligning fundraisers with topical events—such as political causes or social movements—Instagram was able to drive higher engagement for fundraisers with deep, personal relevance.

Instagram found that user motivation doesn’t end after making a donation. To extend the lifecycle of a donation, Instagram added a feature that allows users to add their donation to their profile and receive notifications. This increased the visibility of the cause and encouraged more donations, as users' followers could now see which causes were meaningful to them, driving additional awareness and engagement.

This ended up doubling donations generated through Instagram.

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Airtable is a platform for building internal business applications to do everything from running startup operations to managing the rollout of global TV campaigns to planning events all without having to know how to code. The platform attracts a broad user base, including operations managers, project coordinators, and marketing teams who need customized workflows to keep projects running smoothly.

Business Model

Airtable uses a tiered subscription model to generate revenue. It offers a Free Plan with limited records to attract users, then upsells to paid plans like Team ($20/user/month) and Business ($45/user/month), which include advanced features like more records, automation runs, and premium integrations. For large organizations, the Enterprise Plan provides customized solutions with enhanced security and admin controls.

Introducing Kelly

Introducing Kelly O'Shaughnessy, Head of Core Product and Growth at Airtable, where she leads initiatives to drive revenue growth from self-serve channels and deepen customer engagement. Prior to that, she worked at Uber and led the mobility incubator focused on unlocking new transportation adjacencies (car rentals and reservations).

Problem

Current Funnel

Despite Airtable being primarily a desktop product, a significant portion of new users signed up on their mobile app—around 30%—with an even higher percentage when looking at users acquired through paid ads.

Airtable faced issues with its mobile app onboarding, where users were not activating. Because less users were activating on the mobile app this prevented users from progressing to the web platform to make their final purchase.

Activation is defined as a workspace with 2 or more active users in week 4 post-signup.

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Issues with the Current Mobile App experience

The Airtable team identified several issues with the existing mobile app experience that were contributing to high churn rates.

Problem #1: Sign Up Prior to Showcasing value

Required users to sign up before they could experience any value, which created friction and contributed to drop-off. This approach made it harder to build trust and demonstrate Airtable's capabilities, resulting in users abandoning the process before onboarding, activating or transitioning to the web.

Problem #2: Onboarding Focused on Breadth Instead of Depth

In a user's onboarding, the goal should be to help them achieve their specific job to be done—the primary task or problem they’re trying to solve. Showcasing the full range of a product’s capabilities or use cases may seem like a way to showcase all the functionality, but it often overwhelms them instead. For Airtable, this breadth distracted users from understanding how the platform could help with their immediate needs, leading to confusion and drop-offs.

One common sentiment echoed in feedback was, “I have no idea what Airtable is or what to use it for, and this screen doesn’t help.”

Problem #3: Confusing First Time User Experience (FTUX)

The activation process did not include any hands-on guidance for building something within the app.

Upon signing in, users were taken directly to a blank homepage without any context, access to templates nor instructions.

The Solution

The Airtable team implemented the following changes:

1. Revamped Intro Screens to Build Trust: Added new intro screens to clearly explain Airtable’s value and key use cases prior to sign-up to build trust.

2. New Activation Flow Focused on Building: Introduced a guided activation flow that focused on helping users complete a simple project. The focus was on a single project management template.

3. Improve the First Time User Experience: Redesigned the initial landing experience to provide more contextual guidance.

Revamped Intro Screens on the App

Step by Step Airtable Creation Build Experience

Introduced a step-by-step flow to get the user to focus on a single project management use case:

First Time User Experience

The first time user experience was redesigned to focus on showcasing 4 popular mobile view types for the project management use case – the form, calendar, dashboard, and list which made it more intuitive for users by visually showcasing project timelines and tasks.

This approach not only showcased Airtable’s value proposition more clearly, but also proved a far more engaging and easier to understand experience than the blank homepage.

The Impact

• Improved Upgrade Rate: A 15% increase in the upgrade rate for users in the treatment group.

• Increased Focus on Mobile:

  • Mobile Activation: More resources were allocated to mobile development, specifically targeting activation as a way to drive overall Airtable growth.

  • Expanded Product Roadmap: Product roadmaps, previously web-centric, now include parallel priorities for both web and mobile, reflecting the growing importance of mobile.

  • Reinvigorated Mobile Team: The mobile team transitioned from solely supporting core features to also driving growth initiatives, starting with acquisition and onboarding. This gave the entire team a new sense of purpose and motivation.

Guiding Principles for Implementing Similar Changes

• Establish core needs between platforms by using the Jobs to be Done framework
  Identify the primary tasks users need to accomplish on mobile, especially those they’re likely to engage with on the go. Instead of replicating the desktop experience, streamline features to solve essential, mobile-specific needs effectively.

• Align Onboarding with a Single, High-Value Use Case
  Focus on a single, impactful use case during onboarding to help users experience value quickly. Rather than overwhelming users with multiple options.

• Trust is the Ultimate Shortcut
  Establishing trust early through clear messaging and well-designed intro screens makes users more likely to engage with your product. When users feel confident, they’re more willing to invest time exploring further.

• Encourage Platform Transitions Only After Establishing Value
  Before asking users to make a switch provide enough value.

• First Principles Thinking
  Start by asking what the simplest path is for users to achieve success, then build the experience around that fundamental need.

Conclusion

This win not only delivered immediate improvements but also laid the foundation for a broader roadmap to expand on these successes. The team began by designing new initiatives to capitalize on the insights from the experiment:

1. Push Mobile Web Users to the App: The team focused on driving mobile web users to download the app and activate there, delaying the "push to desktop" transition until further down the funnel.

2. Introduce Onboarding on Mobile Web: A simplified version of the onboarding experience was added to mobile web, guiding users through initial steps before prompting them to transition to either the app or desktop for full activation and conversion.

By starting small and proving the value of investing in mobile, Airtable was able to test their approach without committing to high development projects first.

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There are generally five methods to justify new pricing increases, but we will be focusing this article on (a) how to know when you have an opportunity for a pricing change, (b) common pitfalls and mistakes that happen, and (c) how to do it the right way with confidence. 

The five common methods to justify new pricing are:

1/ Value-Based Pricing: Charge based on how your user gets value. Mixpanel switched from charging for analytics events to charging for monthly tracked users. This aligned pricing with customer value.

2/ Add a New Use Case: Think Figma. Instead of changing an existing tier, they launched a new enterprise tier with its own pricing model. 

3/ Billing Cadence: Switch from monthly to annual billing, or from transactional to subscription. Calm moved to annual-only subscriptions—locking in commitment and boosting LTV. 

4/ Packaging Updates: Repackage what you offer. Dropbox increased storage from 1TB to 2TB and introduced Smart Sync. Same core product, better packaging, higher price.

5/ Price Increase: Amazon Music did it without changing a thing but showcasing new features they’ve launched to justify the price increase.

Addressing the fear of a pricing change head-on 

Many business leaders are hesitant to adjust their pricing, fearing customer backlash or revenue loss. However, this reluctance often leads to leaving significant money on the table—sometimes as much as 40% of potential revenue. 

And this fear is well justified. It can be a disaster for your company if you roll it out wrong. 

Let’s take Toast, a prominent SaaS provider for the restaurant industry. In July 2023, Toast attempted to introduce a 99-cent fee for online orders that directly impacted its restaurant clients. This move led to severe backlash as restaurant owners, already operating on thin margins, saw this as a cash grab with no added benefit to their business. 

It’s likely that Toast made common mistakes before rolling out this pricing change. They probably did not talk to customers or a customer advisory board to understand how customers will react. They likely did not test out pricing on a smaller customer segment to real-world test the pricing change. And they definitely did not clearly communicate the purpose of the fee - otherwise the restaurants would not have thought about it as a money grab. 

There are many more. Zendesk, Unity, Comparato. Look at the headlines below - this is the nightmare that every CEO wants to avoid. 

Now that’s enough about the blowups - we’re here to share tools and practices on how to do this right. 

Companies that approach pricing with a structured and well-thought-out strategy find that they can make changes with confidence and reap the benefits. 

Introducing Amit Saraf, SaaS pricing expert 

Amit Saraf is a Senior Pricing Strategist at Pricing I/O, specializing in SaaS pricing, product management, and high-impact strategies. With extensive experience at Zynga, LiveRamp, and Calix, Amit has a proven record of driving product success from inception to multi-million-dollar revenue streams. At Pricing I/O, a firm that “takes the guesswork out of pricing”, Amit has worked with dozens of software clients that serve a range of industries, to significantly improve their monetization strategies.

Are you underpricing your product?

The first step to developing a pricing strategy is to understand if you are underpricing your product. Amit and Pricing I/O often find companies that are underpricing their services by significant margins. It is not uncommon for firms with $50 million to $200 million in revenue to find that they are leaving 40% or more on the table. Drilling down further with customer segmentation, disparities as high as 90% have been revealed.

In order to stay ahead of this, he uses the following frameworks, which are especially useful in B2B SaaS. Amit recommends companies in B2B SaaS re-evaluate pricing at least every 2 years and no more often than a 6 month period. A pricing committee that meets regularly and a formal or semi-formal process is the best approach here.

Willingness to Pay (WTP):

Understanding WTP is vital and can be assessed through customer interviews, surveys, CRM and product usage analysis, and competitor research. Popular methods like the Van Westendorp Price Sensitivity Meter and the Gabor-Granger technique provide structured ways to determine optimal pricing.

A key item that is often missed is there does not have to be one price across the board. By using customer segmentation, varying packaged offerings (entry vs premium), add-on modules, and strategically implemented gates, a company can capture different WTP levels across its customer base and better optimize for both revenue and value-delivered to its customer base.

Customer Advisory Board (CAB)

Having a customer advisory board can be very useful to start testing the waters with new pricing strategies. This is a group of customers that you can float ideas to and understand quickly, and without risk, if there are blind spots. Even without a formal CAB, a trusted group of customers can validate ideas and identify pitfalls quickly.

Segment your User Base

A common approach to aligning monetization more closely to value-delivered is by segmenting your customer base. Rather than use a single-variable approach, Amit recommends a 2-variable, 9-box approach with strategically chosen variables.

The most common segmentation is customer size or level that they pay, but that does not go deep enough. You can segment by variables such as:

• Customer Tenure

• Industry segment

• Customer size

• Product usage data

• Level of discount to list

• NPS rating

• Even # of recent support tickets

Is it really all or nothing?

One of the largest misconceptions around pricing in the SaaS space is that a price change has to be an all or nothing game. Per research conducted by Pricing I/O, we know there is often a wide range of WTP (willingness to pay) for any given software product. Using that insight, it is likely that most software companies can improve alignment of value-delivered with willingness to pay, and it often be done in targeted and creative manners.

What happens if you encounter pushback? What do you do? 

Let’s say there is a higher willingness to pay in your customer base (based on the Van Westerndorp methodology above), but you test this out in front of your CAB and get pushback on a potential price increase. 

You should not give up there. You should dig further into the data and understand what segments may be more amenable to a price increase. 

Is there a segment that was not represented within your CAB? 

Or is there a higher tier offering you can introduce that will allow customers to choose to upgrade for premium features and/or support, and those that are more price sensitive can remain in a more restricted tier? 

Or are there existing or future features you can offer as an add-on module to capture more value without increasing pricing on the overall base?

Pricing changes can, and often should, be offered in a targeted manner.

How to roll out a pricing change

Once you’ve determined you need a pricing change, it's critical to roll this out carefully and be well-coordinated as a team. There are multiple levers that one can leverage, and we discuss two of these below.

Option 1: Rollout with a standard Battlecard Strategy and Communication

A battlecard helps teams stay aligned on key talking points, pricing justification, and customer responses during price changes. Teams need to know what is ok to say and do not want unstructured conversations nor too many decisions made on the spot and based on a gut feel. GTM Teams are able to use battlecards as their “cheat sheet” for addressing push back on a price increase. Battlecards should be put together ahead of any pricing roll-out and are typically owned by the primary owner of pricing with input from customer success. This owner may be a product marketing lead, product management lead, or other GTM Lead. 

Let’s go back to the Toast example. Imagine if they started with a sample group (ie a CAB or trusted set of customers). They would have understood quickly and w/o risk if there are blind spots. And the GTM teams would have been prepared when going into the communication to set the tone properly without sounding smug, apologetic, or non-responsive (all adjectives that Pricing I/O strongly recommends to avoid in any communication with customers). 

Additionally, Toast may have supplemented its risk mitigation measures with preparation such as a battlecard strategy. In the below visual, you can see an example of that preparation. Each segment may have a battlecard, as outlined below, that highlights the key issues facing a customer that may be receiving a price increase. The battlecard gives the front-line employee an opportunity to quickly address any pushback. For instance, in the “Migration Play” section, we see how the front-line employee (example Customer Success representative) has an “if-then” scenario plan to address any push back. Having these battlecards and this plan, up front, greatly diminishes risk to the roll-out of a pricing change.

It’s critical to go in confident and direct with the reason for the change, but be willing to mitigate when needed. 

Option 2: Run Strategic A/B Tests before rolling it out fully

Strategic A/B testing can offer valuable insights, though in B2B SaaS, it should be used judiciously. While data analysis is crucial, relying solely on numbers without considering broader market intuition can lead to misleading conclusions.

In B2B SaaS, A/B price testing should be used in a more limited and very strategic manner.

In B2C SaaS, A/B price testing should be used much more liberally.

The key here is to blend data analysis with some intuition. 

A word of caution – Amit has seen companies go too deep into the data and miss the forest for the trees and some companies rely too much on “gut”.

Zynga was a classic case where everything in a game was A/B tested with deep data analysis. For instance, new promos were launched, and the PM would always show a nice uplift and the promo variant was chosen.

However, if you looked at the data across years, for the many promos that Zynga did, it was clear that revenue was just being brought up front versus a long-term change in monetization. This is driven, in part, by not being able to do year long or even months long hold out tests. 

This is an example where getting too deep into the data drove misleading results.

It can be done well

A creative industry-specific software company, recently acquired by a private equity firm, suspected they were underpricing but needed concrete data to support this belief. While the PE firm strongly advocated for a price increase, internal opinions varied. Frontline sales and customer success teams voiced significant concerns. The management team was also mixed and concerned that a price increase would encounter adverse consequences.

Amit worked with the company to not only alleviate the team's fears, but demonstrate, with data and concrete insights, just how underpriced their products were. 

Specifically, the team ran through its PDP (Pricing Design Program). This program included:

1/ Evaluating the opportunity

• Analyzing the data. Win/Loss, Churn, and Reasons for Churn, product usage correlated to what was purchased and when it was purchased, etc. 

• Conducting customer and prospect interviews to understand how customers measure their ROI. Include recent “closed lost” in this list of interviewees.

• Researching the competitive landscape - how were the new players, that were up and coming, and cheaper, being perceived?

2/ Running WTP curves using proven WTP methodology (Gabor-Granger, Van Westerndorp)

The Gabor Granger, Van Westerndorp, or other method is a powerful tool that incorporates data-driven insights to set appropriate pricing levels. One approach to gathering this insight can look as follows:

1. Respondents are shown a description of the product/service.

2. Then they are asked how likely they are to purchase it at a given price point, usually on a 5-point scale from "Extremely likely" to "Not at all likely".

3. The price is then adjusted up or down based on their response, and the question is repeated.

4. This process continues until the highest price point the respondent is willing to pay is identified.

Through a WTP method similar to this approach, Amit’s team identified that rather than conducting a blanket price increase, it would be better to focus on a larger increase for a specific customer segment (approximately half the base) that showed the highest opportunity for upside.

Additionally, the data analysis and prospect interviews led the team to recommend an approach to aggressively reset the base for customers with legacy discounts. It was clear that new customers were signing up for the software services at prices much closer to the list price, legacy customers were using the product at similar rates to new customers, and that there was no clear reason that legacy customers remained at lower levels other than historical inertia and intelligent push back on prior attempts to raise pricing.

However, even with the recommendations in place, there were team members of the software business that were concerned around implementing a price increase. To bring these team members on board, multiple discussions were conducted talking the concerned team members through 1) why the data and insights supported the recommendation, 2) how to address any concerns specific to their specific role (commission and role-specific incentives can be a major hurdle to overcome), and 3) why a reset in pricing across legacy discounted customers would better set the company up for success in the future.

Finally, a robust and thorough risk mitigation plan, with battlecards and testing the roll-out, helped bring the CEO and even the private equity firm fully on board with the roll out.

By following this robust process, the software business was able to roll-out the price increase in an accelerated fashion and with more confidence — with an ARR increase of 40%!

Amit and team noted this is a common scenario, as anecdotal information from customers often masks what concrete data and insights portray.

Pricing Strategy Checklist

We went through a lot of content above. With any pricing strategy evaluation and rollout, remember that the below actions are fundamental to success. 

• Segment the User Base: Differentiate pricing based on customer needs and value perception.

• Risk Evaluation: Analyze the potential impact and risks associated with each customer segment.

• Voice of the Customer: Continuously monitor customer feedback and support interactions.

• Segment Modeling: Design models for various customer types to understand potential outcomes of pricing changes.

• Consulting Trusted Customers: Using a CAB or trusted client group to gather initial feedback.

• Strategic Testing: Implementing changes on a smaller scale to detect blind spots.

• Tailored Communication: Presenting changes confidently, with a clear explanation of how it benefits the customer.

• Consistent Support: Preparing support teams to handle incoming queries with well-rehearsed responses.

Applying it to your business

We discussed a number of indicators that may suggest you have pricing power, and if so, a number of tools to monetize that opportunity. It’s likely you do have room to update your pricing, but at minimum you should regularly reevaluate it across your customer segments. 

While any smart and driven CEO, CFO, product marketer, and/or product manager should be able to employ any of these strategies on their own, Amit has seen guided expertise in pricing deliver additional confidence and accelerate the ability for companies to monetize this opportunity, versus going it alone. 

Amit’s firm, Pricing I/O, has the best people in the business for monetization in SaaS and have worked with 100s of clients on monetization opportunities. The firm includes pricing strategists, data-focused pricing analysts, as well as the founder, Marcos Rivera, who has an award winning book on SaaS monetization and is the founder of the firm. If you have any questions on the strategies above or are looking for more guided expertise, feel free to reach out here. 

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Inside Upstart’s Journey to Improve Customer Conversion Rates

Context: 

Upstart is the leading AI lending marketplace. Upstart’s AI helps make borrowing more accurate, efficient, and inclusive.

Product Area: