Category: Product Management Leadership

From Resolutions to Outcomes: How We Price AI Agents Fairly and Amplify Customer Value

I’ve long believed a simple truth about AI in customer support: if AI is going to earn trust, pricing has to be aligned with value. That principle has guided my product decisions and the way I hold our teams accountable for measurable outcomes, not activity.

When we shared our perspective on pricing AI Agents in 2023, we made a simple argument: if AI is going to earn trust, pricing has to be aligned with value. At the time for Fin, that value was clear. You pay when the AI resolves a customer’s problem. If it doesn’t, you don’t. That’s fair, easy to understand, and grounded in results, not activity. We were the first to introduce this pricing model because we believed that pricing and value should be inherently linked.

That belief hasn’t changed, it’s grown stronger over time. What’s changed is what Fin can do. As we expanded capabilities and pushed deeper into complex workflows, it became clear that measuring value solely by end-to-end resolutions no longer captured the full picture of impact.

Resolutions were the right place to start. Historically, we measured value based on whether Fin fully resolved a conversation on its own. These are known as resolutions and they gave support teams a clear way to measure ROI, easily comparing the cost of AI versus human support. They also aligned our incentives with our customers, as our revenue was directly tied to Fin’s performance.

That clarity worked. Today, more than 7,000 teams use Fin. Our average resolution rate across customers has increased every month and now stands at 67%, even as Fin increasingly handles more complex queries. That progress came from building an Agent that could take on harder problems and still deliver.

But as Fin got more powerful, “success” stopped being binary. I saw this first-hand in customer design sessions where policy, risk, and compliance needs rightly demanded human-in-the-loop confirmation. We weren’t failing to deliver value; we were delivering it differently.

Over the last couple of years, we invested heavily to ensure Fin could handle the most complex parts of support. As Fin’s capabilities expanded, customers began pushing what Fin can do for them by deploying Fin deeper into their workflows to handle the toughest queries.

In some cases, this required Fin to work in tandem with a human agent because that’s what customer policies and oversight needs dictated. Subscription changes, transaction disputes, billing issues, and other multi-step support scenarios can often require Fin to gather context, read and write to external systems, and execute actions before handing off to a human agent for confirmation.

Fin is still doing what it was configured for – intentionally handing off after doing more of the heavy lifting, saving valuable time for support teams and overall time to serve for their customers. But our pricing metric only recognized value when the conversation ended in a full “AI resolution” (i.e. a human was never involved).

That’s why we’re evolving Fin’s pricing metric from resolutions to outcomes. This shift reflects how customers now define value: not just in full automation, but in safe, efficient progress toward the right result across complex, multi-step, and policy-constrained workflows.

An outcome represents when Fin successfully completes the action it was configured to perform, as part of a conversation. Resolutions are still one type of outcome Fin can deliver, where it handles the issue end-to-end. Another type of outcome can be a Procedure where Fin gathers context, takes action, and hands the conversation off when that’s what customers configured it to do.

Kick off your journey with the #1 Agent—an AI partner designed to turn resolutions into real outcomes. Tap “Start a free trial” to explore faster, smarter customer service and see how Fin delivers value from day one.

Increasing end-to-end AI resolutions is still a core component of scaling Agents, but they are no longer the only measure of Fin's success and utility. Especially as Fin takes on more complex work. Moving to outcomes recognizes that solving a customer problem with full automation isn’t always appropriate. It’s about getting to the right result, safely, and efficiently.

As Fin’s capabilities expand, teams should feel empowered to use it in more nuanced, collaborative work. Outcomes support that by allowing customers to design workflows that meet compliance requirements and include a human agent when necessary. From a product management standpoint, this is how we align incentives, keep risk controls intact, and still accelerate time-to-value.

Fin is becoming even more powerful at handling complex, multi-step support queries. With outcomes, we can support that growth without constantly reinventing how value is measured. And this change gives us a strong pricing foundation that can scale as Fin continues to grow and take on more roles beyond service. This aligns with our vision of Fin becoming a “Customer Agent,” capable of handling the entire customer experience.

What this means for pricing is intentionally straightforward. An outcome will be counted when Fin successfully completes an action it was configured to perform, as part of a conversation. That keeps the model predictable for finance leaders while staying transparent for operators and product teams managing AI workflows.

The pricing model stays simple and the definition of value becomes more accurate. In other words, we’re doubling down on fairness, predictability, and competitiveness—core tenets for any consumption SaaS pricing strategy tied to real business impact.

When we first wrote about outcome-based pricing, we said that trust is the currency of AI. That’s still true. Trust is earned when customers see pricing move in lockstep with utility and risk posture, especially as gen AI and agentic AI take on higher-stakes tasks.

Pricing has to feel fair, it has to be predictable, and it has to stay competitive. Evolving from resolutions to outcomes isn’t a departure from that belief. It’s the natural maturation of how we measure value as AI moves from simple Q&A into complex procedures and human-in-the-loop collaboration.

Fin has grown more powerful because customers asked more of it. Outcomes are how we reflect that progress honestly, while staying true to the same principles that guided us from the start. This is product strategy in action: align incentives, measure what matters, and scale what works.

And as Fin continues to get stronger, we’ll keep holding ourselves to the same standard: price based on the value delivered. That’s how we build durable trust, sustainable ROI, and a better customer experience at scale.

Inspired by this post on The Intercom Blog.

March 12, 2026
Inside Zipline’s Wild Pivot: My Take on Hiring Heat-Seekers and Scaling to 5,000 Hospitals

I’m consistently drawn to stories where product strategy and operational grit collide to change real lives. Zipline, the world’s largest commercial autonomous delivery system, is one of those rare cases. Serving 5,000 hospitals across multiple countries and saving an estimated 17,000 lives per year, it embodies the kind of mission-driven execution I try to model in product management. The arc—from a near-dead home robot startup to a scrappy bet on drone blood delivery in Rwanda, to 135 million autonomous miles flown—offers some of the clearest lessons I’ve seen on hiring, leadership, and product-market fit under extreme constraints.

One principle that immediately resonated with me: why Zipline doesn’t hire for experience. The idea behind “Why Zipline hires teenagers over PhDs” isn’t a dismissal of expertise; it’s a commitment to learning velocity, ownership, and unteachable hunger. The best startup employees, as described here, are “heat-seeking missiles for pain”—people who chase the hardest problems, not the shiniest projects. In my org, I look for the same signal: candidates who can move from ambiguity to action, who find the bottleneck without being asked, and who care more about outcomes than optics.

I also appreciated the unapologetic stance that “blind references are a non-negotiable.” In high-stakes builds—especially in regulated or safety-critical categories—the cost of a mis-hire compounds. I routinely validate for two traits during references: intellectual humility and accountability. “Can candidates admit when they screwed up?” is a powerful filter. If someone can’t name a hard mistake and how they specifically changed as a result, they’re unlikely to scale with the organization.

Equally important is clarity about who not to hire. The employees Zipline doesn’t want are those who optimize for status, process theater, or low-friction work. In practice, that means pressure-testing for problem-finding, not just problem-solving. I often design interviews around messy, cross-functional constraints (regulatory, operational, and financial) to see who can integrate tradeoffs, not just ideate features. That’s how we build empowered product teams that ship consequential outcomes, not outputs.

There’s a reference to “Zipline’s secret leadership playbook,” and while the specifics remain private, the spirit is unmistakable: first principles decision making, ruthless focus, and a culture that rewards radical responsibility. Translating that to my product organization, I emphasize five behaviors: orient to the mission under uncertainty, run fast but close the loop with data, communicate constraints early and often, own the long tail of consequences (especially in safety and reliability), and scale judgment by teaching the why, not just the what. That blend of clarity and autonomy is the backbone of product management leadership at any growth stage.

On the other side of the culture coin is “Why you should always fire quickly” and “The brutal firing advice that shaped Keller’s leadership.” I’ve learned (sometimes the hard way) that slow decisions erode trust and team velocity. Moving quickly doesn’t mean being harsh; it means being fair, explicit, and humane—tight feedback loops, role clarity, and decisive action when the gap persists. If your bar is clear and your coaching is consistent, acting fast protects both the mission and the team’s energy.

Strategically, the origin story reads like a masterclass in choosing the right problem. The team moved “from toy robots to drone delivery: Zipline’s pivot,” then partnered deeply with Rwanda, where “How Rwanda’s health minister changed everything” is a pivotal moment. It wasn’t a linear climb—”How Zipline almost died – twice” and “Why Zipline’s launch was a ‘complete disaster’” underline a tough truth: breakthrough products rarely arrive fully formed. What matters is the operating cadence that turns early chaos into repeatable reliability—especially when the stakes are measured in minutes and lives.

Scaling from 1 hospital to 5000 required more than product brilliance; it demanded systems thinking across logistics, compliance, safety, and community trust. That’s stakeholder management at its highest level. The product lessons are durable: anchor on outcomes, not artifacts; build reliability as a feature; and practice founder-led GTM where your credibility is on the line with customers and regulators. This is where first principles decision making beats benchmarking—particularly in novel categories where there are no playbooks to copy.

There’s also a hard-nosed operational takeaway in “The 10x hardware cost rule every founder should know.” My read: assume total cost of ownership will balloon once you account for manufacturing variability, support, redundancy, maintenance, and compliance. In product strategy, I treat those multipliers as design inputs, not afterthoughts. If the unit economics can’t survive these realities, the idea isn’t ready—no matter how elegant the prototype looks in a lab.

Across all of this, a few product management patterns stand out for me: build teams around outcomes vs output OKRs; hire for slope, not just intercept; make continuous discovery routine with real users (in this case, clinicians and health systems); and treat operational excellence as a product surface. When a mission is this consequential, culture becomes a safety system—and every leadership decision compounds into either speed with quality or speed with regret.

For leaders building in complex domains, this journey is a blueprint: pick problems that matter, hire “heat-seeking missiles for pain,” keep blind references non-negotiable, lead with first principles, and scale with responsibility. Do that well and even a “complete disaster” launch can become the inflection point of a category-defining company that flies 135 million autonomous miles and saves 17,000 lives per year.

March 12, 2026
Ship Smarter with Amplitude + Lovable: See Behavior, Fix Friction, Iterate Faster

I build products with a simple mantra: launch, learn, repeat. Shipping fast is necessary, but shipping smart is what compounds. To do that, I keep analytics close to the work—inside the builder—so every decision is tied to real user behavior, not assumptions.

Connect Amplitude MCP to Lovable to understand user behavior, spot frictions, and ship better updates without leaving your builder.

In practice, this integration lets me bring Amplitude analytics and behavioral analytics directly into the creative flow. I can explore funnels, cohorts, and drop‑offs the moment I’m crafting an experience, then translate those insights into concrete changes without context switching. The result is tighter feedback loops and more confident iteration.

My typical loop looks like this: identify a friction point from funnel analysis, design two or three variants in the builder, and run A/B testing to validate the improvement. I focus on user activation and retention analysis as leading signals, because sustained engagement is the clearest indicator that we’ve solved a real problem. When the data confirms it, we promote the winning experience and move to the next opportunity.

Keeping the work inside the builder also supports continuous discovery. I can pair quantitative insights with qualitative observations, refine journey mapping, and document learnings while the context is fresh. That makes prioritization and product discovery more reliable, and it turns each iteration into a teachable moment for the team.

Strategically, this builder‑first approach enables product-led growth. With fewer handoffs and a unified analytics platform, we compress time from insight to impact. It helps me defend roadmap decisions with evidence, communicate trade‑offs clearly, and keep the team focused on outcomes that matter to customers and the business.

If your goal is to iterate with speed and precision, bring analytics to where you build. Keep the loop tight, measure what moves the needle, and let the data guide your next best update.

Inspired by this post on Amplitude – Best Practices.

March 11, 2026
Inside Amplitude’s AI Acquisition: Career Lessons Product Managers Can Use to 10x Impact

I’m often asked how to translate early-stage experience into outsized product impact at scale. In my own practice, I study real career arcs that crystallize the habits of high-leverage product managers—especially those operating at the intersection of analytics and AI strategy.

Consider this path: Lucas is a Product Manager at Amplitude. Previously, he was employee #1 at Command AI, acquired by Amplitude in October 2024. Lucas studied computer science at Princeton.

What stands out to me is the compounding effect of being an early builder. When you are employee #1, you live close to the user problem, own outcomes end-to-end, and develop a bias toward focused, continuous discovery. That foundation creates durable instincts around product strategy, sharp prioritization, and empowered product teams—skills that transfer directly to later-stage environments where clarity and speed become competitive advantages.

Acquisition integration is where those instincts meet enterprise rigor. Folding Command AI into a unified analytics platform like Amplitude requires disciplined product roadmapping and sprint planning, precise stakeholder management, and a strong POV on where AI augments core “Amplitude analytics” versus where it creates net-new value. The north star remains unchanged: deliver measurable customer outcomes that strengthen product-led growth and reduce time-to-value.

On the AI front, I’ve seen the most successful PMs treat gen ai and LLMs for product managers as means, not ends. They anchor use cases to concrete analytics workflows—accelerating insight generation, surfacing anomaly detection, improving retention analysis, and driving user activation—while validating each step through continuous discovery and rigorous experiment design. This balance of ambition and evidence protects teams from shiny-object drift and keeps investment tethered to business impact.

Execution-wise, the playbook is straightforward but unforgiving: clarify the problem through customer interviews; define crisp outcomes vs output OKRs; map the journey end-to-end; ship in thin slices; and iterate with observability baked into every release. Along the way, keep your cross-functional partners close—solutions engineering, customer success, and GTM—so that your learning loops extend beyond the product surface and into real adoption dynamics.

If you’re building analytics or AI-powered experiences today, borrow these lessons: translate early-stage builder energy into enterprise-scale focus; make AI serve the product, not the other way around; and use Amplitude analytics to close the loop from idea to impact. That is how PMs compound credibility, accelerate careers, and, most importantly, create products customers can’t live without.

Inspired by this post on Amplitude – Best Practices.

March 11, 2026
Unlock High-Impact Mobile Engagement: Amplitude Guides & Surveys for iOS, Android, React Native

Mobile engagement is most effective when it’s timely, contextual, and grounded in real user behavior. In my experience leading product teams, the fastest path to activation and retention comes from meeting users in the moment with relevant in-app guides and lightweight surveys that reduce friction and illuminate intent.

Deploy behavioral-driven mobile engagement with Amplitude Guides and Surveys for iOS, Android, and React Native platforms.

What excites me about this approach is how naturally it supports product-led growth. In-app guides and product tours streamline onboarding, while targeted micro-surveys surface the “why” behind user actions. The result: clearer journey mapping, fewer blind spots in the funnel, and a smoother path to user activation—all without adding engineering heavy-lift for each iteration.

To optimize continuously, I pair behavioral analytics with A/B testing and retention analysis. This lets my team validate hypotheses quickly, localize friction by segment or stage, and tune messaging for different cohorts. With Amplitude analytics at the core, we can connect engagement nudges to downstream outcomes, not just clicks—so we’re improving time-to-value, not just surface metrics.

My recommended starting point is simple: define a single activation moment, instrument the critical behaviors around it, and launch a focused guide plus one survey to test the narrative. Use journey mapping to identify the key decision points, then iterate weekly based on observed behavior, not opinions. This cadence keeps learning velocity high and ensures every change moves us closer to clear outcomes.

From a leadership perspective, I coach product trios to own an activation or retention KPI, run small controlled experiments, and document learning with crisp before/after evidence. Cross-platform support across iOS, Android, and React Native means we can scale wins quickly, standardize patterns, and create a repeatable playbook for new features and markets—all while keeping the user experience coherent and respectful.

Inspired by this post on Amplitude – Best Practices.

March 11, 2026
Mastering NRR: How Great Customer Success Teams Drive Expansion, Crush Churn, and Scale PLG

Net Recurring Revenue (NRR) is the cleanest truth-teller in my operating system. When I review NRR, I’m not just looking at whether we renewed accounts—I’m assessing whether our product and customer success motions are compounding revenue from our existing customers. Put simply: good CS teams protect revenue; great CS teams grow it through adoption, expansion, and durable retention.

Here’s how I frame NRR with my teams: it reflects revenue from our current customers after expansion, downgrades, and churn. If it’s at or above 100%, the installed base is self-sustaining; if it’s materially above 100%, the base is funding growth without net-new sales. That’s the holy grail for product-led growth and the benchmark I use to separate good from great.

At HighLevel, I’ve learned that you can’t “wish” your way to high NRR. You operationalize it. We align incentives, dashboards, and rituals so everyone—from PMs to CSMs to Solutions Engineering—owns the same outcome. Our “QBRs vs OKRs” discussions anchor on NRR drivers: activation rates, time-to-value, feature adoption depth, and expansion readiness. Those leading indicators tell me where we’ll land on lagging revenue results.

The best Customer Success teams operate like product teams. They use behavioral analytics and retention analysis to segment customers by use case and maturity, then design journey mapping to move each segment from first value to habitual value. They proactively reduce risk while creating clear expansion paths—new seats, premium features, or higher-tier plans—based on real product usage, not guesswork.

Onboarding is where great NRR trajectories begin. I focus on compressing time-to-first-value and time-to-second-value because those moments create the habit loops that underpin renewal and expansion. In practice, that means targeted in-app guides, contextual product tours, and nudges that drive user activation across the “sticky” features that correlate most with long-term retention.

To make this scalable, we blend human and product-led touchpoints. CSMs run outcome-based playbooks, while the product experience handles education and reinforcement at scale. When usage signals an expansion opportunity—say, a team consistently bumps into plan limits—we generate a product-qualified expansion lead and equip the CSM with the exact value storyline and proof points to close it.

Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement.

I’ve seen this playbook move the needle. After instrumenting our key workflows and deploying targeted in-app guidance, we watched adoption of our highest-retaining features climb, risk flags surface earlier, and expansion conversations become far more data-driven. We didn’t chase shiny objects; we built a reliable pipeline of retained and expanded revenue directly from product usage.

If you’re aiming to level up NRR, start with a crisp blueprint: define the critical events that predict renewal and expansion; set activation milestones per segment; deploy in-app guides and product tours to remove friction; give CSMs a single-pane view of risk and readiness; and review NRR weekly with the same seriousness you apply to new ARR. Consistency beats intensity here.

Finally, keep the narrative simple. Your leadership story isn’t “we shipped features,” it’s “we created customer outcomes.” Tie every CS and product initiative back to NRR drivers—and make the wins visible. When teams see the direct line from great onboarding and adoption to measurable expansion, they naturally operate like a unified, product-led growth engine.

NRR rewards rigor. Treat it as the top-line health metric for your installed base, make the software do more of the teaching, and empower CS to coach to outcomes. Do that well, and you won’t just separate the good from the great—you’ll build a compounding machine.

Inspired by this post on Pendo – Best Practices.

March 11, 2026
Kill Your Darlings: Why I Sunset ‘Successful’ Products to Fuel Real Portfolio Growth

There’s a moment in every product leader’s career when the bravest decision isn’t to build—it’s to stop. That’s why the “Kill Your Darlings” theme resonated so strongly with me. In this episode of All Things Product, Teresa Torres and Petra Wille dig into the courage and craft it takes to sunset products that look successful on the surface yet quietly block your path to meaningful growth. As someone accountable for portfolio outcomes, I’ve learned that disciplined endings are often the catalyst for exceptional beginnings.

Listen to this episode on: Spotify | Apple Podcasts

The heart of the conversation is that uncomfortable middle ground between obvious failure and runaway success: products that are profitable, loved by customers, but fundamentally flatlining. Teresa shares candid stories from her own business, including a decision to cut 40% of revenue on purpose. I’ve been there—choosing to retire a “working… kind of” product to free up discovery capacity felt risky in the moment, but it created the focus we needed for durable growth.

Here’s the trap: some traction can be more dangerous than no traction at all. Early fans are not the same as durable product–market fit, and “stable but not growing” can lull leaders into maintaining instead of learning. Every hour of design, engineering, and go-to-market attention that props up a flatlining product is an hour not invested in the next breakthrough—an opportunity cost that rarely shows up on a dashboard, yet compounds month after month.

From a portfolio perspective, this is continuous discovery in action. If we want empowered product teams to tackle meaningful outcomes, we have to protect their capacity from zombie work. That means setting clear thresholds for when we double down, shift strategies, or sunset—before attachment and inertia take over. When I’ve institutionalized this discipline, our throughput of high-quality bets increased, and our confidence in what not to do became a strategic advantage.

Organization design can make sunsetting harder than it needs to be. Dedicated, long-lived teams are fantastic for compounding capability, but they also create emotional and structural ties to specific products. Petra’s point lands: leaders need explicit sunsetting conversations and a portfolio decision-making cadence that sits one level above teams. In my org, we treat sunsetting as a strategic reallocation—not a verdict on a team’s talent—so people are celebrated for learning, not punished for outcomes outside their control.

Killing profitable products can be the right strategic move when the growth ceiling is clear and the opportunity cost is high. I’ve chosen to “burn the ships (on purpose)” more than once—retiring add-ons that generated reliable revenue but diluted our value proposition and spread discovery thin. Yes, it stings in the quarter you do it. But it’s astonishing how quickly focus restores momentum when you create intentional space for what’s next.

Practically speaking, I make sunsetting easier and less traumatic by operationalizing it: Regular portfolio reviews focused on outcomes and opportunity cost; a visible “sunsetting” column so everyone sees what’s on the table; the Horizon (H1 / H2 / H3) model to balance core, adjacent, and transformational bets; and making portfolio decisions one level above teams to avoid local optimizations. Add explicit exit criteria and success metrics for endings, the same way we set entry criteria for new bets.

Another theme I appreciated is designing for the right customers. Teresa highlights intentionally limiting access and pricing to work with customers who show agency and commitment. I’ve applied the same principle: when we’re clear about who we serve and who we don’t, our product–market signal sharpens, churn narratives simplify, and roadmaps get crisper. Focus is a growth strategy.

If you’re leading a product portfolio, running discovery, or wrestling with a product that “works… kind of,” this conversation is permission to act. Product–market fit isn’t binary, and mediocre success can be the most dangerous place to stay. Sunsetting is a portfolio decision, not a team failure; teams shouldn’t be punished for reaching the end of a product’s natural lifecycle. If experimentation isn’t in your DNA, killing products will always feel traumatic—so make space for it intentionally, not passively.

Key moments and themes worth bookmarking: 00:00 – Why “kill your darlings” matters; 04:30 – The dangerous middle ground; 09:30 – The opportunity cost of “okay” products; 14:30 – Sunsetting in product organizations; 19:00 – Real examples of killing revenue streams; 28:00 – Designing for the right customers; 33:30 – Burn the ships (on purpose); 38:00 – Making sunsetting easier with Regular portfolio reviews, a visible “sunsetting” column, the Horizon (H1 / H2 / H3) model, and making portfolio decisions one level above teams; 46:00 – Normalizing product lifecycles.

Resources & Links:

Follow Teresa Torres: https://ProductTalk.org

Follow Petra Wille: https://Petra-Wille.com

Mentioned in this episode:

Ways to Work with Petra Wille

Product at Heart

CDH Membership by Teresa Torres

Product Talk by Teresa

Product Talk Academy by Teresa

Enduring Ideas: The three horizons of growth

Join the Conversation:

Have thoughts on this episode? Leave a comment below.

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Inspired by this post on Product Talk.

March 10, 2026
Turn Support Wins into a Company-Wide AI Blueprint for Consistent, End-to-End CX

Building a great end-to-end customer experience with AI means going beyond support, and I’ve seen firsthand how transformative that shift can be when we treat every interaction as part of one cohesive journey.

Every customer touchpoint, from the first sales conversation through to post-sales support and success, is an opportunity to get it right. Other teams are now turning to AI to transform how they show up for customers, and support, which led the way, has already written the blueprint. In my role, I focus on making that blueprint actionable across the entire lifecycle.

In The 2026 Customer Service Transformation Report, it’s clear most businesses are thinking about what’s next, with more than half planning to scale AI to other departments. Interestingly, they often cite their early success with AI in support as motivation for the move. This makes support teams uniquely positioned to help lead the transition, a strategic role unimaginable just two years ago.

In this piece, I share how teams are introducing AI to other parts of the business, how to think about this expansion effort, and the new opportunities it creates for support leaders who want to drive a unified customer experience.

Support was the first proving ground for AI, and our research suggests that businesses are now planning to expand its use to other areas based on the results it’s yielded so far. Fifty-two percent of respondents said that their organizations are actively planning to scale AI to other departments in 2026.

What will this look like? Leading companies are already finding out.

Wins in support are setting the pace for company-wide AI. Survey results rank the drivers: proven success in support (57%), the push for a unified customer experience (49%), scaling other functions without more headcount (33%), and cross-department demand (31%).

My favorite example is WHOOP, the fitness wearables company. They offer a premium product which makes their sales conversations more consultative than transactional. Customers want to know “Which membership is right for me?” or “How often do I need to charge my WHOOP?” According to Emily Shirley, Business Manager for Growth Product at WHOOP, if someone chatted with the inside sales team, they were twice as likely to convert, but they didn’t have enough reps to respond to incoming queries fast enough. Customers could wait more than 10 hours for a reply.

With a big product launch on the line and an anticipated spike in prospective customer conversations, their three-person team needed help. So they deployed Fin to the "Join" page, the final step before purchase.

With Fin resolving 84% of inbound questions, the sales team was able to focus on high-value leads. Together, they drove a 130% increase in attributable sales. The team is now exploring ways to expand Fin beyond FAQs, focusing on personalised conversation flows, multi-product recommendations, and richer data capture. As Emily says: “There are so many parts of the buyer journey where this applies. We’ve only scratched the surface.”

It’s clear there’s a desire to push AI to other parts of the customer lifecycle, but there is a risk hidden in this expansion. If sales, customer success, and other departments all launch their own Agent, each operating in isolation, you can end up fragmenting the very thing our research says teams want to create. The second-most cited reason for pushing AI beyond support: desire for a unified customer experience.

Without shared context, each handoff becomes a source of friction where customers could receive inconsistent answers or be asked to repeat information. I’ve watched even well-intentioned AI rollouts struggle here—great local wins, but an overall journey that feels disjointed.

A translucent UI visual maps a support-led AI blueprint that scales across the business—from SDR and sales to custom assistants—anchored by layers for goals, memory and user context, business knowledge, and interoperability.

The opportunity (and the challenge) is to keep the customer at the center. Instead of department-specific Agents that operate independently, we must strive for cohesion. That means shared memory, consistent governance, and connected AI workflows that respect the customer’s history and intent across channels.

This is the future I’m building toward: solutions like Fin becoming a “Customer Agent,” capable of handling the entire customer experience. This will mean Fin can function in many roles, supported by a memory that grows with the customer over time and deep knowledge of the business, creating a seamless experience for every interaction. In practice, that’s agentic AI designed to collaborate across teams, systems, and journeys—without losing context.

Pushing AI into new parts of the business requires someone to own the process. And for many organizations, that’s the support team. Nearly a third of respondents (32%) confirmed their customer service teams are leading their business' AI transformation strategy.

This presents a real opportunity for support teams to shape the future of customer experience. Instead of each function reinventing the wheel, support can act as a center of excellence, defining shared standards, guardrails, and operating practices that drive performance.

“You already manage the most complex, high-volume customer interactions; you have rich data on customer needs and behavior; and you know how Agents perform in the real world. Those insights will be invaluable as AI scales across your business.”

Leaders are racing ahead with real AI in support. Explore the 2026 Customer Service Transformation Report to see where deployment is stalling, benchmark your team, and get practical steps to scale automation that delights.

In my organization, when we extended AI from support into sales, we deliberately brought our conversation design expertise, Agent Analytics, and governance models along with it. One team owns the orchestration, memory strategy, and CRM integration so a customer can start with a sales question and end up with a support one—without ever feeling a seam. That continuity is where journey mapping meets product strategy and turns into measurable outcomes.

As Agents like Fin expand their capabilities and move into new areas, I expect many customer service leaders will see their roles expand to include AI implementation across the customer journey. It’s a natural progression for product management leadership in support: owning the experience, the data, and the operating model.

Achieving perfect customer experience is AI’s biggest promise. But in order to get there, teams need to be smart about the solutions they deploy. A unified Customer Agent capable of handling the entire journey end-to-end will have a significant advantage, delivering consistent, context-aware experiences across every interaction.

The Customer Agent future is being built right now, and it’s starting with the team pioneering AI transformation from the very beginning: support. For leaders in these organizations, this is a rare opportunity to shape how customer relationships will be built and maintained in the AI era.

If you’d like to dig deeper into the data and benchmarks guiding these decisions, download The 2026 Customer Service Transformation Report.

Inspired by this post on The Intercom Blog.

March 5, 2026
Stop Selling Your Roadmap: Win Stakeholder Trust by Showing Your Work, Not Conclusions

I’m seeing the same pattern in product orgs everywhere—inside HighLevel and across my network: everyone is racing to add AI to the roadmap, and every stakeholder has a strong opinion about what to build next. Delivery has never been faster, which makes it dangerously easy to confuse speed with progress.

When we chase features without grounding in continuous discovery, we drift back into a feature factory. We ship more, but we ship the wrong things faster. The antidote is simple and hard at the same time: recommit to product discovery, validate with assumption testing, and let the evidence steer our AI Strategy—not the hype.

Of course, that only works if we can bring our stakeholders along. In the AI moment, it’s deceptively easy to get to a slick prototype and painfully hard to harden it for production. Early demos make almost any idea look promising. That’s precisely why stakeholder management must evolve from pitching solutions to showing our work.

In practice, stakeholder management is about alignment with the people who influence our product decisions—executives, sales, marketing, customer success, engineering leadership, and sometimes legal or finance. Some have veto power; others have input. Knowing who can block versus who can shape is crucial for where we spend our time. Even in empowered product trios, the best discovery can derail if we reveal only conclusions at the end.

I’ve tried every mapping framework—power-interest grids, RACI matrices—and they help. But the real challenge isn’t identifying stakeholders. It’s figuring out how to bring them along so that our product roadmapping and sprint planning decisions stick.

Identify who shapes your product decisions. This visual groups stakeholders into three tiers—those with veto power, key influencers, and audiences to inform—so teams can align, communicate, and reduce delivery risk.

Here’s the most common trap I see (and have fallen into): focusing stakeholder reviews on the roadmap, release plan, or prioritized backlog. That invites an opinion battle. And stakeholders have their own conclusions—usually shaped by the last customer call, a board meeting, or a market headline.

This is how the HiPPO dynamic gets created. HiPPO stands for the “Highest Paid Person’s Opinion,” and the saying goes, “The HiPPO always wins.” When we present conclusions without the journey, we set ourselves up to lose. In the gen ai rush, the chorus of “everyone is doing AI” makes that opinion even harder to counter.

So I don’t try to win opinion battles. I bring new information—fresh customer interviews, clear opportunity mapping, and results from assumption tests. The gap between what the market hypes and what customers actually need is often enormous. Our edge is evidence.

The strategy that consistently works for me is simple: show your work. If you’re practicing continuous discovery, your opportunity solution tree isn’t just a thinking tool—it’s your strongest stakeholder management asset. It helps you build confidence in your decisions, and it can help your stakeholders build the same confidence.

Avoid the stakeholder trap of selling conclusions. This visual shows how anchoring on solutions invites HiPPO battles—and how to shift the conversation by sharing discovery evidence, insights, and data.

Step 1 — Start with the outcome. I open every conversation by restating the shared goal and asking whether anything has changed. Anchoring on outcomes vs output OKRs reframes hot-button solution debates (like “we need an AI feature”) back to what will move the needle on the outcome we agreed to pursue.

Step 2 — Share the opportunity space. I show how we mapped customer needs, pain points, and desires. Then I ask, “What did we miss?” Stakeholders often surface opportunities we haven’t seen yet—signals from the field, market shifts, or partner feedback. I capture their input and commit to validating it in upcoming customer interviews.

Step 3 — Walk through prioritization. Using the tree’s structure, I explain why we prioritized one branch over another. Then I ask where they might have chosen differently. This turns debate into collaboration and lets me leverage their expertise without ceding the discovery framework.

Step 4 — Go deep on the target opportunity. Before we talk solutions, I make the customer’s problem vivid and real. Interview snapshots help stakeholders empathize and see what matters most. Once the opportunity is crisp, solution discussions become dramatically more objective.

Show your work, not just your conclusions. This infographic guides product teams through seven steps to build stakeholder confidence—align on outcomes, map opportunities, prioritize, test assumptions, and repeat.

Step 5 — Share solutions and invite theirs. I present our solution set and explicitly ask for additional ideas. If their suggestions diversify our set, we include them. Solution ideas are cheap; the opportunity is what matters. This is where product trios can benefit from leadership’s pattern recognition and industry context.

Step 6 — Share your assumption tests and results. I walk through our story maps, high-risk assumptions, and what we’ve learned so far. I invite stakeholders to add assumptions—this is where their knowledge shines. If we have data, we share it; if we’re pre-data, we share the plan to get it and ask for feedback.

Step 7 — Repeat. I don’t batch this into a big reveal. I keep a steady cadence and tailor depth to each audience: weekly for my manager, monthly highlights for marketing, and concise updates for executives. Continuous discovery pairs with continuous stakeholder management.

Showing your work doesn’t mean drowning people in detail. It means tailoring the signal to the audience. My rule of thumb is outcome, opportunity, solution, evidence—walk the lines of the tree at the right altitude for each stakeholder.

Show your work the right way for each stakeholder. Use a smart filter to turn discovery noise into clear signals—weekly journeys for your manager, focused monthly highlights for marketing, and a 30-second CEO pitch.

In a 30-second update with a CEO, it might sound like this:

“Our goal is to reduce time-to-first-value for new users. We’ve been interviewing customers and learned that onboarding is where most people get stuck—specifically, they don’t know which features to try first. We explored a few approaches and tested them. The most promising one is a guided setup flow that adapts based on the user’s role. In early tests, new users completed onboarding 40% faster.”

That pattern works across channels—Slack updates, monthly reviews, or quarterly planning. The format flexes, the structure doesn’t: outcome, opportunity, solution, evidence.

As you adopt this approach, watch for four anti-patterns that quietly erode trust.

Avoid the traps that erode stakeholder trust. This infographic guides product teams to show their work, welcome ideas, provide frequent updates, and prioritize results over ideology to build alignment and credibility.

Anti-pattern 1 — Telling instead of showing. The curse of knowledge makes our conclusions feel obvious to us and opaque to others. The fix: slow down, start at the top of the tree, walk the decisions, and let stakeholders reach the conclusion with you.

Anti-pattern 2 — Shooting down stakeholder ideas. As you build a library of validated assumptions, it’s easy to spot flaws in a suggestion and say “no” too quickly. Instead, place their idea within your discovery framework. If it maps to a different opportunity, say, “That idea has promise—we’ll consider it when we address that opportunity.” If it rests on risky assumptions, story map the idea together, list the assumptions, and share what you’ve already learned. People accept the evidence they help generate.

Anti-pattern 3 — Saving everything for a big reveal. Infrequent, comprehensive updates invite opinion battles because stakeholders have formed their own conclusions in the dark. Short, frequent updates build alignment as the work unfolds.

Anti-pattern 4 — Fighting the ideological war. Sometimes a more senior stakeholder will overrule you. Don’t turn it into a debate about how product decisions “should” be made. Focus on the decision at hand, do the best work within constraints, and let results—not ideology—prove the value of discovery over time.

Shift from selling to showing. This co-creation guide invites stakeholders into discovery, taps their expertise, and turns relationships from obstacles into partnerships for smarter product decisions.

Here’s the mindset shift that changes everything: stakeholder management is a co-creation opportunity. When we show our work with artifacts like an opportunity solution tree, experience maps, and interview snapshots, we’re not just communicating—we’re inviting collaboration. We’re leveraging stakeholders’ expertise, context, and connections to make better product decisions.

When stakeholders have walked the path with us, they don’t need to be sold on the destination. They become allies. Engagement stops being a status ritual and starts being real partnership—the kind that moves outcomes and builds durable trust.

Try this in your next review: don’t start with your roadmap. Start at the top of the tree. Reaffirm the outcome. Share the opportunity space. Explain your prioritization. Show what you’re learning. Invite contribution. You might be surprised how quickly alignment—and confidence—follow when you stop selling conclusions and start showing your work.

Inspired by this post on Product Talk.

March 4, 2026
From Chaos to Clarity: My Proven Playbook to Scale an Analytics Taxonomy That Sticks

I’ve stepped into too many product reviews where teams argued over numbers that should have been obvious. Three names for the same “signup” event, properties scattered across tools, and no shared definitions—classic analytics chaos. As VP of Product Management at HighLevel, I’ve learned that scaling an analytics taxonomy isn’t just a data exercise; it’s a leadership mandate that unlocks decision velocity, alignment, and confident product bets.

Learn best practices our professional services team has compiled in helping customers move from scattered events to a scalable, user-friendly data structure.

Why does this matter so much? A robust taxonomy powers a unified analytics platform across Amplitude analytics, Pendo, and our CRM stack, reduces rework, and strengthens data governance. When events are clear and consistent, product-led growth accelerates: onboarding becomes measurable, activation is trackable, and retention analysis turns into a weekly ritual rather than a quarterly scramble.

I always start with outcomes, not events. We define a North Star metric and use driver trees to map how user behaviors ladder up to that outcome. Then we ground the plan in journey mapping: what signals mark activation, aha moments, and long-term engagement? This ensures our taxonomy mirrors real user intent, not just engineering convenience.

Next comes naming conventions and structure. We standardize on a readable, durable pattern (for example, actor_action_object), apply consistent property naming, and document required vs. optional properties. We version events deliberately, so we can evolve without breaking dashboards. Most importantly, we align events to product strategy—tracking less, but better.

Governance makes it scale. We establish a clear DRI for the tracking plan, a lightweight review process for changes, and a schema registry that serves as the single source of truth. Privacy-by-design is non-negotiable: we treat sensitive fields deliberately and audit access. Observability closes the loop—schema validations and alerts catch drift before it confuses teams.

Tooling and process turn good intentions into muscle memory. We keep the tracking plan “as code” in a repository, run CI/CD checks to validate events, and use feature flags to roll out new instrumentation safely. Pendo helps us annotate in-app experiences, while Amplitude provides the exploratory lens for cohorts, funnels, and retention. Together, these systems reduce guesswork and speed up discovery.

Migrations are where many teams stall, so I de-risk them with a clear, time-boxed plan. We audit the current event surface, map scattered events to the new taxonomy, and deprecate duplicates with guardrails. We communicate changes broadly, provide easy-to-scan documentation, and pair enablement sessions with hands-on examples from live dashboards. The goal is confidence, not just compliance.

We measure success like a product. Are we answering critical questions faster? Are duplicate events trending down? Are activation and retention questions easy to answer in under five minutes? When the taxonomy is working, stakeholders stop asking, “Do we trust this?” and start asking, “What should we build next?”

One of the most rewarding shifts I’ve seen: product trios moving from ad-hoc analyses to repeatable, weekly rituals. With crisp definitions, onboarding flows become testable, PLG motions are predictable, and leadership reviews focus on outcomes, not definitions. That’s the moment analytics transforms from a cost center into a growth engine.

If you’re staring at a wall of scattered events, start small: clarify outcomes, align your journey map, set conventions, and ship a minimum viable taxonomy to one critical flow. Iterate quickly. The compounding payoff—clarity, speed, and trust—will be obvious to every team you partner with.

When we do this well, analytics becomes a strategic asset. Our teams spend less time reconciling numbers and more time building what matters. That’s the real meaning of moving from chaos to clarity.

Inspired by this post on Amplitude – Best Practices.

March 3, 2026
Lost in the Woods: 5 Survival Patterns Every Product Leader Must Master Now

Ever feel like your product team is “lost in the woods”? I’ve certainly been there—when strategy gets fuzzy, outcomes drift, or constraints aren’t clear. What helped me reframe the chaos was borrowing “lost person” patterns from search-and-rescue and mapping them to product strategy, product discovery, and team behaviors. The result is a practical playbook for product management leadership that keeps empowered product teams moving toward outcomes—not just outputs.

Listen to this episode on: Spotify | Apple Podcasts

Here are the five patterns I see most often—and how I turn each one into forward motion: settle in place (freeze), chase shortcuts, follow the first visible path, use your own navigation (intuition/taste), and retrace your steps. Each of these has a smart, minimal move that helps teams reorient fast without abandoning continuous discovery or product strategy discipline.

Settle in place (freeze). Sometimes the smartest move is to stop. When my team lacks context or authority, I pause delivery work and escalate instead of improvising fixes. This prevents thrash, protects focus, and creates the air cover we need to realign outcomes vs output OKRs.

Chase shortcuts. Shortcuts can be brilliant—or overconfident. I’ve learned to pressure-test whether the “road” is where we think it is before we commit. That means lightweight experiments, clear exit criteria, and the humility to pivot. Think about big bets like Spotify podcasts: compelling vision, but you still have to validate assumptions step by step.

Follow the first visible path. The obvious option isn’t always the best one. My job as a product leader is to make multiple paths visible before we choose. I lean on opportunity solution trees and KPI trees (or driver trees) to surface alternatives, align stakeholders, and keep empowered product teams focused on customer impact and product-market fit—not just the loudest idea.

Use your own navigation (intuition/taste). Judgment matters, especially for product trios making fast calls—but it’s not a replacement for evidence. When my “compass” conflicts with what we observe, I anchor back to customer interviews, rapid tests, and discovery loops. Intuition should guide where we look, while data validates how we proceed.

Retrace your steps. When we’re drifting, I go back to what used to work: principles, quality practices, and discovery habits as feedback loops. Returning to fundamentals—clear problem statements, crisp value propositions, and disciplined outcomes—rebuilds momentum fast.

Team prompt to try: If your team is “lost” right now, which pattern are you defaulting to—and what’s the smallest move you can make this week to get oriented (escalate, test a shortcut, map options, validate intuition with evidence, or retrace to a principle)? I use this question in weekly reviews to keep us grounded in continuous discovery and product strategy.

Resources & Links:

Follow Teresa Torres: https://ProductTalk.org

Follow Petra Wille: https://Petra-Wille.com

Mentioned in the episode:

Lost Person Behavior: A Search and Rescue Guide on Where to Look – for Land, Air and Water

Robert J. Koester

Examples referenced: Xerox, Nokia, Kodak, Volkswagen emissions scandal, Spotify podcasts, large-org tooling contexts like Oracle and SAP

Opportunity Solution Trees: Visualize Your Discovery to Stay Aligned and Drive Outcomes

KPI Trees: How to Bridge the Gap Between Customer Behavior, Product Metrics, and Company Goals

Let's Read Continuous Discovery Habits Together (January 2026) for Continuous Discovery Habits (and the idea of habits as feedback loops)

Shifting from Outputs to Outcomes: Why It Matters and How to Get Started

I’d love to hear how your team navigates these patterns. Which small move will you try this week? Leave a comment below and let’s compare notes on product discovery, stakeholder management, and product roadmapping that actually drives outcomes.

Inspired by this post on Product Talk.

March 3, 2026
March CDH Book Club: Master Experience Mapping to Align Teams and Accelerate Discovery

I’m thrilled to invite you to our March session of the CDH Book Club. Continuous Discovery Habits turns five this year. And to celebrate we are reading the book together. I’ve seen firsthand—leading product trios and empowered product teams—that sharpening our discovery habits is the fastest way to better outcomes vs output OKRs, tighter team alignment, and more confident product strategy.

Each month, I am releasing an in-depth reading guide that includes:

The chapters we will be reading

A preview of the most important concepts we'll be learning about

Short videos you can share with friends and colleagues to help spread the ideas

Individual and team discussion questions to help you absorb and engage with the reading

Team exercises to help you put the ideas into practice

Additional reading to help you go deeper on the core ideas

We’ll be discussing each month’s reading in the comment section and we’ll gather quarterly to discuss on a live call. I’ll be there to trade notes, compare experience maps, and share what’s working across product discovery practices.

Joining late? No problem. I monitor the comments on each reading guide throughout the year. Start with the current month or go back to January—whatever works for you. You can ask for help, share what’s working, and connect with other readers at any point.

If you want to participate, grab a copy of the book (or dig up your old copy), share the "Spread the Love" videos, reserve some time to do the team exercises, and register for the community sessions. Let’s do this!

This Month’s Reading

Chapters:

Chapter 4: Visualizing What You Already Know

Estimated reading time: ~14 minutes

This chapter will introduce you to:

Why starting individually—rather than as a group—is the fastest path to unlocking your team’s collective intelligence

How drawing (even badly) forces you to get specific in ways that words never will

The strategic choice of setting your experience map’s scope—too narrow and you miss opportunities, too broad and you lose focus

How diverse perspectives become your team’s secret weapon when you know how to synthesize them

Why your first experience map isn’t truth—it’s a hypothesis you’ll test and evolve with every customer conversation

Need a copy? Grab the book.

Share the Love with Friends and Colleagues

We learn best in community. Use the following short videos to share the key concepts from this chapter with friends and colleagues. Invite them to participate in the book club with you. In my teams, these quick hits help us align faster before we co-create an experience map or opportunity solution tree.

Visualize your thinking – To bring others along

Unlock team alignment – With visualizations

Reflect & Discuss What You Read

When we reflect and discuss what we read, we absorb more of the material. It helps us put what we learn into practice. Don’t skip this step. In my own practice, the real unlock came when I treated mapping as a living artifact that shapes customer interviews, not a one-off deliverable.

Most of us believe we work collaboratively, but we’ve never truly experienced what it means to build shared understanding from diverse perspectives. This chapter challenges you to get uncomfortable—to draw when you’d rather talk, to work alone before working together, and to see your maps as living documents rather than one-time deliverables.

Individual Reflection

Think about the last time your team tried to align on what you know about your customers. Did everyone start by creating their own perspective first, or did you jump straight into a group discussion? What happened as a result?

When was the last time you drew something at work? What stops you from using drawing as a thinking tool—is it discomfort with your drawing skills, lack of time, or something else?

Look at your current work. If you were to create an experience map right now, what scope would you choose? How does your desired outcome help you determine what to include and what to leave out?

Team Discussion

As a trio, each person should identify one unique perspective they bring to your team’s understanding of your customer. How might these different viewpoints create blind spots if you only relied on one person’s view?

When your team disagrees about what customers need or want, how do you typically resolve it? Do you debate until someone wins, defer to the most senior person, or test your different hypotheses?

Does your team have a current experience map? If so, when was the last time you updated it based on what you’re learning from customers? If not, what’s preventing you from creating one?

Put It Into Practice

Understanding why experience maps matter is different from actually creating one that drives your discovery work. These exercises will help you practice the discipline of starting individually, synthesizing diverse perspectives, and using your map to guide customer conversations. My suggestion: timebox, embrace imperfect drawings, and let the artifact lead your next interview script.

Exercise: Create Your Individual Experience Maps

Time: 20 minutes individually, 45–60 minutes with your team

Do this: Individually first, then share with your trio

Start by agreeing on the scope of your experience map based on your current outcome. Each member of your trio should then independently create their own experience map using pen and paper (or your favorite digital drawing tool).

Focus on drawing the customer’s experience, not your product’s features. Where do they get stuck? What goes wrong? How do they work around problems? Don’t worry about drawing well—boxes, arrows, and stick figures are perfectly fine.

Once everyone has created their individual maps, schedule time to share them with each other. As you explore each person’s perspective, ask questions to understand their thinking. Pay particular attention to the differences between maps—this is where the richest insights emerge.

Exercise: Co-Create Your Shared Experience Map

Time: 30 minutes with your team

Do this: With your product trio

Bring your individual experience maps together and work to synthesize them into a single shared map. Start by identifying all the unique nodes (distinct moments, actions, or events) across all three maps. Arrange them in a comprehensive flow.

Collapse similar nodes, but be careful not to overgeneralize. Add links to show relationships and flow between nodes—including loops, error cases, and abandonment points. Finally, add context about what customers are thinking, feeling, and doing at each step.

As you work, avoid getting bogged down in endless debate. If you disagree about details, draw out the difference rather than debating it. This often reveals you already agree or helps you pinpoint exactly where your understanding differs.

Remember: This map is your current hypothesis about your customer’s experience. Use it to guide your upcoming customer interviews and plan to evolve it based on what you learn.

Go Deeper: Additional Reading

If you prefer an audio summary of this month’s reading, including the book chapters and the following resources, I’ve included an audio version for paid subscribers at the bottom of this post.

Supplementary Reading

Why Drawing Maps Sharpens Your Thinking

Core Concept: Collaborative Decision-Making in a Product Trio

Other Voices

To Draw or Not to Draw: Is Traditional Sketching Still Relevant in the Digital Design Era? by Julia Ku

Journey-Mapping Approaches: 2 Critical Decisions to Make Before You Begin by Kate Kaplan

The Visual Language of Comic Books Can Improve Brain Health by Mary Widdicks

Mapping Your User’s Day with the User Clock Sketch by Ben Crothers

Our Live Discussion Schedule

Our live discussion sessions are for paid subscribers. Sessions are not recorded. Invitations will go out to Supporting Members and CDH Members two weeks before the scheduled event. But reserve the time on your calendar now.

Wednesday, March 18, 2026: 9am–10am PDT and 4pm–5pm PDT

Tuesday, June 16, 2026: 9am–10am PDT and 4pm–5pm PDT

Thursday, September 17, 2026: 9am–10am PDT and 4pm–5pm PDT

Wednesday, December 16, 2026: 9am–10am PST and 4pm–5pm PST

Audio Summary

This summary was produced by NotebookLM. The sources supplied were the book chapters as well as all of the additional reading.

Listen here: March — Draw the User Clock to Build Empathy (audio)

This article is part of the CDH Book Club celebrating the five-year anniversary of Continuous Discovery Habits. See all book club posts.

Inspired by this post on Product Talk.

March 2, 2026