I build products at scale, and I write about how we make them successful. When I need a clear, evidence-based perspective on what actually drives outcomes, I turn to the Amplitude Blog. It’s a dependable source for sharpening my thinking on "digital analytics, product strategy, and product-led growth"—and it consistently helps me translate analytics into business impact.
What keeps me coming back is the way practical, well-structured guidance meets real-world constraints. Whether I’m refining our event taxonomy in Amplitude analytics, evaluating a unified analytics platform approach, or aligning stakeholders on the right success metrics, I find concrete patterns I can apply immediately. The content connects data literacy with product management leadership, the exact combination required to navigate complex roadmaps and high-stakes decisions.
Here’s how I apply these insights day to day. I anchor our experiments in A/B testing best practices and set a minimum detectable effect that matches our traffic realities. I guide teams to prioritize user activation and retention analysis over vanity metrics, and I frame plans with outcomes vs output OKRs so we stay focused on customer and business value. In parallel, I reinforce continuous discovery and product discovery habits—feeding learning back into product roadmapping and sprint planning without losing speed.
The payoff shows up in the details: better funnel instrumentation, cleaner cohorts, and faster hypothesis cycles that reduce rework. When we operationalize these ideas—tying activation to onboarding flows, clarifying value moments, and aligning cross-functional owners—we see measurable lifts without bloating scope. That’s the discipline I expect from a modern, product-led growth motion: rigorous analytics paired with empowered execution.
If you’re scaling a team or modernizing your analytics practice, make the Amplitude Blog part of your weekly ritual. Use it to pressure-test your strategy, level up experimentation, and build a shared language for data-informed decisions. The right "tips and examples" can save months of trial and error—and, more importantly, help you ship products that customers return to again and again.
Inspired by this post on Amplitude – Best Practices.
I’ve felt the drag of data bottlenecks firsthand—PMs waiting on a reporting queue, engineers guessing at success metrics, and stakeholders making decisions with partial context. The “Add Data to Cart” mindset changed the game for me: make high-quality data as easy to request, enrich, and consume as dropping an item into a shopping cart.
Learn how Ankorstore’s teams make autonomous decisions, leveraging enriched data from Amplitude to accelerate feature delivery and drive topline growth.
Here’s what resonates with me and how I apply it in practice. When teams get self-serve access to a unified analytics platform like Amplitude analytics, decision autonomy becomes the default. Product trios operate with clarity, discovery cycles tighten, and we ship with confidence because the evidence is visible to everyone, not buried in a backlog.
The foundation is a clean, shared event taxonomy. I prioritize naming conventions, consistent properties, and governance so we can enrich events once and reuse them across A/B testing, retention analysis, and user activation dashboards. This lets product managers answer critical questions—Who’s activating? Which cohorts retain? Which journeys convert?—without waiting on an analyst, while still preserving data quality.
In my teams, “Add Data to Cart” means we treat data like a product. If a feature team needs a new event or property, they can request it with clear definitions, privacy requirements, and owners. We standardize the instrumentation pattern, ship it through CI/CD, document the event, and surface it in curated Amplitude reports. The result is faster feature delivery and fewer ad-hoc asks.
The payoff shows up in everyday decisions. Product managers run A/B tests with a minimum detectable effect (MDE) they can justify, analysts focus on deeper insights instead of ad-hoc tickets, and engineers get immediate feedback loops post-release. It’s a blueprint for product-led growth: know what moves activation, double down on the paths that retain, and sunset the work that doesn’t move outcomes.
Governance matters as much as speed. I pair data governance with privacy-by-design so teams can move quickly without risking compliance or eroding trust. That means documented event definitions, role-based access, and well-labeled dashboards that steer people to the right sources of truth.
If you’re starting from scratch, begin small: instrument a single critical flow end to end, publish three core dashboards everyone can find, and hold weekly readouts where teams share what changed because of the data. Within a few sprints, the habit forms—questions get sharper, hypotheses improve, and the roadmap shifts from output to outcomes.
“Add Data to Cart” isn’t just a catchy phrase; it’s a practical way to empower product teams. With enriched data in Amplitude, autonomous decisions become the norm, discovery accelerates, and growth compounds because every iteration is informed by what customers actually do.
Inspired by this post on Amplitude – Best Practices.
I love when a bold platform bet translates into tangible product impact. Watching a team commit to a unified analytics platform and then operationalize it across the business is a master class in strategic focus and change management. That’s exactly what this story captures—and why it resonates with my own experience leading complex analytics migrations.
Learn how Dan Grainger led Haven's migration to Amplitude, focusing on user-friendly analytics and data governance for non-technical teams.
That single sentence distills what matters most: if analytics aren’t accessible to non-technical teams, you won’t get the adoption needed to drive outcomes. “User-friendly analytics” isn’t window dressing; it’s the linchpin for empowered product teams and true product-led growth. When teams can ask and answer their own questions—without waiting on analysts—velocity and quality of decision-making improve immediately.
From a product management lens, two elements stand out. First, the choice of Amplitude analytics as the central system of insight—consolidating scattered tools into a unified analytics platform—creates one source of truth for activation, adoption, and retention analysis. Second, a rigorous approach to data governance ensures that trust in the data scales alongside usage, especially for non-technical stakeholders who need clarity, not caveats.
Execution matters. In my playbook, these transformations succeed when you treat them as product initiatives, not IT projects. I partner early with stakeholder management champions, form product trios to define the measurement plan, and use in-app guides, product tours, and targeted onboarding to drive behavior change. The goal is simple: shorten time-to-insight for frontline teams while keeping the instrumentation robust and consistent.
Data governance is the quiet force multiplier. Clear tracking plans, consistent event taxonomies, role-based access, and privacy-by-design guardrails prevent entropy. When everyone speaks the same analytics language, you avoid “metric du jour” debates and keep the focus on outcomes vs output OKRs. That’s where scalable impact comes from.
Measurement closes the loop. I’ve found that when non-technical teams can self-serve retention analysis, funnel drop-off, and user activation patterns, they start running continuous discovery by default—asking better questions, testing smarter hypotheses, and accelerating learning cycles. Amplitude’s strength is not just visualizing what happened, but making it easy to connect behavior to outcomes teams care about.
The broader leadership lesson is straightforward: choose a platform that your broadest set of contributors can and will use daily, invest early in governance, and build enablement into your rollout plan. That’s how a migration becomes a multiplier. When the right platform meets the right operating model, the win is less about a tool and more about a learning culture that compounding value over time.
If your analytics stack feels fragmented or underused, this is your nudge. Align on a unified analytics platform, meet teams where they are with user-friendly analytics, and let governance do the heavy lifting behind the scenes. The payoff—in speed, alignment, and smarter bets—comes faster than most teams expect.
Inspired by this post on Amplitude – Best Practices.
I recently revisited a topic I once pushed back on: using AI to analyze (and maybe even synthesize) customer interviews. After six months of real-world experiments and countless conversations with seasoned product leaders, I’ve evolved my perspective. There is meaningful value here—but only when we’re clear about where AI helps and where it quietly erodes the hard-won customer understanding that powers great product decisions.
If you want to experience the conversation that sparked this reflection, you can listen to the episode on Spotify or Apple Podcast, and watch the discussion here: YouTube. It’s a candid, practical exploration of AI’s role in continuous discovery, and it mirrors what I’m seeing on the ground with product trios and empowered product teams.
Here’s the crux: AI raises the floor for beginners but accelerates experts even more. That matches my experience—early-career PMs get structure, momentum, and a confidence boost, while experienced interviewers can move faster without sacrificing nuance. But there’s a catch. If your interviewing skills aren’t solid yet, AI can create a veneer of insight that masks shallow understanding. In other words, it can help you go wrong more efficiently.
The conversation makes an important distinction between analysis and synthesis. Analysis is about extracting signals from the interview. Synthesis is about building meaning—connecting patterns, weighing contradictions, and deciding what to do next. AI can speed up the former with summaries and highlights. The latter—true synthesis—still demands expert judgment, context, and empathy.
One line from the episode stuck with me: your unpolished interview skills matter more than any shiny new AI workflow. I’ve felt that firsthand. When interview quality is uneven, dropping transcripts into an LLM won’t save you. You still need to synthesize every interview individually so the signals remain traceable and credible. That discipline keeps teams aligned, prevents overfitting to noise, and builds the organizational memory that fuels better bets.
We also explored the operational reality most teams face: interviews pile up. Backlogs grow. Leaders want speed. This is where “expert + AI” shines. With the right prompts, templates, and context, tools like ChatGPT and Claude can help transform raw transcripts into structured artifacts you can trust—provided a strong interviewer sets the frame and makes the calls. That balance preserves both velocity and quality.
What changed my mind most was the evidence from experiments—running sets of interviews through different LLMs and comparing outcomes. The patterns were consistent: beginner + AI is usually better than nothing, but the real performance gains come from expert + AI. When experts guide the process, AI becomes an accelerant rather than a crutch.
A favorite story in the episode takes a detour into building a gaming PC—an unexpected but perfect metaphor for AI’s limits. You can get great step-by-step guidance from a model, but when context shifts or edge cases appear, expertise is what keeps you from making expensive mistakes. Customer interviews are like that. Empathy comes from human interaction; AI can’t replace the experience of talking directly to your customers.
My practical guidance for teams integrating AI into continuous discovery: start with interviewing fundamentals, separate analysis from synthesis, and standardize how you capture single-interview learnings. If you need a tight template for this, refer to “The Interview Snapshot: How to Synthesize and Share What You Learned from a Single Customer Interview.” Use AI for summaries, clustering, and draft artifacts—but have an expert finalize the narratives, evaluate trade-offs, and document assumptions.
If you’re scaling this across an organization, invest in training first, then in workflows. Build a lightweight operating system for discovery: consistent interview guides, “story-based” techniques, and a shared library of prompts. Consider resources like “The Interview Coach,” as well as practical write-ups such as “Customer Interview Analysis: Where AI Helps and Hurts.” These help teams avoid common pitfalls and make better use of AI in high-judgment moments.
My bottom line: AI isn’t magic. It can help, but only if your interviews are strong and you provide the right context. Customer understanding is a competitive moat; outsourcing it entirely will cost you in the long run. Use AI to accelerate—not replace—the human judgment that makes product discovery work.
Resources and links worth exploring: ChatGPT, Claude, The Interview Snapshot: How to Synthesize and Share What You Learned from a Single Customer Interview, The Interview Coach, and Customer Interview Analysis: Where AI Helps and Hurts.
I’d love to hear how your team is using AI in discovery. What’s working, what’s risky, and where do you draw the line between automation and judgment? Share your experiences in the comments—our community learns faster when we compare notes.
In my role leading product teams, the AI product roadmap isn’t just a plan—it’s the operating system for how we discover value, prioritize with rigor, and ship with confidence. The pace has changed, the stakes are higher, and the best product managers are now orchestrating AI capabilities, data, and customer insight in near-real time.
Master the evolving art of the AI product roadmap. Prioritize smarter, turn data into direction and insight into action, only much faster.
When I say “AI product roadmap,” I’m talking about a living system that blends strategy, discovery, and delivery. It’s less about dates and more about outcomes, risk reduction, and sequencing learning. In practice, that means combining AI Strategy with product roadmapping and sprint planning, then validating each bet with real customer signals.
For prioritization, I anchor on outcomes vs output OKRs and connect them to measurable signals across the funnel. Continuous discovery keeps insights flowing, while a unified approach to analytics and retention analysis tells me where the lift is. This lets me rank initiatives not just by impact and effort, but by how quickly we can learn, iterate, and compound value.
On discovery, product trios are non-negotiable. We prototype early with gen ai and LLMs for product managers to accelerate concept validation and reduce ambiguity. When customers can co-create through in-app guides or lightweight product tours, we turn vague needs into crisp problem statements and testable hypotheses far faster.
On delivery, I pair tight feedback loops with experimentation. A deliberate cadence of A/B testing and strong instrumentation ensures we’re learning every sprint, not just launching. The goal is to de-risk decisions quickly, keep momentum high, and translate signals into roadmap movement without thrash.
Under the hood, the AI stack matters. I rely on a retrieval-first pipeline to ground models in trusted data, and I’m intentional about privacy-by-design and data governance from day one. As agentic AI patterns emerge, I put evaluation workflows in place so we can ship confidently—and safely—without slowing down innovation.
Finally, alignment is the multiplier. Clear narrative roadmaps tied to customer outcomes help stakeholders see trade-offs, while crisp interfaces with go-to-market and CRM integration close the loop from roadmap to revenue. When everyone can trace a line from AI strategy to shipped value, prioritization becomes easier and trust grows.
If you’re feeling the acceleration, you’re not alone. With the right AI product toolbox—rooted in discovery, grounded in data, and delivered through tight feedback loops—you can move faster, learn smarter, and build products your customers can’t live without.
When I think about how I stay sharp as a product leader, one principle anchors my approach: design your learning system—don’t leave it to chance. Communities of practice are that system. They turn curiosity into a habit, accelerate product discovery, and strengthen product management leadership across empowered product teams.
I recently dug into a powerful conversation on the All Things Product podcast that explores how product people can intentionally design their own communities of practice—and why that matters for long-term learning and growth. The insights apply whether you operate as an independent coach or you’re scaling continuous discovery inside a product org.
I appreciated the contrast in learning styles. Teresa shares an introvert-friendly approach to continuous learning: curating a personal learning network (PLN) filled with people she wants to learn from. Petra contrasts that with a more collaborative style—learning with others through small peer groups, hackathons, and local meetups. Together, they unpack how each approach supports curiosity-driven development, how to find your “definition of good” when starting something new, and the habits that make learning a deliberate practice.
In my own practice leading product trios and shaping outcomes over output, I rotate between these modes. When I need speed or depth on topics like product discovery or stakeholder management, I learn from people: I curate a tight set of voices, reverse-engineer their decisions, and study how they frame trade-offs. When I need new patterns or accountability, I learn with people: I form small peer circles to review experiments, pressure-test roadmaps, and critique discovery plans. Both paths create momentum—one by focus, the other by feedback.
Key takeaways I’m acting on right now:
– What a “community of practice” really means in modern product work: the infrastructure that makes continuous discovery sustainable—and keeps empowered product teams aligned on craft.
– The difference between learning from people vs learning with people—and when to use each depending on whether you need depth, breadth, or accountability.
– How to find like-minded peers for collaborative learning: start with one person you respect, ask who they regularly spar with, attend one local meetup with a clear learning goal, and follow up with a structured exchange.
– Building your Personal Learning Network (PLN): set a theme (e.g., pricing, product roadmapping and sprint planning), prune it quarterly, and track “who I’m learning from” with the same rigor you track stakeholders.
– Personal knowledge management as a product skill: treat notes, highlights, and artifacts as a system, not a junk drawer—so insights compound and are easy to retrieve when you need them.
– Why curiosity-driven learning builds stronger product intuition: schedule time for curiosity and socialize it with peers so it scales beyond individual motivation.
– How committing to talks, books, or courses drives deeper learning: public commitments create productive pressure and force you to clarify your thinking.
Here’s the simple playbook I use with my team: define a quarterly learning theme; curate a small PLN aligned to that theme; assemble a peer circle (PM, Design, Eng) for monthly critiques; commit to shipping one artifact publicly (a talk, guide, or internal workshop); and close the loop with a short write-up on what changed in our decisions, discovery cadence, or bets. It’s lightweight, measurable, and fits neatly alongside product-led growth priorities.
Two quotes from the discussion capture the spirit perfectly:
“Nobody on that list knows they’re in my personal community of practice.” — Teresa Torres
“Sometimes you don’t know your new definition of good until you start learning.” — Petra Wille
If you’d like to go deeper, you can listen to the episode on your favorite platform:
Listen to this episode on: Spotify | Apple Podcasts
Curation – All Things Product with Teresa & Petra episode
Hamel’s Blog
AI Evals for Engineers and PMs course by Hamel Husain (get 35% off through Teresa’s link) on Maven
Harold Jarche’s Personal Knowledge Management workshop
Petra’s book, Strong Product Communities – The Essential Guide to Product Communities of Practice
I’d love to hear how you’re designing your own community of practice. What’s your learning theme this quarter? Which peers are you building with, and what commitments are helping you go deeper? Drop your thoughts—I’ll share my own PLN stack and peer-circle cadence in a future post.
I’ve spent years helping talented engineers explore what’s next when pure coding no longer feels like the only—or best—path. From hiring across cross-functional teams to mentoring career pivots, I’ve seen firsthand how engineering strengths translate into high-leverage roles that shape product, strategy, and growth.
Software engineers have alternative career options leveraging their skills in roles like product manager, data scientist, business analyst, and 22 more.
When an engineer moves into product management, they’re not starting from scratch—they’re redirecting problem-solving, systems thinking, and customer empathy toward outcomes. In practice, that means mastering product discovery, strengthening stakeholder management, and getting fluent in product roadmapping and sprint planning, so decisions are guided by impact rather than “outputs vs outcomes” confusion. I’ve watched this transition unlock empowered product teams and clearer prioritization across complex backlogs.
Data-oriented paths are equally compelling. If you enjoy experimentation and evidence-based decisions, roles in analytics or data science reward rigor. Think A/B testing, identifying the minimum detectable effect (MDE), and using tools like Amplitude analytics to translate behavioral signals into product bets. Pair that with retention analysis and you’ll become indispensable to growth conversations.
Business-facing roles such as business analyst or product marketing manager are ideal if you’re energized by customer problems and market narratives. Your engineering fluency sharpens value propositions, product positioning, and go-to-market strategy in a way that resonates with both buyers and builders. In my teams, the best bridges between product and revenue often came from former engineers who could articulate trade-offs with clarity.
If operational excellence is your edge, consider SRE, DevOps, or cybersecurity. The same instincts that push you toward clean CI/CD pipelines and resilient architectures translate well into incident management, threat detection and response, and privacy-by-design practices. These roles reward systems thinking and the ability to balance reliability with delivery speed.
For engineers who love community and storytelling, developer evangelism is a natural fit. You’ll translate complex concepts into actionable guidance, from in-app guides and product tours to UX writing and documentation. The best evangelists I’ve worked with turn feedback loops into product insight, strengthening activation and product-led growth without heavy sales pressure.
Customer-facing technical roles—solutions engineer, forward deployed engineer, or technical consultant—let you stay close to the product while solving real-world problems. You’ll drive onboarding quality, user activation, and adoption while surfacing insights that influence roadmaps. Done well, this work tightens the loop between customer outcomes and product decisions.
AI-centered roles are expanding rapidly. If you’re curious about AI Strategy, retrieval-first pipelines, or the practical use of LLMs for product managers, you can bring an engineer’s discernment to a noisy space. The most valuable contributors here pair pragmatic architecture choices with clear risk management and measurable business value, not hype.
Leadership tracks remain a strong option too. The IC to manager transition isn’t about title; it’s about raising the ceiling for others. You’ll coach empowered product teams, shape organizational development, and align initiatives to defensible metrics—think DORA metrics for flow, leading indicators for value, and OKRs that measure outcomes over output.
If you’re exploring a pivot, start small and intentional. Run “career A/B tests” by taking on cross-functional projects, shadowing adjacent roles, or shipping a lightweight portfolio that demonstrates the new muscle. Join a ProductCon session, practice conference networking, and refine a narrative that links your engineering foundation to the outcomes your target role owns.
Finally, map your personal unfair advantages—domain knowledge, systems thinking, customer empathy, or operational rigor—to the roles that value them most. With focus, you can reposition your engineering experience into a differentiated story that accelerates your next chapter. The breadth of options is real, and with a deliberate plan, you’ll turn curiosity into conviction—and conviction into impact.
I treat ChatGPT as a force multiplier across the entire product lifecycle—from discovery and strategy to delivery and growth. Unlock workflows, prompts, and real PM tips showing how ChatGPT quietly reshapes product management behind the scenes.
My goal is pragmatic: turn generative AI into repeatable, measurable leverage for product discovery, product roadmapping and sprint planning, stakeholder management, and product-led growth without sacrificing quality, privacy-by-design, or judgment. This is how I apply LLMs for product managers in a way that strengthens customer empathy and speeds up decision cycles.
In discovery, I use ChatGPT to synthesize interviews, categorize sentiment, and surface emergent themes faster than a manual pass. I’ll feed it anonymized notes and ask for Jobs-to-be-Done statements, contradictory signals to validate, and the top three risks to our hypotheses. When the corpus gets large, I pair it with a retrieval-first pipeline and apply context window management so outputs stay grounded in real customer data.
On strategy and positioning, I draft and refine a crisp value proposition, clarify points of parity, and identify competitive differentiation. I ask ChatGPT to convert inputs into outcomes vs output OKRs, pressure-test assumptions, and produce a one-page narrative that even non-technical stakeholders can engage with. The result is faster alignment and fewer meetings to get to the same level of clarity.
For planning and delivery, I use ChatGPT to accelerate PRD outlines, user stories, and acceptance criteria, while explicitly requesting edge cases, failure states, and non-functional requirements. I’ll have it map risks to mitigations and suggest simple instrumentation aligned to DORA metrics and incident management readiness—useful when we’re iterating within a CI/CD cadence.
In experimentation, ChatGPT helps me frame strong A/B testing plans, calculate a minimum detectable effect (MDE), and sanity-check sample sizes. I also use it to translate metrics into plain language updates for the team, connect learnings to the next experiment, and propose follow-up analyses for retention analysis or activation bottlenecks.
For growth and onboarding, I prompt ChatGPT to generate hypotheses for user activation, in-app guides, and tooltip design that match personas and JTBDs. It drafts variations I can quickly test through Pendo or similar tools, supports product-led growth motions, and helps craft contextual copy that aligns with our value proposition without adding cognitive load.
Stakeholder communications get sharper and faster. I’ll ask for concise executive summaries, a version tailored for engineering leaders, and another for customer-facing teams. It’s especially effective for QBRs vs OKRs updates, where I need crisp narratives tied to outcomes, plus a plain-English articulation of risks and trade-offs for empowered product teams.
The guardrails matter. I set clear AI risk management boundaries, prevent any sensitive data from entering prompts, and align usage with data governance and regulatory compliance requirements. I also version and review prompts just like product artifacts, so the best ones evolve into a durable AI product toolbox the whole team can use.
If you’re getting started, pick one high-friction workflow—say, interview synthesis or PRD drafting—and timebox a week to build a repeatable prompt set and review rubric. Measure cycle-time savings and quality deltas, then expand to a second workflow. Within a month, you’ll have a lightweight operating model for AI Strategy that compounds across your roadmap.
What if your morning started with a helpful check-in from a voice AI that actually improves your sleep—using the same core principles that typically cost thousands of dollars and come with year-and-a-half waitlists? That idea energizes me as a product leader, because it blends clinical-grade outcomes with consumer-grade accessibility. Recently, I dug into how the team at Rest built an AI sleep coach inspired by Cognitive Behavioral Therapy for Insomnia (CBTI), and why their method offers a repeatable blueprint for complex, personal AI products.
The origin story is a classic product discovery moment. Rest’s team noticed that a meaningful slice of users in their podcast app were using audio to fall asleep. Although it represented only about 10% of users, that group showed a high willingness to pay. That signal pushed them to explore a dedicated sleep solution, moving from a general audio app to a targeted sleep experience—and eventually toward an AI-powered coach as LLMs matured.
Through jobs-to-be-done research, they identified a clear, underserved segment: “DIY sleep hackers.” These are motivated users who want agency, structure, and results without navigating clinical systems. Choosing CBTI (a clinically proven approach with 80% efficacy) gave the product a strong evidence-based foundation while remaining accessible as a wellness tool. It’s the kind of strategic choice I look for: credible, measurable, and aligned with user motivation.
The product evolution moved in smart, incremental steps. Rest started with a basic text chatbot before graduating to a voice-first experience—using Vapi for voice and OpenAI for reasoning. Voice changed the relationship dynamic: it increased intimacy, lowered friction for daily check-ins, and made behavioral coaching feel human without pretending to be. The team built a memory system that tracks context (like traveling or having a dog) with time-based relevance, which keeps conversations fresh, respectful, and genuinely personalized.
Daily engagement is driven by dynamic agendas that adapt based on sleep data, the user’s stage in the program, and their recent compliance. I love this mechanic: it operationalizes behavior change by sequencing the right intervention at the right time. In parallel, they developed text via OpenAI Assistants while building voice with Vapi, which let them ship value while learning in two modes. They also moved from massive system prompts to RAG for general sleep knowledge, keeping personal user context in the prompt—reducing brittleness while improving scalability.
Because sleep sits close to healthcare, the team drew a firm line between wellness and medical positioning. They implemented clear guardrails: no diagnosis, no medication advice, and strong boundaries on scope. Weekly error analyses with domain experts (sleep therapists) tightened quality and tone, and they adopted LLM-powered evals to enforce safety boundaries. For observability and evaluations, they leveraged Langfuse, and they experimented with Hamming for voice testing to refine the experience end-to-end.
Under the hood, this is a great example of “one bite of the apple at a time” product building in AI. Start with a simple interface, anchor on an evidence-based method, layer personalization with memory, formalize program structure with dynamic agendas, and shift to RAG when general knowledge outgrows prompt engineering. As a product leader, I see strong echoes of agentic patterns here—goal-oriented orchestration, stateful memory, and adaptive planning—shipped in pragmatic increments rather than as a monolithic platform rewrite.
A few takeaways I’m applying with my teams: First, segment deeply and pick a high-intent niche (those “DIY sleep hackers” were the right beachhead). Second, let modality fit the job—voice is not a gimmick when it boosts compliance and empathy. Third, design safety and scope from day one if you’re anywhere near health. Finally, invest early in evals and observability so you can improve with confidence, not hope.
If you want to explore the full conversation and product decisions, you can listen here: Spotify | Apple Podcasts.
Resources & Links:
Rest – AI sleep coach app
Vapi – Voice agent platform Rest uses
Langfuse – Observability and evals platform
Hamming – Voice testing platform
AI Evals Maven Course by Hamel Husain and Shreya Shankar
Bottom line: Rest demonstrates how to take a clinically grounded method like CBTI, translate it into a daily voice-first experience, and ship it with rigor. If you’re building in AI, this is a model worth studying—practical, safe, and deeply user-centered.
I’ve spent years leading product organizations where the best outcomes emerged from a tight handshake between design rigor and product strategy. The role that consistently sits at that high-impact intersection is the UX product manager. Done well, it’s the engine of product-led growth: deeply empathetic with users, relentlessly focused on outcomes, and fluent in both discovery and delivery.
Curious about the UX product manager role? Discover how it overlaps with design, PM, and why it might be the next step in your career.
At its core, a UX product manager owns the customer experience end-to-end while steering the business toward measurable outcomes. I translate user insights into prioritized problems, shape the solution space with designers and engineers, and validate decisions with data. Unlike a traditional PM who may skew toward market sizing and business cases, or a designer who may emphasize interaction patterns and visual systems, I integrate both frames to ensure we ship experiences that users adopt, retain, and recommend.
On the design side, I work hand-in-hand with product designers and UX writing to define the problem, craft flows, and stress-test usability. I obsess over clarity, affordances, and friction—especially during onboarding. Strong UX writing often makes or breaks first-run experiences, and I treat microcopy as part of the product, not an afterthought.
On the product management side, I anchor teams on outcomes vs output OKRs, facilitate product discovery, and drive prioritization against clear value propositions. I operate within empowered product teams and build tight product trios with design and engineering so we can validate assumptions fast, reduce waste, and increase the surface area for innovation.
Day-to-day, my craft blends qualitative research and quantitative analysis. I lean on tools like Amplitude analytics, Pendo, and Intercom to instrument funnels, run A/B testing, and perform retention analysis. When I experiment, I’m explicit about the minimum detectable effect (MDE) to avoid inconclusive reads. I measure the impact of changes on activation, time-to-value, and core feature adoption—and I make sure we can trace improvements to specific user segments.
User activation is my early warning system. If activation is lagging, I revisit the first-mile experience: guidance, progressive disclosure, in-app guides, product tours, and contextual tooltip design. I also ensure our onboarding is sequenced around the critical path to value rather than a feature parade. When activation improves, downstream KPIs like retention and expansion usually follow.
If you’re looking to become a UX product manager, start by strengthening three pillars: customer insight, product strategy, and experience design. Build a habit of continuous product discovery—co-creating with users, running lightweight experiments, and synthesizing findings into actionable decisions. Learn to translate insights into a product roadmapping and sprint planning cadence that energizes the team and keeps stakeholders aligned.
Your portfolio should read like a decision journal, not a gallery of screens. For each case study, frame the problem, outline constraints, describe alternatives considered, and show the experiments you ran. Include the metrics that mattered (activation, adoption, retention), the instrumentation you used, and the decisions you made when data was ambiguous. Hiring managers want to see your thinking under uncertainty and how you rallied cross-functional partners.
Communication and stakeholder management are differentiators. I tailor narratives for executives (trade-offs and business impact), for engineers (clarity on constraints and sequencing), and for design (user jobs, heuristics, and the narrative arc of the experience). Clear, frequent updates keep momentum high and reduce thrash, especially when priorities shift.
On the execution side, I make sure delivery never drifts from discovery. Every sprint is tied to a learning goal or outcome. We pair quick prototypes with production experiments, and we celebrate killing ideas that don’t move the needle. That discipline keeps us focused on outcomes and accelerates iteration speed without sacrificing quality.
Finally, a few career accelerators: get comfortable with analytics, learn the language of UX writing, practice story-based demos, and go deep on onboarding patterns. If you can move activation, you can change the trajectory of the business. Pair that with a strong perspective on product-led growth and you’ll be ready to lead product work that compounds.
The UX product manager role is a force multiplier. It’s where rigor meets empathy, and where design and PM converge to create experiences customers love—and businesses rely on. If that intersection energizes you, you’re already on the right path.
Setbacks are the tax we pay for doing meaningful product work. As a VP of Product Management, I’ve learned that what separates resilient teams from the rest isn’t a lack of failures—it’s how we metabolize them. This episode of All Things Product with Teresa Torres and Petra Wille is a powerful reminder that recovery, reflection, and rigorous product discovery are as essential as speed and execution.
Listen to this episode on: Spotify https://open.spotify.com/episode/10LYRya7boYJBHTYBnE79E?ref=producttalk.org | Apple Podcasts https://podcasts.apple.com/kh/podcast/dealing-with-setbacks/id1794203808?i=1000737190520&ref=producttalk.org
What struck me most is how Teresa shares a deeply personal story about her long recovery from an injury—and how that journey mirrors the nonlinear reality of product development. In product, just like in healing, progress is rarely a straight line. We have surges, stalls, and moments that feel like reversals. Yet with the right mindset and rituals, we still move forward.
Professionally, we all face moments when your product fails to move a single KPI, when a launch falls flat, or when you just feel stuck. I’ve been there—in quarterly reviews, post-launch standups, and board prep. The instinct is to sprint straight into solutions. The wiser move is to respond with curiosity, emotional honesty, and resilience, then re-engage our discovery habits with intention.
If you’re a PM, designer, or researcher, consider this an invitation to rebalance. Recovery and reflection are just as important as velocity and success. That’s not soft talk—it’s how empowered product teams build durable performance without burning out.
On the emotional reality of setbacks, I’ve learned to normalize naming the loss. We put immense pressure on ourselves, and it’s okay (and necessary) to grieve product failures. When we acknowledge the disappointment, we regain the ability to observe clearly—and to learn.
Leaders play a crucial role here. I create space for teams to recover before jumping into post-mortems. We don’t whiteboard over feelings; we schedule time for decompression, then conduct a crisp, blameless review. That sequencing transforms the quality of insights and strengthens psychological safety.
Another lesson that resonates is the danger of tying performance too tightly to outcomes. Outcomes matter, but they are lagging indicators influenced by many externalities. I evaluate performance on behaviors: clarity of problem framing, rigor in discovery, quality of decision-making, and stakeholder alignment. This aligns with outcomes vs output OKRs and keeps us focused on controllable excellence.
How do we build resilience? Continuous discovery builds resilience by normalizing failure. When we test assumptions routinely with customers and data, we turn large, risky bets into a series of small, learnable steps. Teams recover faster because failure becomes feedback—frequent, cheap, and informative.
For perspective, I often use the 10–10–10 framework (from Decisive by Chip & Dan Heath). I ask: How will this setback feel in 10 minutes, 10 months, and 10 years? The answers de-escalate urgency, expand our time horizon, and produce better, calmer decisions.
Here are the key takeaways I’m carrying forward. Setbacks are not just inevitable—they’re part of doing meaningful product work. Giving teams time and space to process failure builds long-term resilience. Mourning losses is just as important as celebrating wins.
Healthy discovery cultures embrace reflection, psychological safety, and emotional honesty. And most importantly, staying consistent with discovery habits helps teams recover faster and learn more deeply.
Notable moments that stood out for me include: [00:02:00] Teresa shares the story of her injury and what it’s taught her about patience and setbacks. The parallel to product cadence is both humbling and motivating.
[00:10:00] Petra talks about a team whose carefully planned launch didn’t move a single KPI. I’ve led similar debriefs; when we anchor on customer insight gaps rather than blame, the next iteration improves dramatically.
[00:20:00] Discussion on allowing space for grief and frustration after failure. In my teams, we time-box “emotional processing” before we enter analysis mode—it humanizes the work and sharpens the learning.
[00:30:00] Why organizations must decouple performance reviews from short-term outcomes. I align evaluations to strategy execution quality, hypothesis discipline, and cross-functional collaboration.
[00:40:00] How continuous discovery can help teams normalize—and even learn to appreciate—setbacks. When discovery is weekly, momentum becomes self-healing.
If you want to dig deeper, here are useful links from the episode. Follow Teresa Torres: https://ProductTalk.org
Follow Petra Wille: https://Petra-Wille.com
Mentioned in the episode: Decisive by Chip & Dan Heath — The 10–10–10 framework for perspective in decision-making https://heathbrothers.com/books/decisive/?ref=producttalk.org
Teresa Torres’ Continuous Discovery Habits — Building resilience through ongoing discovery practices. https://www.amazon.com/Continuous-Discovery-Habits-Discover-Products/dp/1736633309?dchild=1&keywords=continuous+discovery+habits&qid=1621385051&sr=8-2&linkCode=sl1&tag=teresatorres-20&linkId=34bc439ac78da06e1398f7bf069b219e&language=en_US&ref_=as_li_ss_tl&ref=producttalk.org
Join the Conversation: Have thoughts on this episode? Leave a comment below. I’d love to hear how you create space for recovery while sustaining product velocity.
Full Transcript: Full transcripts are only available for paid subscribers.
Walking into PendomoniumX London, I could feel the AI revolution hitting its stride. The conversations were sharper, the demos more grounded, and the outcomes more measurable—a clear signal that AI Strategy is moving from slideware to shipped value in modern product management.
PendomoniumX’s sixth stop brought 350+ software leaders together for a day of AI transformation, real-world stories, and product innovation.
What stood out to me was the shift from hype to execution. Teams compared playbooks for gen ai and Generative AI, shared lessons from LLMs for product managers, and showed how they’re threading AI into product discovery, product roadmapping and sprint planning, and go-to-market motions. The focus was pragmatic: drive adoption, accelerate time-to-value, and make better decisions with cleaner signals.
On the product-led growth front, I saw compelling examples of using Pendo’s in-app guides and product tours to increase user activation and reduce friction in key onboarding moments. When AI-enhanced experiences are paired with clear guidance and behavioral analytics, customers don’t just try features—they build habits.
What I appreciated most were the leadership narratives: empowered product teams aligning around outcomes, candid retros on where AI prototypes missed the mark, and crisp frameworks for prioritizing the highest-leverage bets. The conference networking felt purposeful, with operators trading hard-won insights on experimentation velocity, data governance, and building trust into AI-infused experiences.
My takeaway: AI is no longer a side project—it’s a core capability in product management. If we anchor our AI Strategy in clear customer problems, instrument for learning, and iterate with discipline, we can consistently turn innovation into impact. And with the right mix of PLG mechanics, in-app education, and thoughtful design, those gains compound across the product lifecycle.
