Customer feedback is the most reliable compass I have for product strategy and execution. Over the years leading product at HighLevel, I’ve built and refined a system that turns raw signals from users into clear, prioritized decisions our teams can confidently ship.
A practical guide to collecting and using product feedback in product management (from AI tools to early-stage tactics) for better product decisions.
My playbook starts with continuous discovery. I keep a steady flow of insights from sales calls, customer support threads, community forums, and in-product behavior so I can triangulate patterns rather than chase loud anecdotes. This mix of quantitative and qualitative data helps me separate urgent noise from strategically meaningful trends.
On the quantitative side, I rely on product analytics to ground the conversation. Amplitude analytics gives me activation, retention cohorts, and feature engagement, while controlled experiments and A/B testing validate whether an idea actually moves a target metric. Tying these signals to specific customer segments helps me see where product-led growth is working—and where it’s stalling.
For qualitative insight, I combine in-app guides and lightweight surveys (via tools like Pendo) with structured interviews and support escalations (often surfaced through platforms like Intercom). I map problems using the Kano Model to understand which requests are basic expectations, which are performance drivers, and which are potential delights. This keeps our roadmap focused on outcomes, not just outputs.
AI now accelerates the synthesis step. With LLMs for product managers in my AI product toolbox, I summarize interview transcripts, cluster themes across thousands of notes, and quantify sentiment without losing nuance. I still review raw artifacts to avoid hallucinations and preserve context, but AI reduces the time from signal to insight dramatically—freeing me to spend more energy on judgment and storytelling.
In early-stage contexts, I bias toward speed and proximity to users. I schedule founder- or PM-led discovery calls weekly, instrument product tours early, and launch scrappy in-product prompts to validate demand before over-investing. When data is sparse, I focus on high-signal channels (power users, churned customers with qualified use cases) and document crisp problem statements that connect directly to activation, retention analysis, and revenue outcomes.
Prioritization ties everything together. I translate insights into hypotheses aligned to outcomes vs output OKRs, then pressure-test them with feasibility and strategic fit. We run small, measurable experiments, track deltas in activation and retention, and adjust the product roadmapping and sprint planning cadence based on what the data and customers teach us.
This approach builds trust with stakeholders and creates empowered product teams. By grounding decisions in a transparent trail of feedback, analytics, and experiments, we reduce thrash, move faster, and—most importantly—ship product moments that customers value.
If you’re refining your own feedback engine, start by instrumenting the basics, set a weekly discovery rhythm, and let AI handle the heavy lifting on aggregation and synthesis. The compounding effect is real: better insights lead to better bets, which lead to better outcomes for your users and your business.
Your roadmap can be coherent, the launch can go smoothly, and adoption can still stall. The usual gap is not a lack of effort. Product strategy, field discovery, technical validation, and post-launch adoption were treated as separate activities, so each team learned something that the others could not use.
You can close that gap by treating solutions engineering as part of the product learning system. The goal is not to give sales more technical coverage or product managers a larger queue of requests. It is to turn real customer conditions into testable product decisions, then follow those decisions until customers reach and repeat the intended outcome.
Key takeaways
Define adoption as an observable customer behavior before you approve features or plan a launch.
Ask solutions engineers to capture evidence in a consistent format, separating what happened from what the team thinks it means.
Treat demos and proofs of value as experiments with a hypothesis, baseline, value event, and decision rule.
Measure the entire path from promise to repeated value. A login, click, or feature visit is rarely sufficient evidence of adoption.
Route each adoption problem to the right response: positioning, enablement, onboarding, integration, product design, or core capability.
Carry launch learning into roadmap and operating reviews so that adoption changes product decisions instead of becoming a reporting exercise.
Write the adoption contract before debating the roadmap
Product strategy becomes operational only when it specifies whose behavior should change, why that change matters, and what evidence would show that the product created value. Without that translation, teams can agree on a strategic theme while holding incompatible ideas about what success means.
Write an adoption contract for every significant product bet. This is not a legal document or a long requirements package. It is a compact agreement among product, engineering, design, solutions engineering, sales, and customer success about the outcome being pursued.
Target user: Name the persona and operating context. A broad account segment is not enough when different people inside the account perform different jobs.
Starting condition: Describe the workflow, constraint, or workaround that exists before the change. This gives you a baseline against which the new experience can be judged.
Desired outcome: State the progress the customer wants, not the capability you intend to ship.
Value event: Identify the observable behavior showing that the user reached meaningful value. Opening a page may indicate exposure; completing the intended workflow is stronger evidence.
Repeat condition: Define what sustained use looks like at the natural cadence of the workflow. Daily activity is useful only when the job itself occurs daily.
Decision evidence: Agree on the metric, qualitative evidence, technical constraints, and guardrails that will determine whether to continue, change, or stop the bet.
Suppose a product automates part of an operations workflow. Increase feature adoption is too weak to guide a team. A more useful contract says that the operations manager can complete the target workflow, obtain the intended result, and return to it when the job recurs without relying on the old workaround. The team can now examine setup, permissions, integrations, comprehension, completion, and repetition as separate parts of the same outcome.
This contract also changes roadmap conversations. A feature request no longer competes on enthusiasm alone. You can ask which persona it serves, which blocked outcome it unlocks, what evidence supports the problem, and which customer behavior should change if the solution works. If those questions have no answers, the request needs more discovery before it needs an estimate.
Keep the contract stable enough to align the organization, but do not protect it from evidence. If customers repeatedly pursue a different outcome, struggle with an unanticipated dependency, or assign value to another part of the workflow, revise the contract explicitly. Silent reinterpretation is dangerous because every function can declare success against a different definition.
Turn solutions engineering into a field evidence system
A solutions engineer sees details that rarely survive a conventional feature request: the customer’s existing architecture, the sequence of the workflow, the people involved in approval, the point at which a demonstration loses credibility, and the difference between a stated objection and an actual blocker. That information can improve strategy, but only if the organization captures it in a form that product teams can compare and act on.
Do not send raw call notes into a backlog and call it discovery. Use a consistent field record containing:
The persona, account context, and job being attempted.
The customer’s desired outcome and current way of achieving it.
The specific moment where the current product or proposed solution created friction.
The relevant technical condition, such as an integration, data, permission, security, or scalability constraint.
The evidence observed: customer behavior, workflow review, demonstration response, deployment result, or instrumented product data.
The proposed interpretation and any plausible alternative explanation.
The next question or test that would reduce uncertainty.
Within that record, separate observation, interpretation, and request. They are not interchangeable.
Observation: The administrator could not complete configuration because a required permission was unavailable.
Interpretation: The permission model may not match how this persona operates.
Request: Add a new administrative role.
The observation is evidence. The interpretation is a hypothesis. The request is merely a candidate solution. Keeping them separate prevents the first proposed feature from becoming the definition of the problem.
Product leaders also need to protect this system from deal gravity. A commercially urgent request can be important without representing a widespread product problem. Conversely, a small implementation detail can reveal a structural barrier affecting an entire target persona. Evaluate field evidence with questions that make those differences visible:
Does the problem recur for the same persona, workflow, or environment?
Does it block the value event, or does it affect a peripheral preference?
Is the root cause a missing capability, product usability, configuration, integration, positioning, or customer process?
Does the problem appear in the strategic segment the product is designed to serve?
Can a smaller or reversible test distinguish competing explanations?
What roadmap decision would change if the hypothesis were confirmed?
The last question is a useful filter. If no plausible answer would change a decision, the team may be collecting interesting information rather than decision-grade evidence.
Bring the strongest records into the product trio’s learning cycle. Field evidence can then sharpen roadmaps, sprint planning, positioning, integration decisions, and stakeholder conversations. The solutions engineer contributes technical discovery and customer context. Product management synthesizes patterns and owns tradeoffs. Design examines behavior and workflow. Engineering tests feasibility and exposes architectural consequences. Sales contributes commercial context without converting urgency into automatic priority. Customer success adds evidence about repeated use after the initial deployment.
This arrangement does not require every customer conversation to become a committee meeting. It requires a common evidence format, clear decision ownership, and a route by which meaningful field learning reaches the people making product choices.
Run demos and proofs of value as product experiments
A polished demo can prove that a presenter understands the product. It does not prove that a customer can adopt it. Even a successful technical pilot can produce a false positive if the solutions engineer performed work that the target user cannot repeat, the environment was unusually controlled, or the team never defined what customer value would look like.
Design every proof around a falsifiable outcome hypothesis. A practical structure is:
For [persona] in [context], enabling [change] should produce [observable behavior] because [value mechanism]. The hypothesis is supported if [agreed evidence] meets [predefined decision rule] without violating [guardrail].
The brackets force useful decisions. You have to name the user, the operating condition, the expected behavior, and the reason the behavior represents value. You also have to decide what would count as failure before enthusiasm about the result changes the standard.
Build the proof plan around the following elements:
Baseline: Document how the customer handles the job now, including meaningful friction and dependencies.
Scope: State which workflow, persona, environment, and constraints the proof includes. Anything outside that boundary remains unvalidated.
Value event: Identify the customer action or result that demonstrates progress toward the desired outcome.
Instrumentation: Decide which product events and qualitative observations will show where users advance or stop.
Enablement: Record how much assistance the customer receives. Heavy expert intervention can validate technical feasibility while leaving self-service adoption unproven.
Decision rule: Define what evidence will justify progression, iteration, repositioning, or stopping.
Learning owner: Name the person responsible for translating the result into a product, go-to-market, or adoption decision.
A compact learning record can follow Insight – Hypothesis – Experiment – Metric. The sequence matters. A customer comment produces an insight, not a roadmap commitment. The insight leads to an explanation that can be tested. The test produces evidence against a metric or decision criterion.
Capture objections with the same discipline. An objection about missing functionality may actually reflect an unclear value proposition, concern about integration effort, lack of trust in the output, or a mismatch between the buyer and the eventual user. Ask what the customer is unable or unwilling to do because of the concern. That question moves the discussion from the requested feature to the blocked behavior.
At the end of a proof, classify what was learned:
Value validated: The target persona reached the intended outcome under representative conditions.
Need validated, capability missing: The problem matters, but the product cannot yet support the required workflow.
Capability works, adoption friction remains: The product can deliver value, but setup, comprehension, trust, or workflow design prevents the user from reaching it reliably.
Technical path blocked: An integration, architecture, data, permission, or operational constraint prevents deployment.
Positioning mismatch: The product can do what was promised, but the outcome is not important enough for the target persona or was framed in the wrong terms.
Evidence inconclusive: The test did not represent the intended user or environment, or the decision criteria were not measurable.
These outcomes lead to different work. A positioning mismatch belongs in messaging and discovery. Adoption friction may require onboarding or product design. A recurring technical blocker may deserve roadmap investment. An inconclusive test deserves a better test, not a success story.
Demos and early deployments become high-signal learning mechanisms when their evidence returns to product strategy instead of ending in the opportunity record. That closed loop is what allows solutions engineering to improve both customer execution and the underlying product.
Measure adoption as a path, then act at the break
Adoption is not a single metric. It is a sequence that begins with a credible promise and ends when the customer repeatedly receives value. Measuring only the end hides the reason users failed. Measuring only clicks overstates progress.
Map the path for the persona named in the adoption contract:
Promise understood: The user or buyer can connect the product to a relevant outcome.
Entry: The intended user begins setup or enters the target workflow.
Readiness: Required data, permissions, integrations, and configuration are in place.
Activation: The user reaches the first meaningful value event.
Repetition: The user returns when the underlying job recurs and reaches value again.
Expansion: Appropriate users, workflows, or capabilities are added because the initial value is credible.
Retention: The product remains part of the operating workflow because it continues to produce the desired outcome.
Instrument enough of this path to locate the break. For each event, retain the eligible persona or account, relevant product context, action attempted, result, and connection to the intended outcome. Always state the denominator. Activation among configured users answers a different question from activation among all purchased accounts.
Use metrics such as time-to-value, daily active usage, and feature adoption only when they match the product’s natural workflow. A monthly administrative task should not be judged by daily activity. A high feature-visit count does not establish value if users repeatedly enter the feature because they are confused. Pair behavioral data with the qualitative context collected during proofs, implementations, and customer conversations.
Observed pattern
Question to investigate
Likely response area
Interest is high, but eligible users do not begin
Is the value proposition relevant and credible to this persona?
Positioning, targeting, or enablement
Users begin, but readiness fails
Which data, permission, configuration, or integration dependency is blocking progress?
Onboarding, implementation, or platform capability
Users complete setup, but do not reach the value event
Does the workflow lead clearly to the promised outcome?
Product design, guidance, or core capability
Users activate, but do not return
Is the job recurring, and was the first outcome valuable enough to change behavior?
Discovery, workflow fit, trust, or value proposition
Adoption differs sharply by persona or context
Was the product designed and positioned for the segment that is actually succeeding?
Segmentation, strategy, or packaging
Usage is high, but retention or customer outcomes remain weak
Is activity measuring value, required effort, or repeated friction?
Metric design, product quality, or outcome alignment
Match the intervention to the break. Use onboarding checklists, empty-state prompts, in-app guides, and product tours when the user needs contextual direction. Do not use a tooltip to conceal a broken permission model, an unreliable integration, or a workflow that does not create enough value. Guidance can reduce comprehension friction; it cannot repair a missing capability.
When you test an intervention, define the intended behavior change and success criterion in advance. Segment the result by the persona and context in the adoption contract. An aggregate improvement can hide a deteriorating experience for the strategic user, while a neutral aggregate can hide a strong response in the segment the product is meant to serve.
The adoption contract and any evidence that challenges it.
Progress through the journey, segmented by the intended persona and context.
The strongest field observations, with interpretations kept separate.
The active hypotheses and experiments.
The product, positioning, enablement, integration, or onboarding decision each experiment may change.
The owner responsible for making that decision and returning the result to the group.
Use operating reviews and QBRs to evaluate outcomes, not to count shipped features or completed guides. Ask which hypothesis was confirmed, which constraint was removed, where customers still stop, and what the organization will do differently. If an experiment has no route to a decision, revise it or stop it.
For your next significant product bet, start before the launch plan. Write the adoption contract, ask a solutions engineer to challenge it with field conditions, and define the evidence that would change your mind. That small discipline gives every later conversation – roadmap, demo, onboarding, analytics, and QBR – the same customer outcome to pursue.
Every quarter, I watch product teams move from gut feel to data-informed decisions—until instrumentation bottlenecks slow them to a crawl. That’s why I’ve become an advocate for codeless analytics: it removes the dependency on engineering sprints for basic event tracking and lets teams answer product questions in hours, not weeks.
We explain what codeless analytics are, why (and how) Pendo supports them, plus responses to the top three myths about low-code/no-code solutions.
Here’s how I frame it with my teams: codeless analytics enables product managers, designers, and customer success to tag features visually, track interactions, and analyze adoption without shipping code. The goal isn’t to replace engineered events; it’s to accelerate discovery, speed up iteration, and reduce context-switching for developers. In practice, this means cleaner prioritization, faster validation of hypotheses, and tighter product-led growth loops.
Why Pendo? In my experience, Pendo’s codeless model shortens the distance from question to insight. Visual tagging makes event setup accessible, in-app guides and product tours let us experiment with onboarding and activation, and governance controls ensure data remains trustworthy across teams. The result is a unified analytics approach where we reserve custom instrumentation for complex logic while using codeless tracking for everyday product questions.
Let’s address the top three myths I hear most often. Myth 1: “No-code is only for simple use cases.” In reality, most decisions we make weekly—feature adoption, path analysis, funnel drop-offs, and retention analysis—do not require custom code. Codeless analytics handles these well, and when we need deeper context (like server-side events), we complement it with engineered tracking. It’s a both/and, not an either/or.
Myth 2: “Codeless data isn’t accurate.” Accuracy comes from governance, not the method. I set clear standards: naming conventions, tagging reviews, ownership, and periodic audits. With disciplined process, codeless tracking yields consistent, decision-grade data. The added benefit is visibility—non-technical stakeholders can validate the instrumentation themselves, reducing misalignment.
Myth 3: “Engineers must instrument everything to scale.” Engineering time is precious; we should spend it on differentiated capabilities, not on routine click tracking. Codeless analytics scales by empowering product teams to self-serve, while engineering focuses on back-end, performance, and edge cases. When paired with a unified analytics platform and clear data contracts, this model scales cleanly across product lines.
For teams adopting this approach, I recommend a simple operating model: define your core product questions up front, tag features aligned to those questions, connect insights to in-app guides for experiments, and measure user activation and retention continuously. Whether you run Pendo alongside Amplitude analytics or within a broader unified analytics platform, the key is to keep the insight-to-action loop tight.
The future of product analytics is codeless because it puts insights where they belong—directly in the hands of the people designing the experience. When we remove bottlenecks, we learn faster, ship smarter, and drive measurable PLG impact. That’s how we turn product analytics from a reporting function into a competitive advantage.
Trust is the currency of any high-stakes AI product, and nowhere is that more true than in healthcare. I recently dug into how Healio built an AI assistant for physicians—an audience that can’t afford to be wrong—and it’s a masterclass in balancing accuracy, transparency, and speed without compromising credibility.
Healio, a 125-year-old medical publishing company, set out to create Healio AI to help clinicians prepare for patient care. From the outset, their guiding principle was simple: physicians won’t trust you until you prove it. That lens shaped every decision—from discovery and prototyping to architecture, evaluation, and ongoing validation.
Discovery started with a survey of 300 healthcare professionals to understand real-world needs at the point of care. The headline insight: physicians primarily want AI for preparation, not bedside use. Even more surprising, the top ask wasn’t purely diagnostic support; it was help with patient communication and empathy—translating complex information into clear, accessible conversation.
Momentum mattered. After beginning with Figma mockups to validate workflows, the team built a working prototype in a single weekend using Cursor. That velocity wasn’t about cutting corners; it was about proving value quickly, reducing ambiguity, and iterating with concrete feedback from physicians.
Under the hood, the system employs RAG and hybrid search—combining lexical search, vector search, and semantic search across multiple trusted sources like PubMed. As any PM who has integrated biomedical literature knows, "just use PubMed" isn’t simple—there are five different ways to access the same data, each with trade-offs. The team made pragmatic choices to balance freshness, coverage, latency, and cost while preserving trust in source quality.
Designing for trust extended all the way to the citation UX. The team leaned into citations that physicians actually trust: subscripts, hover states, and progressive disclosure. This gave clinicians verifiable threads back to source material without overwhelming the core interaction, aligning with how experts want to audit evidence under time pressure.
Evaluation wasn’t left to chance. They stood up eight LLM judges for evals: safety, medical accuracy, faithfulness, relevancy, completeness, reasoning, clarity, and overall quality. Just as importantly, they treated those signals as directional, not definitive. In a high-stakes domain, physician feedback trumps LLM-as-judge feedback—so they complemented automated evals with direct reviews from practicing clinicians to calibrate quality and reduce hallucinations.
On the safety front, the team implemented HIPAA compliance and input guardrails for masking personal health information. That choice reflects strong data governance and privacy-by-design thinking: protect PHI by default, constrain prompts to safe boundaries, and make compliance a first-class citizen in the product architecture.
They also addressed monetization without compromising experience. Serving contextual ads while the LLM processes queries is a practical approach that preserves physician workflow efficiency and creates a clear, non-intrusive revenue model.
Critically, the work didn’t stop at launch. The Healio Innovation Partners provide ongoing discovery and validation, ensuring the system evolves with physician needs and the medical evidence base. This is the operating cadence you want for any AI product that sits at the intersection of safety, accuracy, and fast-changing knowledge.
My takeaways for building AI in high-stakes domains: prioritize retrieval-first pipelines over model cleverness; couple RAG with hybrid search across vetted sources; design citations that earn trust at a glance; use eval-driven development, but let domain-expert feedback be the ultimate judge; and embed regulatory compliance into your product strategy from day one. If trust is your North Star, this is a playbook worth emulating.
I just tuned into the latest conversation on the upcoming Product at Heart 2026, and it hit on the exact challenges product leaders are navigating right now: curating meaningful content in a world where AI moves faster than our agendas, designing formats that create real connection, and ensuring every minute earns its place. Listening to Petra Wille and Teresa Torres map out the speaker lineup, workshops, and structural shifts, I found myself nodding along—this is the kind of thoughtful curation we need if we want product teams and product leaders to walk away with practical value, not just inspiration.
Listen to this episode on: Spotify | Apple Podcasts
What stood out immediately is the bold move to a single-track conference for 2026. In an era of gen ai hype and endless breakouts, this choice signals clear intent: tighter curation, a shared experience, and less FOMO. The team isn’t carving out a separate AI track—and I love that decision. Their stance is simple and sensible: No AI track—AI will show up everywhere, but not as a siloed topic. The team sees it as part of the everyday toolkit. That mirrors how high-performing, empowered product teams actually work today—AI Strategy and AI workflows are part of the operating system, not a side show.
The keynote lineup is already compelling. Christian Idiodi (SVPG) brings storytelling that turns product principles into habits you can actually use on Monday. Elaine Kasket, cyber-psychologist, exploring digital afterlife and AI replicas, will push us to think more deeply about the human side of our systems. And Teresa Torres will be sharing what she’s learning about AI—exactly the kind of continuous discovery mindset we need as we integrate LLMs into product discovery and delivery.
I’m also thrilled to see roundtables become what they’re calling an “alternative track.” That’s a smart way to deepen learning without fragmenting attention. The best conference ROI I’ve had often comes from targeted small-group conversations—where product trios compare approaches, swap metrics frameworks, or challenge each other’s product strategy assumptions. It’s a design choice that rewards curiosity and builds communities of practice.
We also get a behind-the-scenes look at Teresa’s Maker Studio workshop, where participants will build personal AI workflows. That’s exactly the hands-on, practitioner-first approach teams need right now—less demo theater, more systems that stick. If your roadmap includes integrating LLMs into continuous discovery or augmenting your team’s decision velocity, this kind of guided practice is gold.
The broader workshop slate looks deep and balanced. Expect returning favorites and practical frameworks: Rich Mironov on the realities of product leadership in complex orgs; Büşra’s metrics workshop translating outcomes into action; and an overview of additional workshops from Rich Mironov, Büşra Coşkuner, Marcus Castenfors, and Özlem Yüce. From success metrics to toolkits for product managers, the content spans IC to product management leadership—ideal if you’re stepping into new roles or scaling empowered product teams.
One of the most exciting evolutions is the Product Leadership Event, now a 1.5-day retreat. The format blends talk sessions, mini-workshops, dinners, and small-group excursions (boat rides, improv, etc.), giving leaders time and space to exchange playbooks, stress-test decisions, and build real relationships. It’s capped at 60 attendees (all in product leadership roles) to keep it intimate and useful. As someone who believes in outcomes vs output OKRs and first principles decision making, I appreciate how this structure encourages depth over breadth—and real accountability among peers.
Here are the core takeaways I’m carrying into my own planning: single-track means tighter curation, so every talk has to earn its place. Roundtables are growing into an “alternative track,” offering more ways to engage beyond stage talks. Workshops go deep and meet you where you are—IC, manager, or executive. And the leadership retreat expands to maximize learning from peers, not just from the stage. If you care about product discovery, product strategy, and conference networking that leads to actual business impact, this program looks thoughtfully engineered.
If you’re planning your 2026 calendar—or just curious how conferences evolve alongside the craft—this is a thoughtful walkthrough of what to expect. Come say hi to Teresa and Petra—on stage, at a roundtable, or somewhere in the hallway conversations that make these events memorable.
For more context and resources mentioned, explore: Product at Heart, Arne Kittler, Mind the Product, Christian Idiodi of Silicon Valley Product Group, Elaine Kasket, House of Beautiful Business, The 7 Habits of Highly Effective People by Stephen Covey, Rich Mironov, Marty Cagan, Claude Code, Codex by OpenAI, Marcus Castenfors, Büşra Coşkuner and her Success Metrics: A Playbook for Product Managers, Özlem Yüce’s Essential Toolkit for Product Managers, Petra’s Product Leadership Wheel (PLwheel), and Netlight.
Follow Teresa Torres: https://ProductTalk.org
Follow Petra Wille: https://Petra-Wille.com
Full transcripts are only available for paid subscribers.
I set out to create a lightweight, high-impact “Pendo Wrapped” experience for our users—and I did it in under 10 minutes with Pendo MCP. As a VP of Product Management, I’m constantly looking for fast, pragmatic ways to turn product insights into moments that drive engagement. This experiment was about transforming raw analytics into a concise, celebratory year‑in‑review that motivates customers to explore more value.
When I say “Pendo Wrapped,” I mean a simple, narrative-style summary of usage highlights: what got adopted, which moments mattered, and where value showed up most clearly. Framed well, that story reinforces product‑led growth by reminding users why they chose us, nudging them toward the next best action, and strengthening activation and retention without heavy development work.
My approach was straightforward: define a clear objective (celebrate milestones and prompt the next step), choose a focused set of metrics (adoption, engagement, and activation), and target relevant segments. Then I layered the narrative on top of existing analytics using in‑app guides and product tours to deliver the experience where it matters most—inside the product.
The reason it took minutes, not hours, is that Pendo MCP let me work with what we already had—segments, saved reports, and proven guide templates—so I could spend time on the story, not the scaffolding. No code, minimal configuration, and a crisp call to action made it feel polished without being heavy.
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.
If you want to replicate this quickly, start by selecting one user segment and three metrics that matter to them, write a two‑sentence narrative that connects those metrics to outcomes, and ship a short in‑app guide with a single, purposeful CTA. That’s enough to deliver a personalized year‑in‑review feel and spark immediate exploration—no new infrastructure required.
What surprised me most was how a small, story‑driven touch created outsized alignment across customers and internal teams. It turned analytics into advocacy, reminded our users of the value they’re already getting, and opened the door to deeper adoption. If you’re pursuing product‑led growth, a fast “Pendo Wrapped” is one of the highest‑leverage experiments you can run this week.
Happy New Year! I’m kicking off 2026 with a behind-the-scenes look at what’s changing in my product practice, the experiments I’m running with my teams at HighLevel, and the trends I’m most energized by—especially around continuous discovery, AI workflows, and building stronger coaching cultures.
If you want to listen to the conversation that sparked many of these reflections, you can find it here: Spotify | Apple Podcasts.
Why Teresa sunset the live deep-dive cohorts—and how on-demand and the new Discovery Habits Toolbox better support real behavior change. This pivot resonated with my own experience: some skills, especially discovery habits, only stick when they’re reinforced in the flow of real product work, not just in a time-boxed cohort. In my org, we’re leaning into on-demand learning paired with manager coaching to drive durable behavior change.
What leaders actually need to coach interviewing, assumption testing, and core discovery habits inside their orgs. I’ve found that empowered product teams thrive when leaders have lightweight coaching tools, practical prompts, and clear expectations for product trios. This is less about one-off training and more about building communities of practice where deliberate practice and feedback loops become routine.
Why training is shifting toward ongoing, leader-supported learning (and how AI will accelerate the shift). AI Strategy isn’t just about tools—it’s about learning systems. For LLMs for product managers to create leverage, we need eval-driven development, privacy-by-design, and clear guardrails. I’m building AI workflows that enable managers to review interviews, spot anti-patterns, and nudge teams toward better decisions—without replacing critical thinking.
Teresa’s move into paid subscriptions and why AI content doesn’t fit the classic “design once, run for years” course model. I see the same reality in my content roadmap: the half-life of AI guidance is short. That pushes us toward subscription models, tighter feedback loops, and a more adaptive go-to-market strategy for education products.
A sneak peek into the AI tools Teresa is building for discovery work—from interview coaching to near-ready interview snapshot generation. I’m particularly excited by tooling that scaffolds better interviews, sharpens assumption testing, and speeds up synthesis without skipping the human judgment step. These capabilities map directly to where I want my teams investing time: spending less energy on admin and more on learning from customers.
Petra’s plans for the year: community building with Product at Heart, a new product leadership email course, her Product Leadership Wheel, and workshops launching in Cairo. As someone who believes in conferences as high-quality “energy wells,” I’m inspired by how these programs create momentum for leaders who are upgrading their coaching muscles.
The role of conferences and retreats in staying grounded, inspired, and connected. I treat these gatherings as strategic resets—spaces to test ideas, confront blind spots, and deepen my network for future collaboration. The best outcomes often come from serendipitous hallway conversations and hands-on sessions where you can pressure test frameworks with peers.
How Teresa is staying on top of academic research (and why “synthetic users” aren’t ready for prime time). I agree: while synthetic data can be useful for scaffolding, it’s not a substitute for direct customer contact. Combine academic rigor with real-world interviewing and strong data governance—especially when operating under General Data Protection Regulation (GDPR).
The shared challenge of evaluating vendors and conference speakers making questionable AI claims. My heuristic: ask for clear problem statements, reproducible evaluations, grounded benchmarks, and a path to safe deployment. If a pitch can’t show measurable uplift or ignores compliance, it’s not ready for empowered product teams.
Key takeaways I’m carrying into 2026: delivery models matter; leaders need coaching tools, not just training; AI is reshaping how we teach and learn; experimentation is the theme of 2026; and community still energizes. That’s the blueprint I’m using to strengthen continuous discovery, refine our AI workflows, and sustain high standards in product management leadership.
What about you? How are you integrating AI workflows into your discovery practice, and what coaching tools are helping your managers reinforce the right habits? Share your approach—I’d love to learn what’s working in your context.
Resources & Links:
Follow Teresa Torres: https://ProductTalk.org
Follow Petra Wille: https://Petra-Wille.com
Teresa’s website: Product Talk
General Data Protection Regulation (GDPR)
Product Talk Academy
Deliberate Practice – ATP episode where Teresa talked about the ending live cohorts for Deep Dive classes
2026 is closer than it feels, and the signals are already clear. I’ve been synthesizing what I’m seeing across empowered product teams, boards, and cross-functional partners into a practical view of what matters next. A sharp look at product management trends for 2026. Not guesses, but signals from top product leaders shaping how PMs will actually work next.
In this analysis, I distill eleven shifts that are changing the craft—from outcomes vs output OKRs and continuous discovery to stronger product strategy and tighter product roadmapping and sprint planning. The throughline is simple: prioritize customer value, ship with focus, and measure what moves the business. These aren’t headline trends; they’re working patterns I’m seeing across high-performing organizations.
AI is no longer a side project—it’s part of the product manager’s core toolkit. Agentic AI, LLMs for product managers, and trustworthy AI workflows are accelerating discovery, sharpening problem framing, and enabling faster iteration. The best teams pair this with disciplined evaluation and experimentation, so insight compounds without sacrificing safety, privacy, or product quality.
Execution is getting crisper through product trios and stronger stakeholder management. When design, product, and engineering co-own discovery and delivery, teams reduce handoffs and increase clarity. That alignment translates into better prioritization, fewer context-switches, and a roadmap that reflects real trade-offs—not wish lists.
On growth, product-led growth remains a durable engine when it’s anchored in a compelling value proposition and instrumented end-to-end. Clear activation moments, in-app guides, and thoughtful product tours outperform brute-force acquisition. When we connect these motions back to product strategy and the roadmap, we create a repeatable loop that compounds adoption and retention.
Governance and trust are now table stakes. Privacy-by-design, data governance, and a pragmatic approach to regulatory compliance protect both users and velocity. Teams that build these practices into their operating model move faster because they avoid late-stage rework and maintain stakeholder confidence.
If you’re leading a product org—or aspiring to—this is your field guide to 2026. I’ll unpack where these shifts are strongest, how to apply them in your context, and the pitfalls to avoid. The aim is to give you clear language, concrete practices, and a sharper edge as you shape what your team builds next.
Support tickets are the rawest signal of product truth. Leading product teams at HighLevel, I’ve learned that the fastest way to build what customers value is to transform frontline conversations into a repeatable, data-driven system for discovery, prioritization, and execution.
What if your support and product teams could unlock CX insights to turn every ticket into strategic product intelligence? Explore how.
Here’s the operating system I rely on. First, I connect our support stack (think Intercom and our CRM integration) into a unified analytics platform so every conversation, tag, and resolution is queryable. I don’t just count tickets—I segment them by product area, customer segment, lifecycle stage, and revenue impact to reveal patterns that roadmaps can act on.
Next, we standardize a shared taxonomy. Agents apply concise, high-signal labels (problem type, severity, intent), and we augment that with AI-driven auto-tagging to reduce noise and improve recall. The result is trustworthy “voice of the customer” data that product managers and support leaders can both stand behind.
Prioritization then becomes rigorous and fair. I weight themes by severity, frequency, ARR exposure, and time-to-value, and tie them directly to outcomes vs output OKRs. Amplitude analytics helps me quantify impact—what’s breaking activation, what’s dragging conversion, what drives retention analysis—so the backlog reflects business outcomes, not opinions.
Discovery is continuous by design. Product trios (PM, design, engineering) run weekly reviews of the highest-signal themes, recruit users straight from recent tickets, and prototype solutions quickly. We validate ideas with A/B testing when appropriate and ship targeted in-app guides to reduce confusion before it becomes a ticket.
Crucially, we close the loop. When we release a fix or improvement, we notify affected customers and the agents who flagged the issue. We track downstream effects—ticket deflection, CSAT, feature adoption, and time-to-resolution—so everyone sees how customer support ai strategy accelerates product-led growth.
This approach also builds culture. Empowered product teams treat support as a strategic partner, not a cost center. Agents become co-creators of the roadmap, and PMs gain a steady stream of product discovery opportunities grounded in real user outcomes.
If you’re getting started, a simple 30-60-90 can help: in 30 days, unify the data and agree on taxonomy; in 60, instrument dashboards and adopt a weekly insights ritual; in 90, align priorities to OKRs, launch targeted fixes, and measure business impact. That’s how tickets turn into product truth—and how CX insights drive compounding wins.
Inspired by this post on Amplitude – Perspectives.
I’ve sat in countless AI measurement debates and noticed a recurring gap. One major voice has been noticeably underrepresented in the AI measurement conversation: the product manager (PM) that’s leading development. From experience, PMs and developers do need different measurement tools—and making those differences explicit is exactly what speeds up decisions and improves outcomes.
Developers optimize the model and system layer. Their toolkit centers on eval-driven development: offline evals, regression suites, red-teaming, latency and throughput monitoring, token cost tracking, and hallucination rate reduction. On the delivery side, engineering teams watch DORA metrics alongside CI/CD performance to keep iteration fast and safe. When building LLM-backed experiences, they also care deeply about retrieval-first pipeline quality and context window management because those mechanics determine grounding, relevance, and consistency.
PMs, by contrast, own outcomes. We instrument user journeys end to end and define a clear north-star tied to value: activation, time-to-value, task success rate, retention analysis, support deflection, and revenue contribution. We rely on A/B testing frameworks and minimum detectable effect (MDE) planning to separate real impact from noise, and we consolidate behavioral signals in a unified analytics platform like Amplitude analytics and Pendo to understand adoption, friction, and cohort differences. This is the heart of product-led growth and continuous discovery: evidence, not anecdotes.
The fact that these toolboxes differ is a strength, not a weakness. Specialized metrics keep responsibilities crisp: developers guarantee model quality and reliability; PMs guarantee that quality translates into customer and business outcomes. What we need is an explicit metrics ladder that connects layers—model-level quality floors and SLOs, feature-level KPIs, and company-level results—so trade-offs are transparent and prioritization is principled.
In practice, I create a shared measurement contract for every AI initiative. It links eval sets to user-facing success criteria, defines acceptance thresholds, and spells out observability across the stack. We include governance from day one—AI risk management, privacy-by-design, and data governance—so we can scale responsibly without slowing teams down.
Here’s the AI product toolbox I give my teams: start with a concise value hypothesis; define a success rubric the customer would recognize; instrument the happy path and the failure path; plan experiments with MDE up front; segment results by persona and job-to-be-done; and close the loop with qualitative feedback inside the product via in-app guides, product tours, and lightweight surveys. For AI features specifically, add Agent Analytics for agentic AI, capture grounding sources for explainability, and log model/context inputs to make debugging and iteration repeatable. That way, LLMs for product managers stop being magic and start being manageable.
When we roll out a new assistant—whether a retrieval-augmented copilot or a voice AI agent—we set two dashboards: one for developers (eval pass rates, latency, context integrity, error budgets) and one for PMs (activation, task completion, deflection, satisfaction). The dashboards read differently by design, yet they are joined at the hip by shared definitions and experiment IDs. This lets us move quickly with confidence: engineering can tighten quality loops while product steers toward the outcome that matters most.
If you’re feeling the tension between model metrics and product metrics, don’t collapse them—connect them. Start with a thin slice, agree on 3–5 measurable outcomes, and let your evals and A/B tests work together. With a clear metrics ladder and a unified analytics platform, PMs and developers can each excel at their craft and still ship AI that customers love.
Buy-in isn’t a single meeting; it’s a designed journey. Over the years leading product strategy at HighLevel, I’ve learned that the fastest way to earn durable support is to reduce uncertainty, align on outcomes, and create visible momentum. Explore how to get buy-in from stakeholders with practical strategies, clear communication tips, and proven methods used by the best. Here’s the 7-step playbook my teams and I rely on to move from idea to aligned action.
Step 1 — Anchor on outcomes, not outputs. I start by writing a crisp problem statement, the target customer, and the measurable outcome tied to our North Star metric. I translate this into outcomes vs output OKRs so every stakeholder can see the difference between what we’ll ship and what we intend to change. This framing keeps discussions grounded in impact, not features.
Step 2 — Map stakeholders and incentives. Effective stakeholder management begins with a living map: economic buyers, executive sponsors, influencers, and operators. I capture each person’s goals, risks, and decision cadence. When I speak to Finance, I foreground cost and runway; with Sales, I emphasize pipeline and win rate; for Customer Success, I speak to retention and NPS. Meeting stakeholders where they are builds trust quickly.
Step 3 — Co-create early with the product trio. I pull the product trios (PM, Design, Engineering) into continuous discovery with GTM partners to validate assumptions and de-risk the solution. This is where empowered product teams shine—rapid discovery sprints, early prototypes, and clear learning objectives. Co-creating exposes blind spots early and transforms critics into champions.
Step 4 — Socialize a narrative, not a deck. Before any formal review, I circulate a short narrative memo that ties our product strategy to a clear value proposition, competitive differentiation, and go-to-market strategy. I include options and trade-offs so stakeholders feel invited to shape the path, not just stamp approval. Pre-wiring conversations ensure that the “meeting” is simply the last 10% of the decision.
Step 5 — Back the story with data and a viable plan. I combine retention analysis, funnel metrics, and customer evidence to demonstrate opportunity size and risk reduction. Then I outline a phased approach with product roadmapping and sprint planning, milestones, and success metrics. I highlight the smallest viable bet that proves value fast, along with contingency paths if we learn something unexpected.
Step 6 — Design the decision. I define the decision we need, by whom, and by when. The decision doc includes the problem, options, risks, mitigations, and the explicit ask. I schedule 1:1s to address concerns, then run a focused review with clear roles and time-boxed discussion. Clarity about the decision—and the criteria—prevents drift and protects timelines.
Step 7 — Sustain momentum post-approval. After the green light, I convert the plan into execution cadences: weekly demos, transparent dashboards, and QBRs vs OKRs check-ins to reinforce outcomes. We celebrate learning milestones, not just launches, and keep stakeholders informed with concise updates that tie progress to the original outcomes and value proposition. Momentum is the best antidote to second-guessing.
Clear communication and a repeatable process turn buy-in from a hurdle into a habit. When stakeholders see a compelling narrative, credible evidence, and a path to value, they don’t just approve—they advocate. Follow these seven steps and you’ll build alignment faster, ship smarter, and strengthen trust across the organization.
Keeping employees informed and engaged isn’t just a communications challenge—it’s a product challenge. When we treat internal tools like products with clear activation moments, measurable outcomes, and continuous discovery, adoption moves from hope to habit. Over the years, I’ve seen small changes in how we onboard, communicate, and measure compound into dramatically higher engagement, better compliance, and faster time-to-value.
“How to improve onboarding, compliance, and internal communications within your employee tools.” That question guides my approach end to end—from the moment someone logs in for the first time to the day they become an expert, championing best practices across their team.
First, I personalize onboarding to accelerate user activation. I map the critical first actions and design a lightweight sequence of product tours and in-app guides that surfaces only what matters right now. Progressive disclosure, clear UX writing, and thoughtful tooltip design reduce cognitive load. I measure time-to-first-value, A/B test checklist microcopy to remove friction, and use Intercom or Pendo to deliver contextual walkthroughs by role, location, and permission level. Amplitude analytics helps me validate that the guided path leads to the intended activation event and sustained usage.
Second, I make compliance effortless and measurable. Instead of long trainings, I embed micro-learnings and policy nudges directly in the flow of work, with just-in-time prompts and short, scenario-based confirmations. I segment by role to avoid alert fatigue and localize where regulations require nuance. Completion rates, quiz accuracy, and time-to-complete are tracked alongside qualitative feedback. When compliance messaging underperforms, I run A/B testing on tone, timing, and format, then iterate until adherence is both higher and faster.
Third, I orchestrate internal communications as lifecycle messaging—not announcements. Employees get targeted release notes, role-specific tips, and in-app reminders aligned to their stage: new, adopting, proficient, or champion. I avoid channel sprawl by making the primary source of truth available in the product, then reinforcing it via email or chat only when necessary. CRM integration and audience rules ensure relevance, while a champions network and office hours create human touchpoints that deepen trust and accelerate adoption.
Fourth, I close the loop with analytics and continuous discovery. I instrument key events and run retention analysis to understand which behaviors predict long-term engagement. I look at cohorts before and after a new guide or product tour, and I compare lift in user activation and feature adoption over 14-, 28-, and 90-day windows. Amplitude analytics provides the behavioral picture; surveys, interviews, and passive feedback widgets explain the why. Together, these inputs power a product-led growth approach for internal tools—observable, repeatable, and improvable.
When teams ask where to start, I pilot one persona, one workflow, and one high-value outcome. I define the activation event, instrument it, launch a single targeted in-app guide through Pendo or Intercom, and A/B test the onboarding microcopy. Two weeks later, I review retention cohorts and completion data, talk to users, and either scale the pattern or iterate. That cadence builds credibility quickly because it ties every communication to a measurable result.
The payoff is tangible: faster onboarding, higher compliance, clearer internal communications, and employees who feel supported rather than overwhelmed. With disciplined messaging, smart instrumentation, and ongoing discovery, we can turn internal tools into catalysts for performance—and transform engagement from a campaign into a culture.
