I’ve learned to spot durable growth early. When we launch something new, I look for one deceptively simple signal that predicts whether the product will compound or stall: the percentage of users who come back one week later. It’s a small number with big implications for product-led growth and retention analysis.
Discover why 7% of users returning after one week signals long-term growth, and how early activation separates top-performing products from the rest.
Why does this matter so much? A 7% day-7 retention floor tells me we’ve earned a second interaction from a meaningful slice of our cohort, not just a curiosity click. That’s the first hint of habit formation and repeatable value—evidence that onboarding, user activation, and the core value proposition are doing their job. When the curve holds at or above this threshold, growth investments tend to work harder because cohorts keep giving back.
The lever behind that signal is early activation. I define the activation moment as the first time a new user experiences product value—sending a first campaign, integrating a CRM, or completing a workflow that solves their primary job. If we reduce time-to-activation and increase the activation rate, day-7 retention rises. This is where in-app guides, product tours, and thoughtful tooltip design shine: they remove friction without overwhelming the user.
Instrumentation is non-negotiable. I set up event tracking and cohort analysis in tools like Amplitude analytics and Pendo, define a crisp activation event, and review retention curves by first-seen cohorts. We run A/B testing with a clear minimum detectable effect (MDE), validate improvements in activation and day-7 retention, and then double down. The objective is always outcomes over output: fewer features, more value delivered.
Process matters as much as tooling. Product trios using continuous discovery keep us close to user problems, while empowered product teams move faster with context and clear outcomes vs output OKRs. When we connect these practices to a unified analytics view, it becomes obvious which changes move the 7% needle and which are noise.
In practice, I’ve seen a launch turn the corner by clarifying the “aha” moment, cutting onboarding steps nearly in half, and swapping a generic walkthrough for contextual in-app guides. Activation jumped, day-7 retention crossed the threshold, and suddenly our PLG motion became efficient—paid acquisition started compounding instead of leaking.
If you’re below 7%, start by tightening the activation definition, instrument the funnel, and remove the top three sources of friction. If you’re above 7%, stabilize it across segments, scale with targeted in-app guides, and keep iterating via A/B tests to protect that early win. Either way, the rule provides a clear, pragmatic checkpoint for product discovery and growth.
The takeaway is simple: focus the team on earning the second visit. Nail early activation, then build repeatable systems that make the 7% retention rule your new baseline for confident, long-term growth.
Inspired by this post on Amplitude – Perspectives.
I spend a lot of time turning strong product capabilities into enterprise wins, and that almost always starts with a tight partnership between product management and product marketing. The most effective go-to-market strategy is built where customer insight, product value, and revenue goals intersect—and product marketers are the connective tissue that makes this real.
“Michele Morales is a product marketing manager at Amplitude, focusing on go-to-market solutions for enterprise customers”
In my experience, partnering with product marketing leaders on enterprise go-to-market means aligning early on the ICP, the value proposition, and the differentiated messaging that sales can activate. We map buyer committees, refine product positioning against points of parity and competitive differentiation, and ensure our narrative translates cleanly from website to demo to proof-of-concept.
For data-driven execution, I lean on Amplitude analytics and a unified analytics platform approach to validate our hypotheses. We set clear activation and adoption milestones, monitor user activation cohorts, and close the loop with retention analysis to understand which messages and features actually move enterprise accounts from trial to expansion. This is where product-led growth complements sales-led motions, giving us empirical signal across the funnel.
On the launch front, we pressure-test enablement and in-product experiences together: crisp messaging frameworks, in-app guides, and product tours that shorten time-to-value for complex enterprise use cases. The result is a go-to-market strategy that’s both technically accurate and emotionally resonant—clear enough for executives and actionable for end users.
What consistently works: start with real customer pain, express value succinctly, and make the path to first success obvious. Then instrument everything. When product, marketing, and sales can all see the same truth in the data, empowered product teams iterate faster, positioning sharpens, and adoption compounds.
This approach respects the craft of product marketing while grounding decisions in measurable outcomes. It’s how we turn a promising roadmap into repeatable enterprise impact—and why close PM–PMM collaboration remains one of my most reliable growth levers.
Inspired by this post on Amplitude – Best Practices.
I’ve learned that the most powerful AI features rarely emerge from lone-wolf brilliance—they’re born when a community rallies around a shared objective. “Building Amplitude’s AI for insight automation felt a lot like the fable of travelers making stone soup with their community.” That spirit captures how I approach shipping AI for analytics: bring focused ingredients, invite contributions, and let rigorous evaluation transform the result into something extraordinary.
At the core is Eval-Driven Development. Rather than debating preferences, we define explicit evaluation sets, success thresholds, and guardrails, then wire them into CI/CD so every change improves reliability, quality, and relevance. For AI-driven analytics, our evals combine offline judgment tests (precision, recall, hallucination rates), user-centric measures (time-to-insight, actionability), and production health signals (failure modes, latency). When the bar rises, the product improves—continuously and measurably.
We made “stone soup” by inviting contributions from every function. Data science established gold-standard datasets and baselines. Engineering implemented retrieval, orchestration, and safe deployment paths. Product and design framed high-value use cases, in-app guides, and UX writing that clarified intent. Customer success and support piped real-world edge cases into our evals so the system improved where it mattered. Product trios kept us outcome-focused and empowered product teams moved quickly without sacrificing governance.
Why this matters for analytics: AI insight automation reduces the heavy lift of exploring funnels, cohorts, anomalies, and retention patterns—accelerating activation and product-led growth. With a unified analytics platform and strong data governance, we can surface relevant patterns proactively, explain the “why” behind movements, and recommend next best actions without drowning users in noise. The result is faster decisions, cleaner handoffs between teams, and a tighter loop from observation to intervention.
Our practical playbook is simple but strict: define a clear north-star outcome; curate representative eval sets that mirror real user questions; simulate A/B testing offline before live traffic; instrument time-to-insight and adoption; and integrate evals into CI/CD so regressions never ship. We monitor DORA metrics to maintain delivery velocity while holding quality lines, and we use human-in-the-loop review to continuously refine prompts, patterns, and explanations.
We also learned what doesn’t work. General-purpose prompts seldom transfer cleanly to analytics without domain grounding and context window management. A retrieval-first pipeline improves factuality, but only if metadata and event taxonomies are consistent. And while generative UX can delight in demos, it must earn trust in production through transparent reasoning, privacy-by-design, and predictable behavior under load.
In the end, the stone soup metaphor isn’t about cute storytelling—it’s about disciplined collaboration. When a cross-functional community contributes the right ingredients and Eval-Driven Development keeps us honest, AI for insight automation becomes both credible and compounding. That’s how we turn analytics into action—and how we ship AI products that users rely on every day.
Inspired by this post on Amplitude – Best Practices.
In fast-moving product cycles, weak signals slow teams down and let avoidable issues linger. I’ve been leaning on Session Replay to strengthen those signals and align stakeholders faster, especially when we’re balancing roadmap bets with day-to-day reliability fixes.
Discover how frustration analytics, error analytics, and shareable filters in Session Replay help you spot problems faster and collaborate more effectively.
Frustration analytics has become my shortcut to the moments that truly matter. Instead of sifting through countless replays, I start where friction peaks and focus on the sessions that best represent real user pain. In one onboarding flow, these insights pointed us to a confusing step that was suppressing user activation; a simple adjustment to the layout and copy led to higher completions and fewer support tickets.
Error analytics turns anecdotes into evidence. By pairing error trends with conversion and retention analysis in Amplitude analytics, we isolate the defects with the highest customer and revenue impact. That clarity helps my team sequence fixes in sprint planning with confidence—and it gives leadership a clean narrative for why certain issues deserve priority now.
Shareable filters have been a quiet superpower for cross-functional collaboration. I create saved views for specific cohorts—first-time users, power users, or high‑value accounts—so engineering, design, and support can reproduce exactly what I’m seeing in Session Replay. No more screen recordings in Slack or back-and-forth on “what filters did you use?” Everyone starts from the same context and moves to decisions faster.
This workflow fits naturally into how our product trios practice continuous discovery. We pick one question each week, open a shared filter, and review a handful of targeted sessions together. Within the same unified analytics platform, we connect what we observe to metrics that matter, then translate insights directly into product roadmapping and sprint planning without losing momentum.
If your goal is sharper detection of issues and stronger collaboration across stakeholders, these capabilities deserve a place in your toolkit. They compress time-to-insight, improve stakeholder management, and fuel product-led growth by focusing attention where it delivers the most customer value.
Inspired by this post on Amplitude – Best Practices.
I’ve spent enough cycles in product and growth to know the biggest drag on experimentation velocity isn’t creativity—it’s waiting. Waiting for engineering to wire events, for analysts to pull cohorts, for approvals to trickle in. When marketers can move autonomously with the right guardrails, learning accelerates and impact compounds.
“Amplitude’s new web experiment capabilities enable teams to scale experimentation 3X faster without waiting for help.” That promise hits directly at the bottlenecks I see most often across product and marketing organizations.
My takeaway: the real unlock isn’t only speed; it’s confidence. Faster learning loops power continuous discovery and product-led growth, but only if teams trust the data, align on success metrics, and can iterate without creating downstream tech debt. Self-service done right transforms scattered tests into a durable growth engine.
From a VP of Product lens (and what we practice at HighLevel), self-service experimentation means more than a new UI. I look for governance-by-design, role-based permissions, clear metric definitions, pre-built test templates, and operational best practices like minimum detectable effect (MDE) sizing and traffic allocation standards. That mix keeps A/B testing fast, statistically sound, and repeatable—without piling work onto engineering.
Here’s the playbook I recommend to teams leaning into this shift: instrument a unified analytics platform and lock a shared taxonomy; define canonical success metrics and guardrails; require lightweight pre-registration for hypotheses and MDE; stand up weekly experiment reviews; and close the loop by sharing learnings in-product and across go-to-market. When marketers, PMs, and designers operate as an empowered product trio, the flywheel spins.
To maximize value from any web experimentation stack—Amplitude analytics included—connect the dots from insight to activation. Tie experiments to CRM integration for downstream campaigns, ensure user activation metrics are first-class citizens, and keep your experimentation backlog aligned to outcomes, not outputs. The goal is fewer opinions and more evidence, shipped continuously.
Self-service also requires culture. Set expectations around statistical rigor, data governance, and post-test decisions, then celebrate the teams that sunset ideas just as quickly as they scale winners. That’s how you reduce waste, build confidence, and keep momentum high without creating hidden operational costs.
If your marketers are still waiting in ticket queues, it’s time to raise the bar. With the right foundations and process, you can go from idea to live test in hours, not weeks—learning more, shipping smarter, and unlocking 3X faster cycles where it matters most: customer value.
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.
When I push our organization to adopt the product operating model, I’m emphasizing a foundational shift—from “shipping roadmaps of features (output)” to solving real customer and business problems, measured by “business results (outcomes)”. That’s the difference between activity and impact, and it’s the only way to build durable value at scale.
This change inevitably reaches beyond the product organization. It reshapes how company stakeholders in Sales, Marketing, Customer Success, Finance, Legal, Security, and Operations engage with product teams, and it reframes what they expect from us. Instead of asking, “When will feature X ship?” they learn to ask, “How will we move the outcome that matters?”
In practice, the product operating model is a contract: product teams commit to outcomes, and stakeholders commit to partnership. That partnership means we co-own the problem, align on evidence, and share accountability for results. The reward is clarity—everyone sees how their work ladders to strategy and why the sequence of work makes sense.
Here’s how I align stakeholders around this model. First, I ground everything in outcomes vs output OKRs. We replace feature roadmaps with a clear strategy, prioritized problems, and measurable objectives. Our product roadmapping and sprint planning then serve the objectives—not the other way around—so capacity is allocated to the highest-leverage bets.
Second, I build empowered product teams around product trios (product, design, engineering). We practice continuous discovery with stakeholders: we share opportunity trees, test riskiest assumptions early, and bring partners into research when it informs go-to-market strategy, pricing, or enablement. This keeps us honest and avoids late-stage surprises.
Third, I establish operating rhythms that make outcomes visible. Monthly stakeholder reviews focus on progress toward objectives and what we’re learning—not status theater. Quarterly, we connect OKRs to business performance so leaders can see the throughline from discovery and delivery to pipeline, retention, or margin. If priorities shift, we renegotiate objectives explicitly.
Fourth, I define metrics that stakeholders trust. We use a balanced set of leading indicators (activation, engagement, cycle time) and lagging indicators (revenue, retention, unit economics). We socialize definitions early so no one debates the scoreboard mid-game. The result: faster decisions and less “data whiplash.”
Fifth, I invest in change management. Moving from outputs to outcomes can feel threatening if your success has historically been measured by launch volume or roadmap commitments. I address this head-on with training, transparent comms, and clear decision rights. The message is simple: outcomes create more autonomy for empowered product teams and more predictability for stakeholders.
At HighLevel, this approach has been especially powerful when cross-functional dependencies are high. For example, when we set an objective to improve user activation for a new CRM integration, we didn’t promise a bundle of features. We committed to a measurable lift in activation and a shorter time-to-value, co-owned with Customer Success and Marketing. That alignment unlocked smarter experiments, tighter enablement, and a more credible launch narrative.
The anti-patterns are predictable: treating OKRs as a renaming of the roadmap, equating discovery with indecision, or isolating product decisions from go-to-market strategy. The cure is equally consistent: bring stakeholders into discovery, attach every bet to an objective, and show progress with evidence—not just demos.
Ultimately, the product operating model is a leadership choice. It asks us to trade certainty theater for learning velocity, and feature checklists for business impact. When stakeholders see that shift pay off—in faster cycles, clearer priorities, and results that matter—support for the model moves from compliance to conviction.
By 2026, the AI Product Owner will be the keystone role that turns AI strategy into measurable business outcomes. In my teams, this seat bridges market insight, model capability, data governance, and shipping velocity—so product decisions are not just clever, but compliant, reliable, and fast.
I often describe the remit simply: "Here is your clear guide to the AI product owner role (skills, responsibilities, how it differs from PM) and ways AI tools supercharge delivery." In practice, the AI Product Owner translates business goals into model-backed experiences, aligns cross-functional execution, and ensures the product’s AI behavior remains safe, lawful, and on-brand under real-world constraints.
How does this differ from a traditional PM? While Product Management sets portfolio strategy, positioning, and market narratives, the AI Product Owner owns the AI experience end-to-end—data readiness, evaluation harnesses, safety guardrails, and the iterative model improvements that drive outcomes vs output OKRs. I anchor the role inside empowered product teams and product trios (PM/Design/ML Eng) to keep discovery continuous and delivery disciplined.
On responsibilities, I expect four pillars. First, discovery: continuous discovery with customers and internal experts to uncover use cases where generative AI or LLMs beat the status quo. Second, experience: define the right interaction patterns for AI UX, including retrieval-first pipeline choices, context window management, and feedback loops for human-in-the-loop correction. Third, governance: privacy-by-design, AI risk management, data governance, and regulatory compliance baked into the roadmap. Fourth, delivery: CI/CD for models and prompts, observable evaluation with A/B testing and minimum detectable effect (MDE), and SRE-grade incident management when AI behavior drifts.
Skills-wise, I look for product sense plus technical fluency. That includes LLMs for product managers (prompting, grounding, RAG), analytics mastery (Amplitude analytics, retention analysis, activation metrics), and comfort with DORA metrics and deployment frequency to keep iteration high but safe. Strong stakeholder management and clear writing are non-negotiable—AI capabilities evolve fast, and leaders must see risk, cost, and ROI with no ambiguity.
AI tools truly supercharge delivery when they eliminate bottlenecks. My practical stack: an AI product toolbox with Claude Code and a ChatGPT connector for rapid prototyping; CustomGPT workflows for support triage and internal knowledge; Pendo product tours and in-app guides to validate behavior changes; Intercom for customer support ai strategy; and tight CRM integration via HubSpot to measure revenue impact. The outcome is faster idea-to-learning cycles, sharper telemetry, and far cleaner handoffs.
For roadmapping, I prioritize thin slices that prove value early—shipping narrowly scoped assistants or copilots, then expanding with product roadmapping and sprint planning that ties capability unlocks to outcomes. A unified analytics platform helps compare human-only baselines to augmented workflows, while agentic AI patterns automate routine steps under strict guardrails.
Risk is a product surface, not a side task. I require explicit policy gates (PII handling, red-teaming, bias audits), clear escalation paths, and incident playbooks. When we treat policy and reliability as features, customers reward us with deeper adoption and higher trust.
If you’re pursuing the AI Product Owner path, build a portfolio around shipped learnings: the experiment you killed with data, the safety constraint you designed, the postmortem you led, and the business metric you moved. That story—evidence of disciplined discovery, responsible delivery, and real-world results—is exactly what teams (and boards) want to see in 2026.
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.
When AI Agents resolve the majority of customer conversations, the shape of your support team has to change. I’ve experienced this shift firsthand: the moment AI begins to carry the volume, your people must pivot from answering individual questions to engineering the system that consistently delivers quality outcomes.
The old tiered model built around queue management, handoffs, and volume-based productivity no longer fits. AI now handles the bulk of customer interactions, and that changes the role of your human team entirely. Responsibilities evolve, and success is measured differently. It goes beyond just adding automation to existing ways of working. You’re building an operating model that’s entirely new.
Most teams don’t hire a dedicated AI function from day one. They start by distributing a few critical responsibilities across existing team members, and formalize those responsibilities as AI becomes central to how support works. That’s exactly how I recommend getting momentum without over-hiring too early: prove value fast, name clear owners, and then scale.
Once you have executive support and a clear strategy in place, these are the four foundational roles we believe are key to getting AI off the ground in a meaningful way:
Skillset/background: Often promoted from support ops. Deep understanding of workflows, systems, and tooling. Strong analytical and cross-functional coordination skills.
Why you need this: Without clear ownership, performance drifts. This role ensures the AI Agent constantly improves.
AI isn’t replacing support—it’s opening doors. This visual highlights how GenAI is spawning roles in customer success, from digital support engineers to automation success teams, and unlocking clearer, upward career paths.
In my teams, this role becomes the heartbeat of AI performance—instrumenting quality feedback loops, triaging failure modes, and aligning fixes across product, data, and support ops.
2. Knowledge manager
Responsibilities: Owns macros, snippets, and help content. Maintains structured, accurate inputs the AI Agent depends on.
Skillset/background: Often promoted from support ops. Deep understanding of workflows, systems, and tooling. Strong analytical and cross-functional coordination skills.
Why you need this: Without clear ownership, performance drifts. This role ensures the AI Agent constantly improves.
Every generative AI system is only as good as its knowledge. I’ve learned the hard way that inconsistent or stale content erodes trust—both for customers and internal stakeholders. A rigorous knowledge manager prevents that.
3. Conversation designer
Build a winning AI support team with four core roles: an ops lead to drive quality, a knowledge manager to keep content accurate, a conversation designer for tone and flow, and an automation specialist to power customer actions.
Responsibilities: Designs how the AI Agent communicates by focusing on tone of voice, structure, handoff logic, and interaction flow. Tunes how responses feel.
Skillset/background: Background in content design, UX writing, or support enablement. Deep grasp of policy, CX standards, and conversational nuance.
Why you need this: This role ensures the AI Agent speaks like your brand – clearly, helpfully, and in line with customer expectations.
This is your brand’s voice in motion. A strong conversation designer sets the guardrails that keep interactions on-brand, compliant, and empathetic while still efficient.
4. Support automation specialist
Responsibilities: Builds workflows and backend actions the AI Agent can execute.
Skillset/background: Background in support engineering, systems, or tooling. Works closely with product and engineering teams.
AI in customer service thrives with player‑coaches—hands‑on leaders who build, mentor, and iterate with the team. This quote-driven graphic signals a move away from heavy management toward agile, coaching‑first support operations.
Why you need this: Enables the AI Agent to take action – not just respond. This role translates customer intents into business systems.
In practice, this role unlocks the jump from “answering” to “resolving.” They wire up secure actions, map intents to outcomes, and partner with engineering to keep latency low and reliability high.
Introducing new AI-first roles doesn’t mean your existing functions disappear. But they do need to evolve. For AI to scale effectively, every function in your support organization must shift its focus from managing queue-level activity to improving the system’s performance:
Enablement trains human agents to work with the AI Agent: managing handoffs, tuning responses, and understanding how to give feedback that improves the system.
QA evolves from reviewing conversations to reviewing the quality of the customer experience and behavior of the AI Agent: where the AI succeeds, where it falls short, and how the system as a whole performs.
Workforce management plans capacity based on automation coverage, not just inbound volume.
You’ll also need a new kind of leadership to make this model work. The traditional support leader doesn’t map cleanly to an AI-first organization. You need a new layer: leaders who are part strategist, part operator. They roll up their sleeves to analyze the AI Agent’s performance, refine content, and debug handoffs, but they also coach the team through a new way of working.
Customer service is reorganized for the AI era: a VP of Support leads human support, ops and optimization, and a new AI support function—adding conversation design, knowledge management, and systems analysis alongside agents, insights, and WFM.
This is the “player-coach model” – leaders who actively shape both the system and the people within it.
These leaders see the AI Agent as a teammate to manage, not just a tool to monitor. They can’t be purely people leaders or purely systems thinkers. They need to be both, and they’re emerging as a critical hire in support right now.
Some teams are restructuring their organizations around the AI Agent as a core product, not just a support tool. Here are some real-world examples:
At Dotdigital, a dedicated “Fin Ops” specialist role was created to refine content and improve AI performance.
At Clay, a dedicated GTM engineer role has been established as part of the ops team with a focus on making support more efficient at scale using Fin. Additionally, a support engineering function has been embedded directly in the CX organization to help reduce volume by fixing bugs and building internal tools.
Lightspeed created a dedicated Digital Engagement team to manage Fin’s optimization, and formalized a triangular model that brings together technical teams, frontline experts, and content specialists.
In my experience, the most resilient org designs align around three pillars: Human Support, AI Support, and Support Operations and Optimization. Each pillar carries distinct ownership yet shares accountability for AI performance. That structure keeps the team focused on outcomes over output and makes continuous improvement everyone’s job.
AI shouldn’t replace your agents—it should elevate them. This Rocket Money quote highlights a modern support model where automation handles the busywork and people concentrate on high‑value, human moments.
Once AI Agents handle most conversations, your team’s work moves from “answering questions” to “designing and improving the system that answers questions.” They become the force that steers quality, rather than the one that carries the volume.
This is why new roles are important. It’s not because they’re trendy, but because the performance of your support organization now depends on the performance of AI, and no AI Agent succeeds without clear ownership of content, behavior, workflows, and improvement cycles.
That’s the pattern we’ve seen from working with so many teams:
They name owners early.
They distribute responsibilities before they formalize them.
They anchor teams around AI outcomes, not ticket outcomes.
And they hire leaders who can manage both the system and the people.
If you take one thing away from this week’s article, let it be this: if AI is going to handle the majority of your customer conversations, your team needs to be designed to help it do that well.
Your roles, responsibilities, and leadership approach are now part of the architecture of AI performance.
Next week, we’ll go deeper into how these roles actually operate day-to-day – the workflows, responsibilities, rhythms, and collaboration patterns that make an AI-first support organization run.
