Category: Product Management Leadership

From KPIs to Comebacks: How I Lead Through Setbacks with Curiosity, Care, and Discovery

Setbacks are the tax we pay for doing meaningful product work. As a VP of Product Management, I’ve learned that what separates resilient teams from the rest isn’t a lack of failures—it’s how we metabolize them. This episode of All Things Product with Teresa Torres and Petra Wille is a powerful reminder that recovery, reflection, and rigorous product discovery are as essential as speed and execution.

Listen to this episode on: Spotify https://open.spotify.com/episode/10LYRya7boYJBHTYBnE79E?ref=producttalk.org | Apple Podcasts https://podcasts.apple.com/kh/podcast/dealing-with-setbacks/id1794203808?i=1000737190520&ref=producttalk.org

What struck me most is how Teresa shares a deeply personal story about her long recovery from an injury—and how that journey mirrors the nonlinear reality of product development. In product, just like in healing, progress is rarely a straight line. We have surges, stalls, and moments that feel like reversals. Yet with the right mindset and rituals, we still move forward.

Professionally, we all face moments when your product fails to move a single KPI, when a launch falls flat, or when you just feel stuck. I’ve been there—in quarterly reviews, post-launch standups, and board prep. The instinct is to sprint straight into solutions. The wiser move is to respond with curiosity, emotional honesty, and resilience, then re-engage our discovery habits with intention.

If you’re a PM, designer, or researcher, consider this an invitation to rebalance. Recovery and reflection are just as important as velocity and success. That’s not soft talk—it’s how empowered product teams build durable performance without burning out.

On the emotional reality of setbacks, I’ve learned to normalize naming the loss. We put immense pressure on ourselves, and it’s okay (and necessary) to grieve product failures. When we acknowledge the disappointment, we regain the ability to observe clearly—and to learn.

Leaders play a crucial role here. I create space for teams to recover before jumping into post-mortems. We don’t whiteboard over feelings; we schedule time for decompression, then conduct a crisp, blameless review. That sequencing transforms the quality of insights and strengthens psychological safety.

Another lesson that resonates is the danger of tying performance too tightly to outcomes. Outcomes matter, but they are lagging indicators influenced by many externalities. I evaluate performance on behaviors: clarity of problem framing, rigor in discovery, quality of decision-making, and stakeholder alignment. This aligns with outcomes vs output OKRs and keeps us focused on controllable excellence.

How do we build resilience? Continuous discovery builds resilience by normalizing failure. When we test assumptions routinely with customers and data, we turn large, risky bets into a series of small, learnable steps. Teams recover faster because failure becomes feedback—frequent, cheap, and informative.

For perspective, I often use the 10–10–10 framework (from Decisive by Chip & Dan Heath). I ask: How will this setback feel in 10 minutes, 10 months, and 10 years? The answers de-escalate urgency, expand our time horizon, and produce better, calmer decisions.

Here are the key takeaways I’m carrying forward. Setbacks are not just inevitable—they’re part of doing meaningful product work. Giving teams time and space to process failure builds long-term resilience. Mourning losses is just as important as celebrating wins.

Healthy discovery cultures embrace reflection, psychological safety, and emotional honesty. And most importantly, staying consistent with discovery habits helps teams recover faster and learn more deeply.

Notable moments that stood out for me include: [00:02:00] Teresa shares the story of her injury and what it’s taught her about patience and setbacks. The parallel to product cadence is both humbling and motivating.

[00:10:00] Petra talks about a team whose carefully planned launch didn’t move a single KPI. I’ve led similar debriefs; when we anchor on customer insight gaps rather than blame, the next iteration improves dramatically.

[00:20:00] Discussion on allowing space for grief and frustration after failure. In my teams, we time-box “emotional processing” before we enter analysis mode—it humanizes the work and sharpens the learning.

[00:30:00] Why organizations must decouple performance reviews from short-term outcomes. I align evaluations to strategy execution quality, hypothesis discipline, and cross-functional collaboration.

[00:40:00] How continuous discovery can help teams normalize—and even learn to appreciate—setbacks. When discovery is weekly, momentum becomes self-healing.

If you want to dig deeper, here are useful links from the episode. Follow Teresa Torres: https://ProductTalk.org

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

Mentioned in the episode: Decisive by Chip & Dan Heath — The 10–10–10 framework for perspective in decision-making https://heathbrothers.com/books/decisive/?ref=producttalk.org

Teresa Torres’ Continuous Discovery Habits — Building resilience through ongoing discovery practices. https://www.amazon.com/Continuous-Discovery-Habits-Discover-Products/dp/1736633309?dchild=1&keywords=continuous+discovery+habits&qid=1621385051&sr=8-2&linkCode=sl1&tag=teresatorres-20&linkId=34bc439ac78da06e1398f7bf069b219e&language=en_US&ref_=as_li_ss_tl&ref=producttalk.org

Join the Conversation: Have thoughts on this episode? Leave a comment below. I’d love to hear how you create space for recovery while sustaining product velocity.

Full Transcript: Full transcripts are only available for paid subscribers.

Inspired by this post on Product Talk.

November 18, 2025
Inside PendomoniumX London: AI Transformation, Real-World Wins, and Product Innovation

Walking into PendomoniumX London, I could feel the AI revolution hitting its stride. The conversations were sharper, the demos more grounded, and the outcomes more measurable—a clear signal that AI Strategy is moving from slideware to shipped value in modern product management. PendomoniumX’s sixth stop brought 350+ software leaders together for a day of AI transformation, real-world stories, and product innovation. What stood out to me was the shift from hype to execution. Teams compared playbooks for gen ai and Generative AI, shared lessons from LLMs for product managers, and showed how they’re threading AI into product discovery, product roadmapping and sprint planning, and go-to-market motions. The focus was pragmatic: drive adoption, accelerate time-to-value, and make better decisions with cleaner signals. On the product-led growth front, I saw compelling examples of using Pendo’s in-app guides and product tours to increase user activation and reduce friction in key onboarding moments. When AI-enhanced experiences are paired with clear guidance and behavioral analytics, customers don’t just try features—they build habits. What I appreciated most were the leadership narratives: empowered product teams aligning around outcomes, candid retros on where AI prototypes missed the mark, and crisp frameworks for prioritizing the highest-leverage bets. The conference networking felt purposeful, with operators trading hard-won insights on experimentation velocity, data governance, and building trust into AI-infused experiences. My takeaway: AI is no longer a side project—it’s a core capability in product management. If we anchor our AI Strategy in clear customer problems, instrument for learning, and iterate with discipline, we can consistently turn innovation into impact. And with the right mix of PLG mechanics, in-app education, and thoughtful design, those gains compound across the product lifecycle.

Inspired by this post on Pendo – Perspectives.

November 17, 2025
Vibe Check Part 2: Feel Something, Measure Everything with High-Impact Amplitude Analytics

I build products with equal parts intuition and instrumentation. When a campaign’s purpose is to spark a feeling, I still demand proof that those moments translate into measurable outcomes. Learn how you can use Amplitude to better track your vibe marketing initiatives in part 2 of our 3-part series.

Vibe marketing works when emotion and evidence move in lockstep. In my practice, I rely on Amplitude analytics as a unified analytics platform to connect the emotional resonance of a message to product-led growth—tracking how a compelling story influences user activation, retention, and revenue. The goal is simple: feel something, measure everything.

I start by instrumenting the journey around the vibe itself. That means a clean event taxonomy and consistent properties that capture the creative theme, channel, audience segment, and context (for example: campaign_id, creative_theme, entry_channel, audience_mood, landing_variant). Good data governance is non-negotiable—if the data isn’t trustworthy, neither are the insights. With this foundation, I can attribute emotional narratives to downstream behaviors with confidence.

From there, I map the funnel and define activation with intent. I track how vibe-forward touchpoints influence key milestones—first value moments, time-to-activation, and early feature engagement—then ladder those signals into retention analysis. Cohorting users by creative theme or channel helps me see which vibes convert initial curiosity into durable product habits, and which only produce short-lived spikes.

Experimentation is where the rigor shows. I use A/B testing to isolate the impact of a specific narrative, headline, or creative treatment, and I size tests based on minimum detectable effect (MDE) to avoid underpowered decisions. Guardrail metrics (activation, retention, and NPS) protect the experience while I iterate. When the numbers are tight, I supplement with directional reads—session quality, content depth, and return visits—while staying honest about causality.

Operationally, my team lives in shared Amplitude dashboards and notebooks. We annotate launches, align on outcomes vs output OKRs, and review weekly trendlines with our GTM partners. This cadence keeps empowered product teams focused on what matters: which vibes accelerate onboarding, deepen engagement, and ultimately improve unit economics. When a story resonates, the data should echo it across the funnel.

The biggest pitfalls I see are vanity metrics and disconnected systems. To avoid them, I link campaign data to product behavior, unify identifiers across tools, and ensure CRM integration so we can follow the customer journey end-to-end. The payoff is clarity: I can tell a creative team exactly which narrative unlocked user activation and which one stalled—then iterate with speed and precision.

Vibe marketing isn’t soft; it’s strategic. When we respect the craft of emotion and the discipline of measurement, we build experiences that people love and businesses depend on. If you’re ready to upgrade how you track the intangibles, Amplitude gives you the instrumentation to turn feelings into forward motion.

Inspired by this post on Amplitude – Best Practices.

November 13, 2025
The Product Positioning Statement Playbook: Build a Message That Wins and Endures

Your product positioning statement decides if you stand the test of time. I’ve seen this truth play out across launches, pivots, and category-defining moments—when the positioning is razor sharp, everything from roadmap to revenue snaps into alignment. When it’s vague, teams ship features, but customers don’t buy the story.

At HighLevel, I’ve led product trios and go-to-market teams through the hard work of distilling complex value into a single, credible promise. The pattern is consistent: the best positioning clarifies who we serve, the problem we own, the market category we play in, and the competitive differentiation that earns us the right to win.

Positioning is not a tagline or a homepage headline; it’s the narrative spine that informs value proposition, messaging, pricing, user activation, sales enablement, and product-led growth. It’s also how we drive internal focus—shaping outcomes vs output OKRs, roadmap trade-offs, and investment bets with discipline.

Here’s the anatomy I rely on: target customer and context; problem worth solving; category anchor (what buyers already recognize); value proposition (the outcome we deliver); points of parity (table stakes we meet) and points of differentiation (where we win); and proof—evidence that reduces risk for the buyer. When each element is explicit, your product positioning becomes both compelling and testable.

Use a simple scaffold to draft quickly: For [target customer], who [urgent need or job-to-be-done], [product] is a [recognized category] that [core value proposition]. Unlike [primary alternatives], it [distinct, defensible differentiation]—proven by [evidence: results, usage, social proof, or integrations]. Write it plainly enough that a sales rep can say it and a customer can repeat it.

Then pressure-test. In product discovery, validate the language with real customers—do they self-identify as the target and echo the outcome? In analytics, check if activation and retention analysis improve when onboarding, in-app guides, and product tours mirror the positioning. In go-to-market strategy, A/B test messaging in campaigns and sales conversations, and listen for shorter time-to-understanding and cleaner objection handling.

Expert products operationalize positioning across the journey. The category and value proposition show up consistently on the pricing page, inside onboarding tooltips, in CRM integration notes, and within sales collateral. Product management leadership, marketing, and sales align weekly on one narrative, and product-led growth metrics verify that narrative with behavior, not just opinions.

To write one that sticks, I take this sequence: define the narrowest viable target; articulate the must-solve problem in the customer’s words; choose a category buyers already understand; frame a value proposition that promises an outcome, not a feature; document points of parity so you don’t over-claim; highlight two to three competitive differentiation pillars; add proof; and cut jargon until a smart outsider gets it in one read.

Common failure modes include trying to be for everyone, leaning on feature soup instead of outcomes, skipping proof, and drifting from what the product can actually deliver. The fix is focus: fewer claims, clearer benefits, and evidence that eliminates buyer uncertainty.

If you need a fast start, run a 30-minute working session: five minutes to align on the target and problem, five to choose the category, ten to draft value proposition plus parity and differentiation, five to add proof, and five to define two experiments (one discovery conversation, one A/B test) that validate the language this week. Learn how other expert products do it and how to write one that sticks—then let data and customer language refine every word.

Great positioning earns clarity, confidence, and compounding advantage. When we get it right, the market tells us quickly—prospects move faster, users activate with less friction, and the team finally feels like it’s rowing in the same direction.

Inspired by this post on Product School.

November 13, 2025
Inside-Out vs Outside-In: How I Balance Both to Build Products Users Love—and CFOs Trust

Inside-out or outside-in thinking? I choose both. The strongest product strategies fuse a bold internal vision with relentless customer evidence, creating a flywheel that lifts adoption, engagement, and revenue while reducing risk.

When I lead with inside-out thinking, I articulate a clear product thesis, technical roadmap, and platform leverage. This is where we define points of parity and differentiation, sharpen our value proposition, and ensure our architecture scales. It’s disciplined, outcomes-first, and anchored in product positioning—not output checklists.

Outside-in thinking ensures that vision stays honest. I listen to customers, analyze friction in onboarding, instrument user activation, and study retention analysis to validate whether our promises translate into real user value. This is where product discovery, A/B testing, and in-app signals tell me what’s working, what needs refinement, and what we should stop doing.

In practice, I operationalize this balance through Software Experience Management. “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.” That promise captures the core of how I align strategy with reality inside the product, not just around it.

Concretely, I combine product analytics with in-app guides and product tours to accelerate onboarding and improve user activation. I run targeted experiments to de-risk decisions, and I iterate quickly based on what users actually do—not just what they say. The result is a product-led growth engine that compounds over time.

This approach also builds trust with finance and go-to-market partners. Inside-out clarity gives us confident, sequenced bets; outside-in data provides proof that those bets pay off. When engagement expands and adoption climbs, the business case writes itself.

If you’re deciding where to start, begin with three moves: define activation events aligned to your value proposition, instrument the experience end-to-end, and ship one high-impact in-app guide to remove a known onboarding blocker. Then measure, learn, and iterate—quickly.

The truth is, great products emerge when conviction meets evidence. Inside-out sets the vision. Outside-in earns the right to scale it.

Inspired by this post on Pendo – Perspectives.

November 12, 2025
AI Won’t Replace Engineers—Engineers Using AI Will: A Practical Playbook for Your Next Move

Will AI replace software engineers or reshape their roles? Explore risks, opportunities, and alternative career paths in tech.

I’m often asked whether AI will make software engineers obsolete. My short answer: AI is already automating tasks, not eliminating the role. The engineers who learn to orchestrate models, systems, and stakeholders will create more value—not less. The real shift is from keystrokes to judgment, from writing code to designing socio-technical systems that deliver outcomes.

Today’s gen ai assistants—think Claude Code and ChatGPT connector—excel at unit test scaffolding, boilerplate generation, refactoring, docstrings, and code search. When integrated into CI/CD, they can open draft pull requests, annotate diffs, and propose fixes. This lifts developer productivity and frees time for higher-leverage work: problem framing, architecture decisions, and customer discovery.

What changes in the role? We spend more cycles on product discovery, privacy-by-design, and AI Strategy, and fewer on repetitive implementation. We design agentic AI workflows that combine retrieval, tools, and guardrails; we evaluate trade-offs that blend performance, cost, and safety; and we partner with empowered product teams to ship the smallest valuable slice, learn, and iterate.

Measure what matters. If AI is working, DORA metrics should improve: higher deployment frequency, shorter lead time for changes, stable change failure rate, and faster MTTR. Pair that with outcomes vs output OKRs to avoid gaming the system—shaving seconds off a build is meaningless if it doesn’t move activation, retention, or revenue. A unified analytics platform can help connect engineering signals to business impact.

Risk is real—and manageable. AI risk management and data governance are now core competencies, not afterthoughts. Protect IP with robust access controls, context window management, and red-teaming. In production, instrument threat detection and response to catch prompt injection, data leakage, and model drift. Treat this like any other reliability discipline alongside SRE.

If parts of coding get automated, where can great engineers thrive? Several high-impact paths are emerging: platform engineering for LLMs (tooling, evals, observability), SRE for AI-infused systems, developer evangelism and education, product management for AI-native experiences, security engineering focused on model and data threats, and forward deployed engineers who pair with customers to solve messy, real-world problems.

How to upskill fast: build an AI product toolbox and ship small. Prototype gen ai features end-to-end—retrieval, function calling, human-in-the-loop QA—and connect them to your CRM integration or support stack. Use A/B testing with a clear minimum detectable effect (MDE) to validate impact. Leverage CustomGPT workflows for internal enablement and in-app guides or product tours to onboard users safely.

Here’s a pragmatic 90-day plan. Week 0–2: audit your top 10 engineering tasks by time spent; identify 3 that are ripe for AI augmentation. Week 3–6: pilot inside CI/CD with explicit guardrails; track DORA metrics and developer sentiment. Week 7–10: productionize the wins; document runbooks; add incident management paths. Week 11–12: share learnings with product trios, refine your value proposition, and set next-quarter OKRs.

AI won’t replace software engineers; engineers who master AI will outpace those who don’t. If we embrace the shift—toward systems thinking, responsible governance, and customer outcomes—we’ll build better products faster and open new, rewarding career paths. The opportunity is here and compounding.

Inspired by this post on Product School.

November 12, 2025
5 Costly UX Research Pitfalls I See Often—and How AI + Qual Insights Prevent Them

In product reviews and roadmap debates at HighLevel, I come back to a simple truth: great products start with great user research—but even seasoned teams fall into the same traps. After leading product discovery across empowered product teams and product trios, I’ve learned that a few avoidable mistakes consistently derail speed, quality, and outcomes.

Learn how to avoid the top five UX research pitfalls. Discover how AI and qualitative insights can help teams uncover the why behind user behavior.

The “why” behind user behavior is where durable growth lives. When we pair qualitative insights with analytics and a clear AI Strategy, we don’t just validate a solution—we de-risk the roadmap, improve user activation, and increase retention. Here are the five pitfalls I watch for and how I coach teams to avoid them.

Pitfall 1: Treating opinions as insights. Early in my career, I mistook strong stakeholder opinions for customer truth. Now I insist on a clear research question, a decision we will make with the evidence, and a hypothesis we’re trying to falsify. A/B testing is great for measuring impact when you’ve defined minimum detectable effect (MDE), but discovery research demands explicit learning goals and unbiased inputs.

How to avoid it: Write the decision statement first (“We will proceed with X if we learn Y”), then design the research. Keep a visible decision log so insights connect directly to product roadmapping and sprint planning, not to the loudest opinion in the room.

Pitfall 2: Leading questions and flawed methods. I still see interview guides that telegraph the desired answer. This corrupts the signal. Instead, I push teams to pilot guides with a product trio, remove solution language, and focus on behaviors. We complement interviews with in-app guides, targeted surveys, and session reviews using tools like Pendo and Intercom to capture moments of friction in-context.

How to avoid it: Ask neutral, behavior-first questions (“Tell me about the last time you…”) and validate with artifacts (screenshots, workflows). Pilot every guide with a colleague, then refine for clarity and neutrality.

Pitfall 3: Over-indexing on quantitative data and ignoring the why. Amplitude analytics and retention analysis tell me what happened; they rarely tell me why it happened. When teams chase dashboards without pairing them with qualitative interviews, we optimize for surface-level metrics and miss underlying jobs, anxieties, and unmet needs.

How to avoid it: Pair funnels and cohorts with a short round of qualitative interviews. Use Generative AI to summarize transcripts, cluster themes, and highlight contradictions, then validate themes against Amplitude analytics and CRM integration data. The synthesis is where insight emerges.

Pitfall 4: Recruiting bias—talking only to superfans or the most vocal detractors. If we only hear from power users, we build for edge cases; if we only hear complaints, we over-index on blockers. The result is a lopsided roadmap that misses mainstream value.

How to avoid it: Recruit across segments—new users, churned users, evaluators who never converted, and adjacent personas. Balance the sample and document who you didn’t talk to. For sensitive segments, lean on privacy-by-design practices and data governance so participants feel safe sharing candid feedback.

Pitfall 5: Weak synthesis and no path to action. Research often ends with a beautiful report that gathers dust. Insights must translate into choices: what we will do, what we will not do, and what we must learn next. Without this, research slows delivery without improving outcomes.

How to avoid it: Convert findings into atomic insights with evidence, confidence, and impact. Tie each insight to outcomes vs output OKRs, then schedule a decision review with the product trio. If you can’t articulate the decision, you haven’t finished the research.

How I use AI without losing the plot: I rely on LLMs for product managers to speed the busywork, not to replace judgment. Gen AI helps me transcribe, tag, and cluster themes; extract Jobs to Be Done; detect hesitation and sentiment; and draft UX writing variants for follow-up surveys. With a ChatGPT connector or similar tools, I can map qualitative themes to Amplitude analytics events and Pendo paths, revealing the narrative behind the numbers.

Guardrails matter: I apply AI risk management and privacy-by-design principles—no sensitive data in prompts, clear consent, and human-in-the-loop validation. AI is a force multiplier when the prompts are grounded in a solid research plan and the outputs feed a real decision.

A quick checklist I share with teams: define the decision and hypothesis; recruit a balanced sample; use neutral, behavior-first questions; triangulate quant with qual; synthesize into atomic insights; and link every insight to a concrete action or OKR. Do this, and you compress time-to-learning without sacrificing rigor.

When we respect the craft of research and thoughtfully apply AI, we consistently uncover the why behind user behavior—and build products that users adopt, love, and keep. That’s the fastest path to product-led growth and durable differentiation.

Inspired by this post on Amplitude – Perspectives.

November 11, 2025
Stop Falling for Hollywood Demos: The Unfiltered Truth of Live AI Voice for Support

I’ve sat through countless AI demos, and I’ve learned there are really two kinds: the “Hollywood demo,” which is polished to perfection, and the “real-world demo,” which shows the product raw—imperfections and all. The former dazzles, but the latter is where you discover what’s actually ready for prime time.

Hollywood demos look great, but sometimes need a closer look to make sure what you see is what you’ll get. When I’m evaluating an AI Agent for customer service, I always look past the polish. I’m assessing how well it will handle real-world scenarios—the messy, complex conversations your team deals with every day. That’s especially true on voice, the toughest channel to get right.

Voice is one of the toughest tests of any AI system. It’s not just “chat with speech.” An AI Agent needs to be able to listen, respond, and adapt in real time. Timing, tone, and turn-taking are all part of the product, they shape the experience as much as accuracy or reasoning.

An edited video might sound seamless, but it can’t show how a system behaves in a real support environment—like when a conversation takes an unexpected turn or when it pauses briefly to reason or retrieve data. Those small moments—latency, clarifications, interruptions—are when you see what the AI Agent is really capable of. A real-world demo lets you see and hear how the system actually behaves under real conditions, not in a controlled environment that’s been smoothed out with editing.

That’s why the live Fin Voice demo at Pioneer stood out. The team called Fin live on stage to show the real thing (with real latency and interruptions) so people could understand the product they’d be deploying to their own customers. As a product leader, I appreciate that level of transparency because it mirrors how customers will experience the system in production.

When Paul Adams, Chief Product Officer, demoed Fin Voice at Pioneer, the goal was to show the product exactly as customers experience it. In 90 seconds, Fin verified his identity, retrieved account data, managed an interruption, offered options, completed the workflow, and sent a follow-up email. That’s the kind of end-to-end outcome I look for—fast verification, accurate retrieval, natural pacing, and a closed loop.

Latency. You could hear brief pauses while Fin fetched subscription details and checked backend systems. That wasn’t lag—it was work happening in real time. In voice AI, thoughtful latency that signals reasoning is far better than synthetic speed that collapses under real load.

Natural conversation flow. Fin detected when Paul finished speaking, handled interruptions gracefully, and replied in short, human-like turns. That turn-taking behavior is essential for trust and comprehension in voice customer support.

Awareness and tone. Subtle changes in pacing when Paul laughed or hesitated showed sensitivity to context. Tone control is not a “nice to have” in voice—it’s a core UX capability.

Unscripted conversation design. No rigid IVR menus or fixed paths. Paul spoke naturally, and Fin adapted to resolve his query. That adaptability is what differentiates a true AI Agent from a glorified decision tree.

Those details are the real test. A voice AI Agent that performs well in a live demo is one that will perform well for you and your customers too.

Voice has been one of the most demanding, and rewarding, areas of development for Fin. Since launch, we’ve been expanding what it can do so support leaders can customize how Fin sounds, behaves, and aligns with their brand.

Voice and tone customization: Choose from multiple natural voices, set greetings, and fine-tune how Fin communicates with customers.

Escalation and conversational guidance: Teach Fin to use your terminology, ask clarifying follow-ups, and escalate when needed.

Deployment controls: Manage rollouts, test safely in internal environments, and fine-tune before going live.

Flexible integrations: Connect to any telephony system via call forwarding, and link Fin Voice to backend systems or APIs to take action.

Multilingual capability: Fin Voice now supports 28 languages natively.

Alongside these features, we’ve made big improvements to Fin’s answer quality—the foundation of a great voice experience. When people call, they’re looking for accurate, immediate answers they can trust.

So we’ve focused on three key areas: low latency, which is down roughly 30–40% since launch; clarification flow, so Fin asks smart follow-up questions to reduce back and forth and improve resolution rates; and voice-specific answer structure, so Fin delivers information in shorter sentences with pacing designed for listening.

Together, these improvements mean customers get the highest-quality answers as quickly as possible, resulting in more resolutions and better experiences.

Running a live demo always carries risk because things can go wrong. But that’s also why it matters—because that’s how customers experience it too. Support leaders stake their reputation on the systems they choose, so the only way to understand what you’re putting in front of your customers is to see it under real conditions.

When you see Fin in a demo, you’re seeing the same system that runs in production. Real-world demos take more effort and don’t always go perfectly, but they show what’s real—and that’s exactly what you need to evaluate before you deploy voice AI at scale.

Inspired by this post on The Intercom Blog.

November 11, 2025
Global Invoicing Nightmares: Hard-Won Product Lessons on EU Tax, Compliance, and Customer Value

I hit play on Global Invoicing – All Things Product Podcast with Teresa Torres & Petra Wille and felt an immediate jolt of recognition. We’ve all launched a feature that looked solid—until a small, overlooked detail broke everything. Their stories about global invoicing and taxes echoed challenges I’ve faced leading product for international customers: if you don’t design for the last mile of compliance, you can accidentally block the very "moment of value creation" your product promises.

Listen to this episode on: Spotify | Apple Podcasts

The conversation starts as a candid rant about EU tax compliance and quickly becomes a precise product management lesson: when we fail to map the entire path to customer value—down to the tiniest regulatory requirement—we can ship something “done” that still doesn’t work in the real world. That gap between intention and outcome is where good product teams live or die.

In my experience, the nightmare of global invoicing for small online businesses is very real. Even big platforms (like Squarespace and Teachable) miss the mark on EU tax compliance, and when they do, customers feel it immediately. It’s the kind of edge case that doesn’t show up in a demo but absolutely shows up in revenue. Or as Teresa put it, “It’s not a little detail when your client won’t pay the invoice.” — Teresa Torres

I appreciated how the episode digs into the difference between passing a regulatory checklist and actually meeting customer needs. Put plainly: the product isn’t “done” when the ticket moves to Done; it’s done when the customer completes the job—receives an acceptable invoice, pays successfully, and can reconcile it without friction. That’s why I lean hard on story mapping for regulatory work; it exposes the invisible steps where value creation can silently fail.

Here’s how the episode resonates with my own playbook: the nightmare of global invoicing for small online businesses is a systems problem; why even big platforms (like Squarespace and Teachable) miss the mark on EU tax compliance is a prioritization and discovery problem; how Petra and Teresa navigated invoicing across borders with Ableify and LearnWorlds highlights pragmatic tool choices and trade-offs; the key difference between meeting regulations and meeting customer needs is an outcomes-over-output mindset; what product teams can learn from regulatory edge cases is how to find the seams where markets, laws, and workflows collide; how missing a single detail can block the "moment of value creation" is a reminder that value is defined by customers; and why story mapping is critical for finding gaps between "we shipped it" and "customers got value" is the method that connects all of the above.

Practically, that means I treat regulatory features like any other high-stakes product surface: do real product discovery with affected users; co-design the happy path and the ugly edge cases; write acceptance criteria that include jurisdictional and document-level specifics (e.g., VAT numbers, invoice formats, timing rules); align with finance and legal early; and instrument the journey from invoice issued to invoice paid so we can see where real customers get stuck. This is outcomes vs output OKRs in action, and it’s one of the fastest ways to earn trust with stakeholders.

Key takeaways worth bookmarking: Customers define value, not your compliance checklist. Regulatory work still requires discovery—you can’t skip understanding user needs. The path to value doesn’t end when your feature works; it ends when your customer succeeds. “Sweating the details” isn’t micromanagement—it’s good product management.

Memorable quotes to bring back to your team: “If you don’t sweat the details, people choose other platforms.” — Petra Wille. “It’s not a little detail when your client won’t pay the invoice.” — Teresa Torres.

Follow Teresa Torres: https://ProductTalk.org | Follow Petra Wille: https://Petra-Wille.com

Mentioned in the episode: Squarespace | Stripe | Product at Heart | Teachable | LearnWorlds | Ablefy | Become a Better Product Leader: A 52-Week Transformation Journey | Product Talk Academy

Have thoughts on this episode? Leave a comment below.

Full transcripts are only available for paid subscribers.

Inspired by this post on Product Talk.

November 11, 2025
Cut Time to Value, Boost Retention: My Proven Playbook for Activation, Growth, and Loyalty

Time to value is the most reliable early indicator of long-term user retention I know. When customers experience meaningful product impact fast, they stick around, expand, advocate, and cost less to support. Over the years leading product teams, I’ve learned that speed-to-impact isn’t a nice-to-have—it’s the engine behind sustainable product-led growth and efficient go-to-market.

Accelerate retention by reducing time to value. Learn how faster product impact drives growth, reduces costs, and keeps users engaged in the long term.

Practically, I define time to value as the duration from first touch (or first login) to the moment a user achieves their “aha” outcome—something tangibly useful aligned to their job-to-be-done. The shorter that journey, the higher the likelihood of user activation, trial conversion, and durable engagement. This is why I obsess over onboarding, in-app guides, product tours, and the clarity of our value proposition.

My first move is to map the Minimum Path to Value (MPV): the smallest set of actions needed to deliver a real result for a new user. I strip away everything non-essential in that path—fields, clicks, choices, and jargon. Opinionated defaults, smart templates, sample data, and single-player workflows let customers succeed in minutes, not days. The goal is to reduce cognitive load while making the next best action unmistakably clear.

Instrumentation turns TTV from a hunch into a system. I track activation events, cohort retention, and conversion using platforms like Amplitude analytics and Pendo, with timely nudges through Intercom when users stall. I look at the distribution of TTV (not just the average), correlate it with retention analysis, and set explicit targets such as “new users reach first value within 10 minutes.” Those targets become team-level outcomes—not outputs—and we review them weekly.

Experimentation is how we iterate toward the fastest path to value. I rely on A/B testing to compare onboarding flows, progressive profiling to delay non-critical inputs, and opinionated setup wizards to remove guesswork. Auto-generated example projects, pre-configured integrations, and guided checklists accelerate user activation without sacrificing flexibility for advanced users.

Content and guidance matter as much as UX. Tooltips, contextual in-app guides, and short product tours should be timely, skippable, and laser-focused on the outcome, not the feature. I pair these with a concise knowledge base and short explainer videos that reinforce the same value narrative a user sees inside the product.

Cross-functional alignment is essential. Product, marketing, sales, and customer success must rally around the same activation metric and TTV target. That alignment ensures our trial messaging, onboarding emails, and CS playbooks don’t compete—they compound. When everyone points to the same first-value moment, friction drops and adoption rises.

Pricing and packaging can also accelerate time to value. Free trials should be long enough for users to credibly reach first value; usage-based gates should never block the MPV. I prefer to unlock everything needed to hit the “aha” moment, then meter after the value is viscerally felt—this respects the user’s time and reinforces trust.

There’s a cost story, too. Faster time to value reduces tickets, shortens onboarding cycles, and lowers cost-to-serve. It also clarifies product discovery: when we see where users stall, we don’t guess at roadmap priorities—we let the data guide our next bet.

In my experience at HighLevel, I’ve repeatedly seen activation rates jump when we cut time to value from days to minutes. The specific tactics vary by product, but the pattern holds: when the first outcome is undeniable and fast, retention follows—and so does efficient growth.

If you’re looking for a starting point, try this: define one activation event that clearly signals value, instrument it end-to-end, design a Minimum Path to Value that gets new users there in under 10 minutes, and run weekly experiments until you consistently hit the target. Do that, and you won’t just improve onboarding—you’ll build a product that earns loyalty from the very first session.

Inspired by this post on Amplitude – Best Practices.

November 9, 2025
Prototypes vs Products: How I De-risk Ideas Fast and Ship Reliable Value at Scale

Note: This is part of the product creator series of articles, based on the overview article, The Era of the Product Creator. This series is for anyone who wants to create a successful product—whether or not you’ve had formal training or experience in product management, product design, or engineering. Over the years, I’ve watched smart teams stumble because they treated a prototype like a product. The distinction is simple but vital: prototypes exist to learn; products exist to earn trust by delivering value reliably at scale. When we blur that line, we ship avoidable risk to customers and slow ourselves down later with rework. When I build a prototype, I’m testing assumptions as quickly and cheaply as possible. It might be a clickable Figma mock, a Wizard‑of‑Oz demo, or a quick script stitching together a ChatGPT connector with a CustomGPT workflow. It’s intentionally disposable. I expect missing edge cases, fake data, hand‑waving on latency, and limited attention to security or privacy. The only goal is to answer the riskiest questions fast. A product is a promise. It’s hardened for reliability, performance, security, and privacy‑by‑design. It’s observable with real analytics, supports CI/CD and rollback, meets accessibility guidelines, and can be maintained by empowered product teams. It has clear SLAs, incident management runbooks, and instrumentation that lets me track outcomes vs output OKRs and DORA metrics. Keeping prototypes and products separate makes us faster and safer. Prototypes accelerate discovery; products operationalize value. If I catch myself “polishing” a prototype, I pause and either discard it or define the path to production with the right engineering rigor, data governance, and stakeholder management. Here’s how I decide. In prototype mode, I timebox learning to days, not weeks, and focus on a single risky assumption—value, usability, or feasibility. I validate through qualitative research and usability tests, not vanity metrics. To graduate to product work, I require a crisp problem statement, evidence of problem‑solution fit, a technical plan for scale and observability, a privacy and threat modeling review, and a measurement plan (including minimum detectable effect) for upcoming A/B testing. AI adds new wrinkles. For gen AI and agentic AI, I evaluate model behavior offline before exposing anything to customers. That includes prompt design, context window management, guardrails to minimize hallucinations, and clear fallback strategies. I define red‑team scenarios, logging for auditability, and policies for data retention and encryption as part of AI risk management. A recent example: we prototyped an agent workflow in a day that felt magical in demos. We resisted the urge to ship. Instead, we added authentication, rate limiting, PII redaction, human‑in‑the‑loop review, observability, and in‑app guides and product tours for onboarding. Only then did we move to a limited release with a well‑defined go‑to‑market strategy and support readiness. One more trap to avoid: calling a prototype an MVP. An MVP is still a product—minimal in scope but complete enough to deliver value, gather trustworthy data, and support customers. If you wouldn’t put your name on it or support it in production, it’s a prototype, not an MVP. If you’re a product creator, align your product trios around this discipline. Use prototypes to learn quickly in discovery, and use products to deliver outcomes in delivery. That mindset protects customer trust, speeds iteration, and moves you toward product‑market fit with far less waste.

Inspired by this post on SVPG.

November 7, 2025
How Incident.io’s AI SRE Diagnoses, Hypothesizes, and Fixes Outages in Slack at Record Speed

When your site goes down, every second counts. I’ve lived that reality across multiple product lines, and the difference between a five-minute blip and a two-hour outage is felt by customers, engineers, and the business. That’s why I’ve been closely following how Incident.io has evolved from coordination during chaos to intelligent, proactive response.

Now, they’re building something new: an AI SRE that can actually help diagnose and respond to incidents. As someone who thinks deeply about reliability, velocity, and customer trust, that promise hits the intersection of AI Strategy, product management leadership, and operational excellence.

I recently spent time with Lawrence Jones, Founding Engineer at Incident.io and Ed Dean Product Lead for AI at Incident.io, digging into how their team is teaching AI to think like a site reliability engineer. They shared how they went from simple prototypes that summarized incidents to a multi-agent system that forms hypotheses, tests them, and even drafts fixes—all from within Slack.

Here’s what stood out to me first: AI’s biggest impact comes from compressing time—identifying causes minutes instead of hours. In practice, that means fewer cycles lost to paging the wrong on-call, clearer paths to root cause, and faster recovery—without cutting humans out of the decision loop.

Equally important is deciding where automation belongs. The team’s approach aligns with how I evaluate high-risk workflows: Identify which parts of debugging can safely be automated. Combine retrieval, tagging, and re-ranking to find relevant context fast. Use post-incident “time travel” evals to measure how well their AI performed. Balance human trust and AI confidence inside high-stakes workflows. The human remains accountable; the AI accelerates context, options, and execution.

On the technical side, the retrieval choices were refreshingly pragmatic. Retrieval-augmented reasoning still benefits from simplicity: deterministic tagging and re-ranking often beat complex vector setups. I’ve seen the same in production: start with crisp, deterministic signals, then layer embeddings where they truly add value. This keeps systems debuggable and stable as you scale.

The interface choices matter just as much as the models. “Slack as the interface for human-AI collaboration” puts the agent where incidents already live, reducing friction and increasing adoption. Under the hood, they’ve been pragmatic with “PGVector and Postgres for retrieval experiments”, using “RAG (Retrieval-Augmented Generation)” and “Multi-agent orchestration” to chain context gathering, hypothesis formation, and action proposals. The north star is compelling: “AI as your company’s immune system”.

What impressed me operationally was the rigor around evaluation. Post-incident “time travel” evals let teams score AI accuracy after they know what really happened. That’s the standard we should all adopt: test the agent against reality, not just synthetic prompts, and feed those learnings back into prompts, tools, and guardrails.

Trust is the currency in incidents, so the product surface must reflect uncertainty with care. Building trust in AI isn’t just about precision—it’s about showing reasoning and uncertainty in ways humans understand. In other words, show the chain of thought as a structured artifact (signals considered, hypotheses rejected, evidence gathered), expose confidence bands, and always make it easy for humans to override or guide.

From a workflow standpoint, the investigation loop mirrors seasoned SRE practice: fast scoping, parallel checks and data sources, building hypotheses and refining findings, then proposing remediations paired with the context that justifies them. Human-agent collaboration here is not a handoff—it’s a tight copilot loop where the agent gathers, tests, and drafts, and the human confirms, prioritizes, and executes.

For platform and security leaders, this approach blends speed with safety. Clear permissions, auditable actions, blast-radius constraints, and CI/CD integration keep the AI inside defined guardrails while still delivering material acceleration. The payoff is higher deployment frequency without compromising reliability—because detection, triage, and rollback become faster and more repeatable.

My takeaway as a product leader: this is a blueprint for agentic AI in mission-critical workflows. Start in the tools users live in (Slack), nail retrieval with deterministic foundations, model the expert’s playbook (not just their summaries), and make evaluation a first-class part of the product. Do that well, and the AI goes from assistant to teammate—conservative when it should be, bold when the evidence supports it, and always legible to the humans in the loop.

The momentum around Incident.io’s AI SRE suggests where we’re headed next: deeper integrations, broader coverage across service catalogs, and richer automations that remain transparent and controllable. For teams investing in reliability, this is the moment to operationalize agentic AI—measured, auditable, and designed for trust—so you can move faster when it matters most.

Inspired by this post on Product Talk.

November 6, 2025