Inspired by this post on Pendo – Perspectives.
Tag: product strategy
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I Built a ‘Pendo Wrapped’ in 10 Minutes with Pendo MCP to Boost Adoption and Delight Users
I set out to create a lightweight, high-impact “Pendo Wrapped” experience for our users—and I did it in under 10 minutes with Pendo MCP. As a VP of Product Management, I’m constantly looking for fast, pragmatic ways to turn product insights into moments that drive engagement. This experiment was about transforming raw analytics into a concise, celebratory year‑in‑review that motivates customers to explore more value. When I say “Pendo Wrapped,” I mean a simple, narrative-style summary of usage highlights: what got adopted, which moments mattered, and where value showed up most clearly. Framed well, that story reinforces product‑led growth by reminding users why they chose us, nudging them toward the next best action, and strengthening activation and retention without heavy development work. My approach was straightforward: define a clear objective (celebrate milestones and prompt the next step), choose a focused set of metrics (adoption, engagement, and activation), and target relevant segments. Then I layered the narrative on top of existing analytics using in‑app guides and product tours to deliver the experience where it matters most—inside the product. The reason it took minutes, not hours, is that Pendo MCP let me work with what we already had—segments, saved reports, and proven guide templates—so I could spend time on the story, not the scaffolding. No code, minimal configuration, and a crisp call to action made it feel polished without being heavy. Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement. If you want to replicate this quickly, start by selecting one user segment and three metrics that matter to them, write a two‑sentence narrative that connects those metrics to outcomes, and ship a short in‑app guide with a single, purposeful CTA. That’s enough to deliver a personalized year‑in‑review feel and spark immediate exploration—no new infrastructure required. What surprised me most was how a small, story‑driven touch created outsized alignment across customers and internal teams. It turned analytics into advocacy, reminded our users of the value they’re already getting, and opened the door to deeper adoption. If you’re pursuing product‑led growth, a fast “Pendo Wrapped” is one of the highest‑leverage experiments you can run this week.
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From PDFs to Proposals: How Tendos AI’s Agent Swarm Automates Construction Quotes Fast
Anyone who has lived inside construction tendering knows the grind. "When a construction company receives a bid request, someone has to open that email, parse the attached PDF (sometimes 1,800 pages describing an entire building), figure out which products are relevant, look up pricing, and draft a quote—all before the deadline. It's tedious, error-prone, and surprisingly manual." That painful reality is exactly why this conversation about Tendos AI caught my attention—and why it matters for product leaders building agentic AI in complex, document-heavy workflows.
I listened as Daniel Kappler and Matthias Hilscher from Tendos AI walked through how they’re automating the tendering workflow for manufacturers in the construction industry. What began as a narrow prototype—matching radiator requests to product catalogs—has matured into a full agentic system that does the heavy lifting from email categorization to offer generation. The end result: a scalable AI workflow that tackles messy inputs, orchestrates specialized agents, and produces quotes that are ready for human review—or even straight-through processing.
What impressed me most was the rigor. They validated the opportunity with a design partner, spent a week on-site observing real workflows, and then engineered a multi-agent architecture where specialized agents collaborate, including a "review agent" that checks work before anything reaches a human. They evaluate each agent independently (not just the whole chain), built custom observability when off-the-shelf tooling fell short, and use human-in-the-loop feedback to push toward a self-learning system.
From a product management perspective, this is agentic AI done right. It blends continuous discovery with eval-driven development, thoughtful UX decisions, and pragmatic guardrails. Evaluating agents individually makes debugging tractable and change detection transparent; a dedicated "review agent" mirrors code review to reduce error propagation; and custom tracing plus Agent Analytics provide the observability needed to operate AI workflows reliably at scale.
My key takeaway: "Start narrow to prove value: Tendos AI began with just radiators for one design partner before expanding to all building products"—a classic wedge strategy that accelerates learning while building credibility.
Another takeaway I’ll adopt in future roadmaps: "Own the interface: building a web application (vs. integrating into legacy systems) gave them control over UX and the ability to iterate toward full automation." Controlling the surface area let them move faster than a purely backend integration ever could.
On measurement and reliability, I loved this: "Evaluate each agent, not just the chain: per-agent evals make debugging tractable and show exactly where performance changed." That’s true eval-driven development—aligning metrics to decision points rather than only outcomes.
Quality gates matter in automation, and they nailed it: "Use review agents: a separate agent that checks work (like code review) catches errors before they reach humans." It’s a simple pattern with outsized ROI.
Finally, the product-market signal is unmistakable: "Let customers pull you: customers asked Tendos to replace their CPQ software—strong signals of product-market fit." When buyers invite you to displace existing systems, you’re past validation and into expansion.
If you’re exploring agentic AI for enterprise workflows, the themes here are gold: the tendering chain in construction is ripe for automation; domain expertise accelerates opportunity discovery; robust entity extraction across PDFs ranging from 1 to 1,800+ pages is non-negotiable; planning patterns for creating and updating task plans matter; agents must reason about product fit against customer requirements; custom tracing and observability unlock debugging for complex agent chains; and human feedback loops pave the path to self-learning systems.
Guests: Daniel Kappler — CPO (Product & Design), Tendos AI; Matthias Hilscher — CTO (Engineering), Tendos AI.
Want to dive deeper? Listen to this episode on: Spotify | Apple Podcasts.
Explore the team and product: Tendos AI.
For builders of agentic AI, here’s my playbook distilled from this story: start narrow to earn trust and accuracy; own the interface to speed iteration; use per-agent evaluations to localize issues; add a "review agent" as a quality gate; invest early in tracing, observability, and Agent Analytics; keep humans in the loop until your metrics justify autonomy; and let strong pull signals guide your roadmap. That’s how you turn complex emails and massive PDFs into precise, production-grade quotes—consistently.
Inspired by this post on Product Talk.
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AI Ethics That Win Trust: The Product Manager’s Playbook for Safe, Scalable Innovation
I’ve learned that the fastest way to lose customers with AI is to ship something powerful but unpredictable. The fastest way to earn their loyalty is to ship something powerful and trustworthy. That’s the job.
AI ethics in product management isn’t about theory anymore. It’s the line between trusted products and unpredictable ones. Here’s what PMs need to know.
When I frame AI ethics for my team, I translate principles into practices that protect customers and accelerate velocity. We bake trust into product strategy, delivery, and operations—so ethics is not a separate checklist, but a core capability that compounds over time.
First, I anchor the roadmap on explicit outcomes and guardrails. We set success metrics alongside ethical constraints, tying them to outcomes vs output OKRs, so teams know not only what to achieve but what to avoid. If a feature can’t meet our trust thresholds, it doesn’t ship—no matter how impressive the demo.
Data is where trust starts. We enforce data governance from day one: clear data lineage, collection minimization, role-based access, and privacy-by-design defaults. We document lawful bases for processing, consent flows, and retention policies, then automate checks so they run with every change—not just at launch.
On the model side, we use eval-driven development to turn subjective “looks good” into measurable quality. We design evaluations for safety, bias, robustness, and performance; we red-team prompts; and we test failure modes in realistic conditions. For LLMs, we lean on a retrieval-first pipeline to ground responses in authoritative data, and we apply context window management and prompt engineering patterns to reduce hallucinations.
In the product experience, we make ethical choices visible. That means clear disclosures when AI is in the loop, user controls to review and correct outputs, and transparent UX writing that avoids overclaiming. In-app guides and thoughtful tooltip design help users understand capabilities and limits without friction.
Shipping safely requires operational discipline. We build kill switches, human-in-the-loop overrides for high-risk actions, and incident playbooks that pair incident management with threat detection and response. SRE partnerships ensure observability covers both model behavior and customer impact, with rollback paths ready when drift or regressions appear.
Governance is a team sport. I maintain an AI risk register, review it with security, legal, and product trios, and brief leadership on residual risks and mitigations. Regulatory compliance isn’t a final hurdle; it’s a design input that shapes technical choices long before code reaches production.
Build vs buy decisions carry ethical implications too. Vendor due diligence covers model provenance, data handling, eval results, and incident history—not just feature checklists. Contracts codify SLAs, audit rights, and deletion commitments so our obligations to customers flow down the stack.
Finally, we earn trust in public. We publish model facts, change logs, and limitations in a customer-facing trust center, and we invite feedback loops that turn real-world usage into better safeguards. Stakeholder management matters here: being candid about trade-offs often increases confidence more than chasing perfection.
This is how I keep teams fast without being reckless: ethics as a product capability, not a poster. Build with intention, measure what matters, and make it easy for customers to understand, control, and benefit from your AI. That’s how we ship innovation that stays trusted—at scale.
Inspired by this post on Product School.
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11 Product Management Shifts Redefining 2026: Actionable Signals from Top Leaders
2026 is closer than it feels, and the signals are already clear. I’ve been synthesizing what I’m seeing across empowered product teams, boards, and cross-functional partners into a practical view of what matters next. A sharp look at product management trends for 2026. Not guesses, but signals from top product leaders shaping how PMs will actually work next.
In this analysis, I distill eleven shifts that are changing the craft—from outcomes vs output OKRs and continuous discovery to stronger product strategy and tighter product roadmapping and sprint planning. The throughline is simple: prioritize customer value, ship with focus, and measure what moves the business. These aren’t headline trends; they’re working patterns I’m seeing across high-performing organizations.
AI is no longer a side project—it’s part of the product manager’s core toolkit. Agentic AI, LLMs for product managers, and trustworthy AI workflows are accelerating discovery, sharpening problem framing, and enabling faster iteration. The best teams pair this with disciplined evaluation and experimentation, so insight compounds without sacrificing safety, privacy, or product quality.
Execution is getting crisper through product trios and stronger stakeholder management. When design, product, and engineering co-own discovery and delivery, teams reduce handoffs and increase clarity. That alignment translates into better prioritization, fewer context-switches, and a roadmap that reflects real trade-offs—not wish lists.
On growth, product-led growth remains a durable engine when it’s anchored in a compelling value proposition and instrumented end-to-end. Clear activation moments, in-app guides, and thoughtful product tours outperform brute-force acquisition. When we connect these motions back to product strategy and the roadmap, we create a repeatable loop that compounds adoption and retention.
Governance and trust are now table stakes. Privacy-by-design, data governance, and a pragmatic approach to regulatory compliance protect both users and velocity. Teams that build these practices into their operating model move faster because they avoid late-stage rework and maintain stakeholder confidence.
If you’re leading a product org—or aspiring to—this is your field guide to 2026. I’ll unpack where these shifts are strongest, how to apply them in your context, and the pitfalls to avoid. The aim is to give you clear language, concrete practices, and a sharper edge as you shape what your team builds next.
Inspired by this post on Product School.
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The Modern Playbook for AI Agents: Build One‑Person Departments and Scale with Amplitude
I’ve spent the last few years turning AI from an intriguing demo into an operational advantage, and the clearest wins come when we treat agents as productized workflows—not toys. In practice, that means aligning agentic AI to a sharp product strategy, instrumenting everything, and scaling what works across the organization.
Learn how companies like Replit are consolidating workflows, creating one-person departments, and building systems for scale with Amplitude
When I talk about agentic AI, I’m focused on outcomes: fewer handoffs, faster cycle times, and measurable uplift in activation, retention, and NPS. The most successful rollouts start with a specific job-to-be-done, translate it into clear AI workflows, and then iterate with a tight feedback loop between data, design, and engineering.
My implementation playbook is simple and disciplined. First, choose a high-friction workflow and define success upfront. Second, make the build vs buy call on the foundation model, orchestration layer, and connectors. Third, establish AI risk management and safeguards early—before scale amplifies errors. Finally, run small, eval-driven releases and promote what performs.
Instrumentation is where the leverage compounds. With Amplitude analytics as a unified analytics platform, I design purposeful events (agent intent, tool calls, resolution state, human handoff), map funnels from user input to agent outcome, and cohort users by context to pinpoint lift. This gives me an honest read on where agents help, where they hinder, and what to tune next.
The “one-person departments” concept isn’t about doing more with less at all costs; it’s about assembling a tight loop of product management leadership, data, and automation so one operator can own a business outcome end-to-end. An agent handles the repeatable work, while the human focuses on judgment, edge cases, and continuous improvement that compounds.
As we scale, I look for platform scalability patterns: shared tools and policies, reusable prompt libraries, standardized evaluation suites, and consistent governance. That structure keeps agent performance predictable while preserving speed, and it aligns beautifully with product-led growth when agents are embedded directly in the product experience.
If you’re starting now, begin with a single, valuable workflow. Instrument it thoroughly with Amplitude analytics, make decisions from the data you see—not the demos you remember—and expand only after you’ve proven uplift. Iteration beats ambition here: agentic AI rewards teams who measure relentlessly and scale only what truly works.
Inspired by this post on Amplitude – Perspectives.
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Turn Every Support Ticket into Product Truth: My Playbook for Data-Driven CX Wins
Support tickets are the rawest signal of product truth. Leading product teams at HighLevel, I’ve learned that the fastest way to build what customers value is to transform frontline conversations into a repeatable, data-driven system for discovery, prioritization, and execution.
What if your support and product teams could unlock CX insights to turn every ticket into strategic product intelligence? Explore how.
Here’s the operating system I rely on. First, I connect our support stack (think Intercom and our CRM integration) into a unified analytics platform so every conversation, tag, and resolution is queryable. I don’t just count tickets—I segment them by product area, customer segment, lifecycle stage, and revenue impact to reveal patterns that roadmaps can act on.
Next, we standardize a shared taxonomy. Agents apply concise, high-signal labels (problem type, severity, intent), and we augment that with AI-driven auto-tagging to reduce noise and improve recall. The result is trustworthy “voice of the customer” data that product managers and support leaders can both stand behind.
Prioritization then becomes rigorous and fair. I weight themes by severity, frequency, ARR exposure, and time-to-value, and tie them directly to outcomes vs output OKRs. Amplitude analytics helps me quantify impact—what’s breaking activation, what’s dragging conversion, what drives retention analysis—so the backlog reflects business outcomes, not opinions.
Discovery is continuous by design. Product trios (PM, design, engineering) run weekly reviews of the highest-signal themes, recruit users straight from recent tickets, and prototype solutions quickly. We validate ideas with A/B testing when appropriate and ship targeted in-app guides to reduce confusion before it becomes a ticket.
Crucially, we close the loop. When we release a fix or improvement, we notify affected customers and the agents who flagged the issue. We track downstream effects—ticket deflection, CSAT, feature adoption, and time-to-resolution—so everyone sees how customer support ai strategy accelerates product-led growth.
This approach also builds culture. Empowered product teams treat support as a strategic partner, not a cost center. Agents become co-creators of the roadmap, and PMs gain a steady stream of product discovery opportunities grounded in real user outcomes.
If you’re getting started, a simple 30-60-90 can help: in 30 days, unify the data and agree on taxonomy; in 60, instrument dashboards and adopt a weekly insights ritual; in 90, align priorities to OKRs, launch targeted fixes, and measure business impact. That’s how tickets turn into product truth—and how CX insights drive compounding wins.
Inspired by this post on Amplitude – Perspectives.
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7 Proven Steps to Win Stakeholder Buy-In with Clarity, Data, and Lasting Trust
Buy-in isn’t a single meeting; it’s a designed journey. Over the years leading product strategy at HighLevel, I’ve learned that the fastest way to earn durable support is to reduce uncertainty, align on outcomes, and create visible momentum. Explore how to get buy-in from stakeholders with practical strategies, clear communication tips, and proven methods used by the best. Here’s the 7-step playbook my teams and I rely on to move from idea to aligned action.
Step 1 — Anchor on outcomes, not outputs. I start by writing a crisp problem statement, the target customer, and the measurable outcome tied to our North Star metric. I translate this into outcomes vs output OKRs so every stakeholder can see the difference between what we’ll ship and what we intend to change. This framing keeps discussions grounded in impact, not features.
Step 2 — Map stakeholders and incentives. Effective stakeholder management begins with a living map: economic buyers, executive sponsors, influencers, and operators. I capture each person’s goals, risks, and decision cadence. When I speak to Finance, I foreground cost and runway; with Sales, I emphasize pipeline and win rate; for Customer Success, I speak to retention and NPS. Meeting stakeholders where they are builds trust quickly.
Step 3 — Co-create early with the product trio. I pull the product trios (PM, Design, Engineering) into continuous discovery with GTM partners to validate assumptions and de-risk the solution. This is where empowered product teams shine—rapid discovery sprints, early prototypes, and clear learning objectives. Co-creating exposes blind spots early and transforms critics into champions.
Step 4 — Socialize a narrative, not a deck. Before any formal review, I circulate a short narrative memo that ties our product strategy to a clear value proposition, competitive differentiation, and go-to-market strategy. I include options and trade-offs so stakeholders feel invited to shape the path, not just stamp approval. Pre-wiring conversations ensure that the “meeting” is simply the last 10% of the decision.
Step 5 — Back the story with data and a viable plan. I combine retention analysis, funnel metrics, and customer evidence to demonstrate opportunity size and risk reduction. Then I outline a phased approach with product roadmapping and sprint planning, milestones, and success metrics. I highlight the smallest viable bet that proves value fast, along with contingency paths if we learn something unexpected.
Step 6 — Design the decision. I define the decision we need, by whom, and by when. The decision doc includes the problem, options, risks, mitigations, and the explicit ask. I schedule 1:1s to address concerns, then run a focused review with clear roles and time-boxed discussion. Clarity about the decision—and the criteria—prevents drift and protects timelines.
Step 7 — Sustain momentum post-approval. After the green light, I convert the plan into execution cadences: weekly demos, transparent dashboards, and QBRs vs OKRs check-ins to reinforce outcomes. We celebrate learning milestones, not just launches, and keep stakeholders informed with concise updates that tie progress to the original outcomes and value proposition. Momentum is the best antidote to second-guessing.
Clear communication and a repeatable process turn buy-in from a hurdle into a habit. When stakeholders see a compelling narrative, credible evidence, and a path to value, they don’t just approve—they advocate. Follow these seven steps and you’ll build alignment faster, ship smarter, and strengthen trust across the organization.
Inspired by this post on Product School.
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Unlock Travel & Hospitality Growth: Product Benchmarks and Metrics Top Teams Rely On
I lead product teams building travel and hospitality experiences, and one lesson keeps repeating: companies that measure what matters move faster. Benchmarks turn gut feel into grounded product strategy, making it clear where activation, conversion, and retention are underperforming—and where we can unlock outsized growth.
Discover exclusive data and strategies from our Product Benchmark Report. Compare the travel and hospitality industry’s performance across key product metrics.
When I evaluate a product line, I start with a simple model: attract, convert, delight, and retain. For travel and hospitality specifically, I focus on search-to-book conversion, onboarding completion, first-booking activation rate, time-to-book, average booking value, cancellation rate, support contact rate, DAU/MAU stickiness, repeat booking rate, and long-term retention. These key product metrics reveal friction in discovery and checkout flows, surface pricing and inventory gaps, and quantify loyalty.
From there, I assemble a test-and-learn plan. Using Amplitude analytics to instrument the funnel and Pendo for in-app guides and product tours, my teams design A/B testing with a clear minimum detectable effect (MDE), prioritize hypotheses, and execute rapid, weekly iterations. This is classic product-led growth: reduce cognitive load in onboarding, streamline search and filter UX, clarify policies before payment, and personalize reactivation nudges to improve user activation and retention analysis.
Benchmarks are only as trustworthy as the underlying data. I insist on strong data governance, privacy-by-design practices, and clear event taxonomies so that insights remain reliable across quarters and across markets. That foundation keeps our decisions defensible with stakeholders and regulators while accelerating delivery.
Finally, we translate insights into action with crisp product roadmapping and sprint planning. Cross-functional product trios align OKRs to the biggest benchmark gaps, and we review progress in weekly performance rituals so every experiment ladders up to strategy. This cadence helps teams stay empowered and keeps leadership focused on outcomes, not output.
If you’re building in travel and hospitality, use these benchmarks as your starting line and your ongoing scorecard. Calibrate targets against peers, double down on what moves the needle, and let the data guide bold, customer-centered bets. When teams rally around meaningful metrics, momentum compounds.
Inspired by this post on Amplitude – Perspectives.
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Join My 2026 Continuous Discovery Habits Book Club: Build Weekly Discovery Routines That Stick
Continuous Discovery Habits turns five this year, and I’m celebrating by inviting you to read it with me. Over 135,000 people have bought the book. I’ve seen these habits transform outcomes, reduce rework, and sharpen product strategy in my teams and across the product community, but I also know it’s not easy to sustain the practice—especially when you feel like the lone champion in your organization.
To make it easier and more social, I’m launching the 2026 Continuous Discovery Habits Book Club. We’ll read the book together—one section per month—with discussion questions, practical exercises, and resources that help you actually do the work, not just read about it. Whether you’re picking up the book for the first time or revisiting it, the goal is to build real muscle memory in discovery.
By December, you won’t just understand continuous discovery—you’ll be practicing it.
Each month, I’ll share a reading guide with reflection prompts, exercises you can run solo or with your product trios, and short videos to help you spread the ideas across your team. I’ll monitor comments throughout the year so you can ask for help, share what’s working, and connect with peers—even if you join late.
I’ll also host quarterly live discussion sessions so we can compare notes, push through sticking points, and swap tactics with other empowered product teams. If you want to participate, grab a copy of the book (or dig up your old copy), share the "Spread the Love" videos to get friends and colleagues on board, reserve time to try the team exercises, and register for the community sessions. Let’s do this.
🎖️ This reading guide is brought to you by New Year, New Habit: The 5-Day Customer Interview Challenge. Become a more confident interviewer in less than a week. You’ll conduct one practice interview a day, get personalized and detailed feedback so you know exactly what to improve, and we’ll be giving out daily prizes to the most improved. Join the challenge today.
This Month’s Reading: Introduction; Chapter 1: The What and Why of Continuous Discovery; Chapter 2: A Common Framework for Continuous Discovery. Estimated reading time: ~40 minutes.
These chapters will introduce you to why discovery and delivery are not phases—they happen continuously. You’ll see a clear benchmark for what "continuous discovery" looks like, learn what product trios are and why they’re the foundation for good discovery, and explore six prerequisite mindsets (outcome-oriented, customer-centric, collaborative, visual, experimental, continuous) you’ll need before these habits can stick. You’ll also get the opportunity solution tree—a visual framework for connecting what you’re building to why you’re building it. Need a copy? Grab the book: https://amzn.to/3hGkNYT?ref=producttalk.org
We learn best in community. Use these short videos to share key concepts with teammates and invite them to read along: What is product discovery? https://videos.producttalk.org/videos/799fdbb41e16ebc4f0/what-is-product-discovery?ref=producttalk.org — a quick intro to the key idea behind discovery work. Defining continuous discovery https://videos.producttalk.org/videos/a79fdbba151ee3c72e/defining-continuous-discovery?ref=producttalk.org — a clear benchmark to aspire to. The rhythm of continuous discovery https://videos.producttalk.org/videos/4d9fd5b4111ee0c2c4/the-rhythm-of-continuous-discovery?ref=producttalk.org — the two small research activities you should do every week. The underlying structure of product discovery https://videos.producttalk.org/videos/449fdbb5191fedc4cd/the-underlying-structure-of-product-discovery?ref=producttalk.org — how outcomes, opportunities, and solutions connect. What’s a product trio? https://videos.producttalk.org/videos/a79fdbb31e1be2c12e/whats-a-product-trio?ref=producttalk.org — why cross-functional collaboration matters.
🎖️ This reading guide is brought to you by Just Now Possible, a podcast about how AI products come to life—straight from the builders. If you are being asked to add AI features to your roadmap, you don’t have to start from scratch. Get a head start by hearing how other teams are navigating similar challenges. Find it on YouTube, Apple Podcasts, and Spotify.
When we reflect and discuss what we read, we absorb more and apply it better. This month is about building awareness of where you are today—no judgment. The first step in any change is getting a baseline. Next month, we’ll take small steps to strengthen the habits.
Here are three prompts for individual reflection. 1) Think about a recent product decision your team made. Did you rely more on opinions, data, or customer input? Get specific. 2) Which of the six prerequisite mindsets (outcome-oriented, customer-centric, collaborative, visual, experimental, continuous) is strongest for you personally? Which would require the biggest shift? 3) What’s your reaction to weekly customer touch points? Does this excite you? Scare you? Something else?
And here are three prompts for team discussion. 1) Who on your team is responsible for discovery and delivery? How interconnected are these activities? 2) How does your team currently collaborate cross-functionally? When product, design, and engineering come together, is it to make decisions—or to hand off work? 3) Think of a recent feature your team built. What opportunity did it address? What else could you have built to address that opportunity?
For this introductory month, focus on seeing your current system clearly. In my experience, visibility alone reveals friction and makes the path to change obvious—and measurable.
Exercise: Draw Your Current Discovery Process. Time: 60 minutes. Do this solo first, then compare with your team. Take a blank sheet and draw how your team actually decides what to build. Show where ideas come from, who makes decisions and how, where (if anywhere) customers enter the picture, and how you know if you built the right thing. Then compare drawings with teammates. Where do perceptions differ? What does that say about your shared understanding?
Exercise: Audit Last Week’s Decisions. Time: 30 minutes. Do this solo or with your team. List every product decision your team made last week—big or small. For each decision, note who made it, what information it was based on, and whether customer input was part of the process (and how). Then look for patterns: how many included direct customer input versus assumptions, opinions, or secondhand information?
If you prefer an audio summary of this month’s reading—including the book chapters and the resources below—listen here: Stop Building The Wrong Things Faster (audio summary by NotebookLM): https://www.producttalk.org/content/media/2025/12/January—Stop_Building_The_Wrong_Things_Faster.m4a
Related in-depth guides to go deeper: Product Discovery Basics: Everything You Need to Know: https://www.producttalk.org/product-discovery/ Product Trios: What They Are, Why They Matter, and How to Get Started: https://www.producttalk.org/product-trios/ Opportunity Solution Trees: Visualize Your Discovery to Stay Aligned and Drive Outcomes: https://www.producttalk.org/opportunity-solution-trees/
Other voices worth reading: Product Discovery: Pitfalls and Anti-Patterns by Chris Jones: https://svpg.com/product-discovery-anti-patterns/?ref=producttalk.org Addressing the Challenges of Product Discovery by Saeed Khan: https://medium.com/swlh/the-challenges-of-product-discovery-6ac6109d13a8?ref=producttalk.org Making Product Discovery Work in Small Teams by Sofia Quintero: https://www.chargebee.com/blog/product-discovery/?ref=producttalk.org Product Waste and the ROI of Discovery by Richard Mironov: https://www.mironov.com/waste?ref=producttalk.org
Related course if you want structured practice: Product Discovery Fundamentals – this course walks you through the complete continuous discovery framework with hands-on exercises: https://learn.producttalk.org/cdh-master-class?ref=producttalk.org
Our live discussion schedule for 2026 (sessions are not recorded): Wednesday, March 18, 2026: 9am–10am PDT and 4pm–5pm PDT. Tuesday, June 16, 2026: 9am–10am PDT and 4pm–5pm PDT. Thursday, September 17, 2026: 9am–10am PDT and 4pm–5pm PDT. Wednesday, December 16, 2026: 9am–10am PST and 4pm–5pm PST. Invitations will go out to Supporting Members and CDH Members two weeks beforehand—reserve the time now.
As you work through this month’s material, connect it to your product strategy, outcomes vs output OKRs, and product roadmapping and sprint planning. In my teams, discovery sticks when product trios own the rhythm, weekly customer touch points are normalized, and the opportunity solution tree keeps everyone aligned on outcomes.
I’m thrilled to learn alongside you this year. Grab the book, invite your trio, and let’s build habits that last.
Inspired by this post on Product Talk.
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Build vs Buy in 2026: How I Make Confident, AI-Savvy Software Decisions That Scale
Every planning cycle, I’m asked the same high-stakes question: should we build or buy? In 2026, with generative AI reshaping the software landscape and budgets under scrutiny, the classic calculus needs an upgrade. The right call can accelerate time to value, protect precious engineering capacity, and sharpen competitive differentiation—while the wrong one can quietly inflate total cost of ownership for years.
“Navigate the build vs buy software dilemma, learn how AI is changing the game, and what you should leverage (and when).” That’s been my north star for product strategy this year, and it’s how I guide teams when the pressure is on.
My first principle is simple: build where we differentiate, buy where we need parity. If the capability is central to our value proposition or our defensibility, I’m inclined to build—often with a phased approach that de-risks scope. If it’s a non-differentiating layer (think billing, analytics plumbing, basic CRM integration), I’ll buy to accelerate, then revisit once scale and specialization justify a deeper internal investment.
AI changes the equation on both sides. On the “buy” side, modern platforms now ship agentic AI, fine-tuning options, and robust APIs that let us compose advanced capabilities fast. On the “build” side, AI workflows and toolchains (from code copilots to eval-driven development) compress cycle time, making bespoke solutions more attainable. The trade-off has shifted from pure functionality to questions of AI risk management, model governance, data privacy, and the portability of prompts, embeddings, and training data.
I evaluate decisions across two economic horizons: time to value versus total cost of ownership. Buying often wins the first round—faster deployment, proven reliability, and lower initial lift. But TCO can creep: integration work, per-seat or consumption SaaS pricing, training, vendor-driven roadmap gaps, and the “shadow ops” of maintaining connectors in our CI/CD. Building flips that profile: slower early velocity, higher upfront complexity, but potentially lower long-run costs and tighter fit with our platform scalability goals.
Operational risk matters just as much as features. I look at incident management posture, SRE maturity, SLAs, and DORA metrics to gauge resilience. If a vendor can’t meet our uptime and recovery expectations—or if their roadmap pace mismatches our deployment frequency—we’re effectively renting risk we can’t control. Conversely, if our team can’t realistically support the operational burden, buying is the safer choice.
Security, regulatory compliance, and data governance are non-negotiables. I assess privacy-by-design, data residency, audit logs, role-based access, SOC2/ISO coverage, and threat detection and response. For AI-heavy systems, I add model lineage, red-teaming practices, PII handling, and retention policies. If we can’t verifiably meet our obligations in a build scenario within the launch window, we buy and require clear data exit and portability clauses.
To keep decisions objective, I use a lightweight scorecard across five dimensions: differentiation, urgency/time to value, regulatory/security risk, integration complexity, and AI leverage/portability. We weight criteria with product trios (PM, design, engineering), run discovery spikes, and validate assumptions with stakeholder management up front. A disciplined scorecard curbs recency bias and helps us communicate trade-offs to leadership.
In practice, I favor staged commitments. When uncertainty is high, we buy to learn—ship value quickly, instrument usage, and collect evidence. If adoption proves sticky and integration pain remains moderate, we double down with deeper vendor integration. If we uncover unique needs or cost inflection points, we pivot to a build plan that reuses learnings, data models, and UX patterns from the bought solution to reduce risk.
AI-specific choices deserve their own pass. For example, if we need retrieval-augmented generation, I’ll often buy for the orchestration and observability layer while building our domain-specific retrieval-first pipeline and prompt engineering guardrails. That split gives us speed plus control: we retain our IP and data gravity while tapping best-in-class tooling that evolves with the ecosystem.
Vendor strategy matters as much as technology. I negotiate clear data export, transparent API quotas, sandbox environments for continuous discovery, and price protections for growth. I pressure-test roadmaps, ask for integration references, and align on outcome-based milestones rather than feature checklists. Strong partners welcome this rigor; weak ones stall—another useful signal.
On the build side, I right-size ambition. We target minimum lovable scope, isolate risk in early sprints, and leverage open source where it’s mature and secure. We design for modularity so we can swap components without rewriting the world, and we budget time for in-app guides and product tours to smooth adoption, because user activation is the real finish line.
Here’s the playbook I return to: buy to validate and compress time to value; build to differentiate and reduce long-run TCO; continuously re-evaluate as the AI toolchain and our scale evolve. With a transparent scorecard, a bias for learning, and a clear view of risk, the build vs buy decision becomes less of a leap of faith and more of a repeatable product management capability.
2026 will reward teams that move fast without mortgaging the future. Make the call deliberately, instrument the outcomes, and stay humble—because the best strategy is the one you can adapt as new evidence arrives.
Inspired by this post on Product School.
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Agent Analytics That Matter: How Pendo Drives Adoption, Cuts Costs, and Reduces Risk
Every quarter, I revisit the same three questions: Are we accelerating adoption, lowering cost-to-serve, and managing risk without slowing the roadmap? Tools that help me answer all three with clarity earn a place in my stack. That’s why the concept behind Pendo’s Agent Analytics resonates so strongly—it gives product leaders a way to see, in one view, how users engage with AI-powered assistants, in-app guides, and core workflows, and how those behaviors translate into product-led growth.
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.
In practice, Agent Analytics functions as a unified analytics platform for the modern product team. I can observe how users interact with agents and nudges inside the product, connect those interactions to user activation and retention analysis, and prioritize improvements that deliver measurable outcomes. The result is fewer blind spots across the journey and a tighter feedback loop between discovery and delivery.
The real value shows up when I pair analytics with targeted interventions. For example, I’ll instrument critical paths, baseline activation, then use in-app guides to remove friction at the exact moment users need help. I incorporate A/B testing and continuous discovery to validate which prompts, pathways, or workflows actually move the needle. With a clean view of adoption, engagement, and time-to-value, my team can double down on what works and retire what doesn’t—faster.
Risk reduction is equally important. With clear behavioral signals, I can spot confusing prompts, unhelpful agent responses, or unexpected drop-offs before they scale into churn or support volume. That visibility informs our product strategy, aligns stakeholders on trade-offs, and keeps our governance tight without stifling innovation—especially critical as AI Strategy becomes part of everyday product decisions.
If you’re weighing whether Agent Analytics deserves a place in your toolkit, consider this: better instrumentation yields better decisions. When you unify guide interactions, agent engagement, and core product usage, you can attribute uplift more precisely, forecast impact with greater confidence, and operationalize product-led growth. That’s how we increase adoption, cut unnecessary cost, and de-risk the roadmap—while building experiences customers actually love.
Inspired by this post on Pendo – Perspectives.
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Stop Choosing: Blend Inside-Out and Outside-In Thinking to Accelerate Product-Led Growth
I’ve never seen great products emerge from a one-sided mindset. Inside-out thinking (strategy-first) and outside-in thinking (customer-first) aren’t rivals—they’re a flywheel. When I weave product vision and defensible differentiation together with real customer signals and behavioral data, adoption climbs, engagement deepens, and the roadmap becomes a catalyst for growth rather than a list of features.
For clarity: inside-out anchors on product strategy, value proposition, and the unique capabilities only we can deliver. Outside-in centers on continuous discovery, user research, and telemetry that reveals what customers actually do—not just what they say. At HighLevel, we pair these perspectives in every planning cycle so we’re bold in direction and grounded in evidence.
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 why the blend matters. Product-led growth lives or dies on moments like activation, time-to-first-value, and day-30 retention. Inside-out thinking ensures we’re building toward a compelling vision; outside-in thinking ensures users can discover, adopt, and realize value through clear onboarding, in-app guides, and contextual product tours.
Here’s how I apply it in practice. We start by articulating the smallest, sharpest version of our strategy—who we serve, the jobs we must win, and the non-negotiable outcomes. Then we pressure-test that thesis with continuous discovery: call snippets, funnel analysis, pathing, and retention analysis by cohort. When friction shows up in onboarding or early feature adoption, we deploy targeted in-app guides and tours to accelerate user activation without bloating the product or training costs.
A simple operating rhythm keeps the balance: begin each quarter with outcomes vs output OKRs tied to adoption and retention; instrument flows to expose drop-offs; ship iterative improvements; and reinforce them with just-in-time guidance. We use outside-in signals to sequence what we tackle next, and inside-out conviction to avoid chasing noise. The result is faster learning cycles and fewer expensive reworks.
Measurement closes the loop. I track activation rate, time-to-first-value, engagement with the few behaviors that predict renewal, and the impact of each guide or tour on completion rates. When we see lift, we codify the pattern; when we don’t, we prune and refocus. That evidence-based cadence keeps teams empowered and stakeholders aligned.
Culture makes this sustainable. Empowered product teams own outcomes, not tickets. Stakeholder management becomes easier when decisions are grounded in a clear strategy and transparent evidence from real users. And customers feel the difference when the product teaches itself—meeting them with the right help, in the right moment, without getting in their way.
If you’ve been choosing between inside-out and outside-in, stop. Fuse them. Lead with a crisp product strategy, listen with humility, and operationalize adoption through purposeful onboarding, in-app guides, and product tours. That’s how we compound learning, reduce risk, cut support costs, and accelerate product-led growth.
Inspired by this post on Pendo – Perspectives.