Is hiring broken—or just badly designed? I’ve been sitting with that question after a recent conversation that crystallized what I see across product organizations: AI-fueled application overload, sprawling interview loops, and fuzzy criteria that invite groupthink at exactly the wrong moments. If you’ve ever watched a promising candidate stall out late in the process, you’re not alone. Listen to this episode on: Spotify | Apple Podcasts.
Here’s the reality I’m observing in the market: Layoffs and hiring freezes have flooded the funnel, while AI tools make it trivial to submit hundreds of applications. Companies are overwhelmed, so they respond by adding more interviews and more stakeholders, hoping more touchpoints equal better signal. In practice, that complexity often dilutes accountability and increases noise—especially for product management leadership roles where clarity, not consensus theater, determines success.
I’ve seen too many offers derailed by “one last step.” A candidate clears every structured interview, then a casual lunch or unframed panel suddenly becomes the deciding factor. The team isn’t briefed on what to evaluate, one lukewarm comment lands, and group dynamics cascade into a no-hire. That’s not rigor—it’s randomness masked as prudence.
Groupthink ≠ good hiring decisions. When everyone has veto power, risk-averse no-decisions become the default. Focus-group-style interviews create bias, not signal, and “culture fit” often becomes a proxy for stereotyping or personal preference. As product leaders, we’d never ship a feature based on vibes; we shouldn’t make high-stakes hiring calls that way either.
There’s a better way—and it mirrors how we run great product discovery. Define who you’re hiring before writing the job description. Set clear success metrics for the role. Assign each interviewer specific criteria to evaluate. Treat hiring like product discovery: intentional, structured, and evidence-based. In my teams, that looks like tight scorecards, interviewer calibration, and a decision owner who synthesizes evidence—not a popularity contest where the loudest voice wins.
Chemistry checks still matter, but only when we define what collaboration actually means for the role. Introversion, debate style, or lunch-table small talk are not performance indicators. I look for behaviors we value in empowered product teams—clarity of thinking, healthy dissent, co-creation under constraints—often via a real working session with the future product trio. Diverse teams outperform homogenous ones, even if not everyone “vibes,” so I optimize for complementary strengths over sameness.
If you’re a candidate, remember: When a process feels broken, it’s often not about you. Ask how you’re being evaluated to gauge process maturity; a thoughtful team will happily walk you through their rubric and what great looks like. For structure and support, I’ve seen “Who: The A Method for Hiring” help leaders clarify requirements; “Never Search Alone” and joining a Job Search Council (JSC) can give you peer accountability and sharper narratives. For current openings, I regularly point PMs to Scott Baldwin’s PM job postings on LinkedIn.
My challenge to fellow product leaders: Audit your hiring process the way you’d audit your roadmap. Where are decisions getting stuck? Where are you over-indexing on consensus and under-indexing on evidence? Tighten the criteria, streamline stakeholders, and instrument the funnel so you can learn and improve. The payoff is faster, fairer, more confident decisions—and teams that reflect the rigor we expect in product strategy and stakeholder management.
What’s one change you can make this week—reworking the scorecard, calibrating interviewers, or replacing an unstructured lunch with a real collaboration exercise? Small improvements compound. Let’s build hiring systems that are worthy of the talent we’re trying to attract.
“Continuous Discovery Habits” turns five this year, and I’m celebrating by reading the book together with you. Each month, I’m releasing an in-depth reading guide designed for empowered product teams and product trios—complete with the chapters we’ll read, a preview of the key concepts, short shareable videos, individual and team discussion prompts, team exercises you can run immediately, and additional reading to go deeper.
We’ll discuss each month’s reading in the comments, and we’ll gather quarterly for live calls. If you’re joining late, no problem—I’ll be monitoring comments throughout the year. Start with the current month or go back to January (https://www.producttalk.org/lets-read-continuous-discovery-habits-together-january-2026/). Jump in where it serves you best, ask for help, share what’s working, and connect with other readers any time.
If you want to participate, grab a copy of the book (https://amzn.to/3hGkNYT?ref=producttalk.org)—or dust off your old one—share the “Spread the Love” videos with your colleagues, set aside time to run the team exercises, and register for the community sessions. Let’s do this.
This Month’s Reading
Chapters: Chapter 3: Focusing on Outcomes Over Outputs
Estimated reading time: ~22 minutes
This chapter zeroes in on the critical difference between business outcomes and product outcomes—and why it matters which one your team is assigned; how to translate lagging business metrics into actionable product outcomes you can actually influence; why setting outcomes should be a two-way negotiation between leaders and product trios; when to start with a learning goal versus a performance goal; and five common anti-patterns that derail outcome-focused teams. Need a copy? Grab the book (https://amzn.to/3hGkNYT?ref=producttalk.org).
Share the Love with Friends and Colleagues
We learn best in community. I like to seed conversations across my org with short, high-signal content—especially when I’m shifting a culture from outputs to outcomes and sharpening OKRs. Use these short videos to bring peers into the conversation and invite them to read along:
“What’s an outcome?” (https://videos.producttalk.org/videos/ea9fdab71d1ee3c263/whats-an-outcome?ref=producttalk.org) — The real value of starting with an outcome. “Business outcomes vs. product outcomes” (https://videos.producttalk.org/videos/069fd5b5101ee2c78f/business-outcomes-vs-product-outcomes?ref=producttalk.org) — Why product teams need product outcomes, not business outcomes. “What’s the difference between OKRs and outcomes?” (https://videos.producttalk.org/videos/069fdab61919e4c38f/whats-the-difference-between-okrs-and-outcomes?ref=producttalk.org) — Any outcome can be represented as an OKR. “Understanding revenue model formulas” (https://videos.producttalk.org/videos/799fd5b5101ee2c4f0/understanding-revenue-model-formulas?ref=producttalk.org) — How to identify the business outcomes your company cares about. “Revisit your outcome every quarter” (https://videos.producttalk.org/videos/449fd5b4111ee0cfcd/revisit-your-outcome-every-quarter?ref=producttalk.org) — Don’t abandon your outcome, but do revisit how you measure it.
Reflect and Discuss What You Read
Reflection is the conversion rate optimizer for learning. When we pause to discuss what we’re reading, we retain more and apply it faster—especially in product discovery and product strategy work. This chapter challenges us to update our definition of success: away from features shipped and toward outcomes achieved. This month, I’m examining my own relationship with outcomes—where I’ve been rigorous, where I’ve drifted, and how I can help my teams strengthen day-to-day behaviors.
Individual Reflection
If your team isn’t working toward an outcome, look at the features or projects on your roadmap and ask: What impact are they supposed to have? If they succeed, what customer behavior or business result would change? If your team does have an outcome, consider whether it’s a business outcome, a product outcome, or a traction metric—and how that choice shapes your daily decisions and discovery cadence. Finally, think about the last time your team’s outcome changed: Was it a deliberate strategic shift, or did it feel like ping-ponging from one priority to the next?
Team Discussion
As a team, classify your current outcome: Is it a business outcome, a product outcome, or a traction metric? If it’s a business outcome, identify the leading customer behaviors that would signal momentum; if it’s a traction metric, broaden it to a product outcome that gives you more room to explore. Then, name which of the five anti-patterns (pursuing too many outcomes, ping-ponging, individual outcomes, outputs as outcomes, or tunnel vision) shows up for you and pick one concrete change. Finally, assess how outcomes are set: Are they handed down, or does your product trio co-create them? What would it take to make this a true two-way negotiation?
Put It Into Practice
Understanding the difference between business outcomes and product outcomes is table stakes. Translating one into the other is where product management leadership shows up. These exercises will help you connect company goals to customer behavior, avoid outcomes vs output OKRs traps, and increase your span of control over meaningful change.
Exercise: Map Your Revenue Model
Time: 30 minutes. Do this: Solo first, then share with your team. Start with this question: How does your company make money? Write out the formula for your revenue model. For example, a subscription business might be: Revenue = Number of Customers × Average Monthly Spend × Retention. Once you have the formula, identify each variable as a potential business outcome. Then, for each business outcome, brainstorm two to three product outcomes (customer behaviors or sentiments) that might be leading indicators. Which of these product outcomes is your team best positioned to influence?
Exercise: Audit Your Current Outcome
Time: 45 minutes. Do this: With your product trio. Take your team’s current outcome and run it through a quick diagnostic: Is it a business outcome, product outcome, or traction metric? If it’s a business outcome, what product outcomes might drive it? If it’s a traction metric, how might you broaden it to a product outcome? Is it a leading indicator or a lagging indicator? Can you measure progress weekly, or do you have to wait months? Is it within your team’s span of control? Based on your answers, draft a revised outcome that offers more actionable feedback while still connecting to business value, and prepare to discuss this with your product leader.
Go Deeper: Additional Reading
If you prefer an audio summary of this month’s reading, including the book chapter and the resources below, I’ve included an audio version at the end of this post for paid subscribers.
Related In-Depth Guide: Shifting from Outputs to Outcomes: Why It Matters and How to Get Started (https://www.producttalk.org/shifting-from-outputs-to-outcomes/).
Supplementary Reading: Empower Product Teams with Product Outcomes, Not Business Outcomes (https://www.producttalk.org/2020/05/product-outcomes/). Defining Product Outcomes: The 8 Most Common Mistakes You Should Avoid (https://www.producttalk.org/2022/12/defining-product-outcomes/). Understanding How Product Outcomes Connect to Revenue and Costs (https://www.producttalk.org/2023/04/connecting-product-outcomes-to-revenue-and-costs/). Product in Practice: Iterating to an Actionable Outcome at tails.com (https://www.producttalk.org/2020/08/actionable-outcomes/). Product in Practice: Iterating on Outcomes with Limited Data (https://www.producttalk.org/2023/12/iterating-on-outcomes-with-limited-data/). Measurable Outcomes – All Things Product with Teresa Torres and Petra Wille (https://www.producttalk.org/measurable-outcomes-all-things-product-podcast-with-teresa-torres-petra-wille/).
Other Voices: The Business Equation by Brett Bivens (https://venturedesktop.substack.com/p/the-business-equation?ref=producttalk.org). KPI Trees: How to Bridge the Gap Between Customer Behavior, Product Metrics, and Company Goals by Petra Wille and Shaun Russell (https://www.petra-wille.com/blog/kpi-trees-how-to-bridge-the-gap-between-customer-behavior-product-metrics-and-company-goals?ref=producttalk.org). Persistent Models vs. Point-In-Time Goals by John Cutler (https://cutlefish.substack.com/p/tbm-2553-persistent-models-vs-point?ref=producttalk.org). Is It Time to Ditch the Old SaaS Metrics? by Kyle Poyar (https://openviewpartners.com/blog/saas-metrics-plg/?ref=producttalk.org). How Engagement Metrics Can Be Misleading by Oleg Yakubenkov (https://gopractice.io/blog/how-engagement-metrics-can-be-misleading/?ref=producttalk.org). Subscription Churn Metrics and Benchmarks for Operators by Elena Verna (https://www.elenaverna.com/p/subscription-churn-benchmarks-and?ref=producttalk.org).
Related Courses: Business Fundamentals: Navigate Your Business Context with Confidence (https://learn.producttalk.org/course/business-fundamentals?utm_source=Product+Talk&utm_medium=cdh-book-club-february-2026).
Our Live Discussion Schedule
Our live discussion sessions are for paid subscribers and will not be recorded. Invitations will go out to Supporting Members and CDH Members (http://members.producttalk.org/?ref=producttalk.org) two weeks before each event—reserve time on your calendar now so you can participate fully and bring real examples from your team.
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.
Audio Summary
Prefer to listen? I’ve included an audio summary—Stop Measuring Code Start Measuring Behavior—at the end of this post so you can review the main ideas on your commute or between meetings.
I’m excited to dive into outcomes with you this month. As a product leader, I’ve seen teams transform their product discovery, product roadmapping and sprint planning, and OKR quality when they anchor on clear product outcomes tied to business value. Let’s build that muscle together and make this a quarter where we stop measuring output and start driving outcomes.
I just tuned into the latest conversation on the upcoming Product at Heart 2026, and it hit on the exact challenges product leaders are navigating right now: curating meaningful content in a world where AI moves faster than our agendas, designing formats that create real connection, and ensuring every minute earns its place. Listening to Petra Wille and Teresa Torres map out the speaker lineup, workshops, and structural shifts, I found myself nodding along—this is the kind of thoughtful curation we need if we want product teams and product leaders to walk away with practical value, not just inspiration.
Listen to this episode on: Spotify | Apple Podcasts
What stood out immediately is the bold move to a single-track conference for 2026. In an era of gen ai hype and endless breakouts, this choice signals clear intent: tighter curation, a shared experience, and less FOMO. The team isn’t carving out a separate AI track—and I love that decision. Their stance is simple and sensible: No AI track—AI will show up everywhere, but not as a siloed topic. The team sees it as part of the everyday toolkit. That mirrors how high-performing, empowered product teams actually work today—AI Strategy and AI workflows are part of the operating system, not a side show.
The keynote lineup is already compelling. Christian Idiodi (SVPG) brings storytelling that turns product principles into habits you can actually use on Monday. Elaine Kasket, cyber-psychologist, exploring digital afterlife and AI replicas, will push us to think more deeply about the human side of our systems. And Teresa Torres will be sharing what she’s learning about AI—exactly the kind of continuous discovery mindset we need as we integrate LLMs into product discovery and delivery.
I’m also thrilled to see roundtables become what they’re calling an “alternative track.” That’s a smart way to deepen learning without fragmenting attention. The best conference ROI I’ve had often comes from targeted small-group conversations—where product trios compare approaches, swap metrics frameworks, or challenge each other’s product strategy assumptions. It’s a design choice that rewards curiosity and builds communities of practice.
We also get a behind-the-scenes look at Teresa’s Maker Studio workshop, where participants will build personal AI workflows. That’s exactly the hands-on, practitioner-first approach teams need right now—less demo theater, more systems that stick. If your roadmap includes integrating LLMs into continuous discovery or augmenting your team’s decision velocity, this kind of guided practice is gold.
The broader workshop slate looks deep and balanced. Expect returning favorites and practical frameworks: Rich Mironov on the realities of product leadership in complex orgs; Büşra’s metrics workshop translating outcomes into action; and an overview of additional workshops from Rich Mironov, Büşra Coşkuner, Marcus Castenfors, and Özlem Yüce. From success metrics to toolkits for product managers, the content spans IC to product management leadership—ideal if you’re stepping into new roles or scaling empowered product teams.
One of the most exciting evolutions is the Product Leadership Event, now a 1.5-day retreat. The format blends talk sessions, mini-workshops, dinners, and small-group excursions (boat rides, improv, etc.), giving leaders time and space to exchange playbooks, stress-test decisions, and build real relationships. It’s capped at 60 attendees (all in product leadership roles) to keep it intimate and useful. As someone who believes in outcomes vs output OKRs and first principles decision making, I appreciate how this structure encourages depth over breadth—and real accountability among peers.
Here are the core takeaways I’m carrying into my own planning: single-track means tighter curation, so every talk has to earn its place. Roundtables are growing into an “alternative track,” offering more ways to engage beyond stage talks. Workshops go deep and meet you where you are—IC, manager, or executive. And the leadership retreat expands to maximize learning from peers, not just from the stage. If you care about product discovery, product strategy, and conference networking that leads to actual business impact, this program looks thoughtfully engineered.
If you’re planning your 2026 calendar—or just curious how conferences evolve alongside the craft—this is a thoughtful walkthrough of what to expect. Come say hi to Teresa and Petra—on stage, at a roundtable, or somewhere in the hallway conversations that make these events memorable.
For more context and resources mentioned, explore: Product at Heart, Arne Kittler, Mind the Product, Christian Idiodi of Silicon Valley Product Group, Elaine Kasket, House of Beautiful Business, The 7 Habits of Highly Effective People by Stephen Covey, Rich Mironov, Marty Cagan, Claude Code, Codex by OpenAI, Marcus Castenfors, Büşra Coşkuner and her Success Metrics: A Playbook for Product Managers, Özlem Yüce’s Essential Toolkit for Product Managers, Petra’s Product Leadership Wheel (PLwheel), and Netlight.
Follow Teresa Torres: https://ProductTalk.org
Follow Petra Wille: https://Petra-Wille.com
Full transcripts are only available for paid subscribers.
I’ve led product teams through countless discovery cycles, and nothing has accelerated our learning loops like AI. By weaving AI into our continuous discovery practice at HighLevel, I cut time-to-insight, reduce risk earlier, and keep our product strategy relentlessly focused on customer outcomes.
AI streamlines product discovery by accelerating research, prototyping, and validation, enabling teams to make faster, smarter, and user-driven decisions.
In the research phase, I use gen ai and LLMs for product managers to synthesize interviews, cluster themes, and surface unmet needs in minutes instead of days. Pairing those qualitative insights with behavioral signals in Amplitude analytics helps me spot high-intent cohorts and friction points at scale, so our problem framing is both human-centered and data-backed.
From there, I translate insights into crisp hypotheses and prioritize with the Kano Model and outcomes vs output OKRs. To keep experiments honest, I define a minimum detectable effect (MDE) up front and design A/B testing plans that reflect realistic traffic and seasonality, ensuring our decisions are statistically grounded rather than anecdotal.
Prototyping is where gen ai for product prototyping really shines. I spin up multiple UX flows, UI copy variants, and edge-case scenarios using prompt engineering, then iterate with rapid feedback from product trios. When needed, I mock in-app guides and product tours to validate onboarding concepts before we commit to code, preserving velocity without sacrificing quality.
For validation, I lean on a mix of lightweight experiments—fake-door tests, concierge pilots, and targeted A/B testing—augmented by in-product surveys via Pendo or Intercom. For AI-powered features, I apply eval-driven development to measure relevance, latency, and safety, so we can ship responsibly while maintaining the pace of learning.
This approach only works when the team is structured to move fast. Empowered product teams and product trios own discovery end-to-end, with clear guardrails around data governance, privacy-by-design, and AI risk management. That alignment lets us shift from opinions to evidence, and from output to outcomes, without friction.
If you’re getting started, pick one discovery loop to transform: automate research synthesis, prototype two to three variants with AI, and validate with a tightly scoped experiment. Instrument your analytics, track time-to-insight and time-to-prototype, and iterate your product roadmapping and sprint planning with what you learn. The payoff is immediate: faster cycles, stronger conviction, and a more user-driven path to product-led growth.
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.
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.
I’ve been systematically exploring how the product model shows up inside iconic companies. After studying “The Product Model at Spotify” and “The Product Model at Amazon,” I’m turning my lens to Google—specifically, how the product operating model, product culture, and product strategy manifest in practice and what we can pragmatically take back to our own organizations.
When I talk about the product model, I’m looking at the machinery that connects strategy to outcomes: empowered product teams, clear decision rights, tight product trios, continuous discovery, data-informed bets, and an operating cadence that enables learning at speed. My goal here is to unpack how those elements come together at Google and translate them into repeatable patterns you can adopt.
At a high level, I focus on how teams are empowered to solve problems rather than ship outputs, how outcomes vs output OKRs clarify what matters, and how experimentation (from rapid prototyping to A/B testing) de-risks decisions before they scale. I also examine how engineering and product partner to balance platform scalability with customer value, and how stakeholder management reinforces alignment without slowing teams down.
Why does this matter? Because the product model is a lever for resilience and speed. When product strategy is explicit and the operating model is built for learning, organizations multiply the impact of talented people. That’s how small, focused teams repeatedly deliver outsized results—even in complex, regulated, or high-scale environments like Google.
In the sections that follow, I’ll synthesize what I see as the core patterns behind Google’s approach and distill them into actionable guidance: how to structure product trios, how to run continuous discovery alongside delivery, how to set and calibrate OKRs for outcomes, and how to evolve your product culture so empowered product teams can do their best work. My aim is not to idolize a model, but to extract what’s portable and help you adapt it to your context.
I spend my days partnering with technical leaders who bridge invention and impact. The role of a Senior Software Engineer at Amplitude working on AI-powered products epitomizes how engineering and product fuse to ship customer value with speed, safety, and conviction. In my world, that fusion isn’t accidental—it’s designed, measured, and relentlessly improved.
When I form product trios—engineering, product, and design—we clarify the problem, the target users, and the measurable outcomes before a single line of code ships. This is how empowered product teams operate: we trade feature wish-lists for hypotheses, align on success metrics, and commit to learning loops that turn ambiguity into progress.
On the technical front, modern AI systems demand a retrieval-first pipeline, robust data contracts, and a thoughtful orchestration layer for LLMs. I expect eval-driven development to be first-class: offline unit-style evals for prompts and policies, and online evals that track behavior changes and quality at scale. This rigor gives us confidence to ship, learn, and iterate without burning cycles on guesswork.
Velocity matters, and so does reliability. I look for CI/CD that makes small, safe, frequent releases the default, and for DORA metrics to shine a light on delivery health. Pair that with platform scalability, clear SLOs, and pragmatic SRE practices, and teams earn the right to move fast without breaking trust.
Responsible AI is non-negotiable. We operationalize AI risk management with guardrails, input/output filters, red-teaming, and human-in-the-loop review where stakes are high. Data governance and privacy-by-design ensure that our creativity never outruns our compliance—because durable products are built on durable trust.
Impact comes from evidence. I advocate for disciplined A/B testing, careful minimum detectable effect (MDE) planning, and retention analysis that ties feature work to real business outcomes. Clear analytics pipelines and transparent dashboards keep stakeholders aligned and make good decisions repeatable.
Ultimately, the Senior Software Engineer I want to collaborate with is a builder who balances systems thinking with customer empathy: someone who can design reliable architectures, instrument the work with meaningful evals, and co-lead discovery to de-risk the roadmap. When we combine that mindset with crisp execution, AI-powered products stop being demos—and start becoming indispensable.
Inspired by this post on Amplitude – Perspectives.
I recently listened to Role of Leadership in Transformations – All Things Product Podcast with Teresa Torres & Petra Wille, and it crystallized a pattern I’ve seen across multiple transformations: teams often get trained in continuous discovery, but nothing changes because leadership habits stay the same. If you want to move from projects to true product thinking, “train your leaders first” isn’t a catchy mantra—it’s a prerequisite.
The episode digs into why discovery training can be stellar while adoption still stalls. I’ve witnessed this firsthand: teams return excited to interview customers and test ideas, but leaders continue to manage via features, roadmaps, and approvals. The result is predictable—discovery fades. When leaders evolve how they evaluate work, talk about outcomes, and shape rituals, discovery sticks. Without that shift, even energized, empowered product teams drift back to output.
What resonated most was how organizational dynamics kick in the moment teams start bringing real customer evidence to the table. Discovery uncovers conflicts. Sales, account management, stakeholders, and executives all feel the impact when the old “my job is to tell teams what to build” mindset collides with evidence-driven practices. Hierarchy also clashes with modern product practices—because in discovery, “all ideas come equal.” Product culture isn’t an accident; it must be intentionally created through norms, expectations, and systems that prioritize outcomes over output.
I’ve also seen the leadership skills gap up close. Many product leaders never learned continuous discovery themselves, so they aren’t equipped to coach it, critique it, or celebrate it. This is where great product management leadership shows up: the ability to assess discovery quality, reinforce outcomes vs output OKRs, and run cadences that create momentum. Leaders who invest in building these muscles—often through communities of practice and structured coaching—transform the operating environment for product trios and cross-functional teams.
The episode’s discussion of pilot teams is spot-on. Start small to surface hidden blockers—the corporate “immune system”—before going broad. Pilots expose decision bottlenecks, misaligned incentives, and policy friction that standard training never reveals. Tools like the Product Leadership Wheel help set clearer expectations for the craft of product leadership, while a coherent Product Operating Model makes the path from pilots to full transformation explicit and durable. I’m particularly excited about resources like the Discovery Habits Toolbox because they give leaders practical ways to coach continuous discovery without reverting to feature policing.
Here are the big takeaways I’m carrying forward. Skills training isn’t enough—if leaders still manage through feature requests and static roadmaps, teams will abandon discovery even if they loved the training. Leaders need training too—they must know how to evaluate discovery work, talk about outcomes, and create rituals that reinforce new habits. Discovery will surface conflicts—plan for stakeholder management, alignment with sales and account teams, and executive sponsorship. Product leadership is a craft—seniority alone doesn’t create clarity, systems, or culture. And transformations should start with leaders and pilot teams—because that’s where the real blockers live.
If you want to go deeper, listen to this episode on Spotify: https://open.spotify.com/episode/5cBTEbYX1YW3BF6icAPXzi or Apple Podcasts: https://podcasts.apple.com/kh/podcast/role-of-leadership-in-transformations/id1794203808?i=1000740342572. It’s a concise masterclass on why leadership behaviors—not just team skills—determine whether continuous discovery thrives.
For further exploration, I recommend these resources. Follow Teresa Torres: https://ProductTalk.org. Follow Petra Wille: https://Petra-Wille.com. Product Talk Academy’s Train Your Team by Teresa Torres: https://learn.producttalk.org/train-your-team. Melissa Perri’s “Train leaders first, not last.” Linkedin post: https://www.linkedin.com/posts/melissajeanperri_train-leaders-first-not-last-most-product-activity-7380927349732839424-sqBJ/. Coaching for Product Leaders/Executives by Petra Wille: https://www.petra-wille.com/coaching-packages. Product Leadership Wheel by Petra: https://www.petra-wille.com/plwheel.
To get hands-on with discovery skills, check out Story-Based Customer Interviews: https://learn.producttalk.org/course/story-based-customer-interviews. For visual management, see An idea board—do we see enough potential?: https://images.squarespace-cdn.com/…/idea_board3.png and Four Taskboards in a simple illustration: Idea Board, Product Overview Board, Product Discovery Board and Development Team Board: https://images.squarespace-cdn.com/…/boards.png. Opportunity Assessment: Do We Want to Invest in Discovering This Idea?: https://www.petra-wille.com/blog/opportunity-assessment-do-we-want-to-invest-in-discovering-this-idea?rq=taskboard.
If you’re preparing your organization to adopt a product operating model, read Is Your Organization Ready to Adopt the Product Operating Model?: https://www.producttalk.org/organizational-readiness/ and The Product Operating Model Explained: From Pilot Teams to Full Transformation: https://www.producttalk.org/the-product-operating-model/. Communities of practice can accelerate leadership growth: Community of Practice by Petra: https://www.petra-wille.com/community-of-practice. For foundational texts, see TRANSFORMED: Moving to the Product Operating Model: https://www.svpg.com/books/transformed-moving-to-the-product-operating-model/ and EMPOWERED: Ordinary People, Extraordinary Products: https://www.svpg.com/books/empowered-ordinary-people-extraordinary-products/.
I’d love to hear how you’re enabling continuous discovery in your context. What leadership behaviors have made the biggest difference? Where does your corporate immune system show up, and how are you addressing it with pilot teams, clearer expectations, and a consistent product operating model? Share your perspective—I read every comment.
Most mornings start the same way for me: coffee in hand, I sit down, open Claude Code, and type /today. In a few seconds, Claude pulls fresh tasks from my Trello board, compiles a clean today.md with what matters most, and assembles a research digest of the latest academic work across my focus areas.
Scanning that today.md has become my daily ritual. My workload typically spans writing, coding, and administration. I now make a habit of asking Claude, "What's on my to-do list that you can help with?" That simple question keeps me honest about where AI can accelerate my day.
I’m experimenting with a workflow where Claude enriches every task based on what it can take on or accelerate. It’s still early, so we iterate together for a few minutes each morning to tighten the loop and improve the prompts and outputs.
Next up is my research digest. I skim, download the PDFs that look promising, and move on. Tomorrow, Claude will deliver detailed summaries of every paper I saved—so I stay current without burning hours on search and sorting.
For the first few hours, I protect deep work. Today, that means writing this article. My to-do list and draft live side-by-side in Obsidian, so I click directly from the task into the outline, pick up my running conversation with Claude, and get right back into flow. I pair-write: we outline, I draft, and then I ask, "I wrote the intro. What do you think?"
A terminal-based AI helper suggests concrete ways to lighten your workload—draft a blog, plan 2026, launch a course, migrate files, craft a survey, and digest research—so you can pick the next task fast.
Claude gives pointed feedback—what’s working, what needs tightening—and we iterate. This is genuinely how I work now. I pair with Claude on almost everything I do. It didn’t happen overnight; over the past five months, I’ve built a personal AI-enhanced operating system that has fundamentally improved how I operate: more output, faster cycles, and frankly, more joy in the work.
Because it’s made such a difference, I’m sharing the playbook. If you’re new to Claude Code or want to get more from it, start here:
Claude Code: What It Is, How It's Different, and Why Non-Technical People Should Use It
Stop Repeating Yourself: Give Claude Code a Memory
How to Use Claude Code Safely: A Non-Technical Guide to Managing Risk
In recent office hours, one question came up again and again: Where do I start—what should I automate and what should I have AI augment? Today, I’ll walk through how I decide, share my own workflows, and show how I prioritize what to build next. Next week, we’ll get into how to design and build personal workflows.
This series was inspired by my personal usage of Claude Code. I have not received any compensation from Anthropic for writing this series. And you can trust that if that ever changes, I will disclose it. This is not only required by the FTC here in the US, but I strongly believe it is the right thing to do. You can count on me to do so.
Understanding what AI workflows can do for you
Peek inside a dark-themed writing workspace where a markdown editor displays an article on choosing tasks to automate with AI. The sidebar organizes notes, while the draft outlines pulling Trello tasks, making today.md, and using Claude.
I started with ChatGPT in the browser not long after it launched and quickly began asking, “Can ChatGPT help with this?” As my use cases grew (and my patience for copy-paste vanished), I moved to Claude Code. The philosophy never changed: continuously push the envelope of what LLMs can do today while managing risk.
My default stance is to attempt everything with AI, then decide what becomes a reusable workflow versus a one-off assist. A workflow, to me, is a sequence of steps where some are automated by AI, others are AI-augmented, and some still require me.
Across my setup, clear patterns emerged. I use AI to: (1) do more of what I’m already good at, (2) eliminate friction in frequent tasks, and (3) remove what drains me. The goal is simple: multiply impact without sacrificing quality.
Take writing. I now average about 35,000 words per month—up from roughly 8,000. I’m writing more often and in more depth. I draw more from academic research and include more stories—both my own and those from others. Claude gives me detailed feedback on everything I write, which helps me maintain momentum. It’s remarkable how often a simple nudge—“Ready to write the next section?”—keeps me in the zone. I also spend more time with Claude on structure before drafting, so I discard far less.
Go behind the scenes of creating an AI automation guide: a split-screen workspace pairs the article draft with detailed reviewer notes, revealing a practical, iterative process of outlining, fact-checking, and refining before publication.
Podcast production is another domain where AI shines. I produce two weekly shows: I love connecting with Petra Wille on All Things Product, and talking with product teams building AI-powered products on Just Now Possible. I use Descript to edit, and I rely on Claude Code shortcuts (slash commands) to draft episode titles, descriptions, show notes, chapters, and social posts. I still own the editorial bar—no “AI slop”—but I let AI handle the heavy lifting so I can focus on shaping the final story.
Then there are tasks I fully automate. I love reading across creativity, collaboration, AI efficacy, and more. I do not love searching for relevant papers. So I don’t. Every morning, my automated research workflow finds the newest, most relevant articles and populates my digest. All I do is review.
Choosing your first AI workflows
Classic delegation advice still applies: build awareness of where your time goes; identify what you can delegate; invest your time in the work you’re uniquely equipped to do. That’s a great start for AI workflow strategy, but don’t ignore what you love doing and want to do more of. Augmentation often generates the highest returns—AI helps me go deeper, faster, without diluting my craft.
Peek inside an AI-powered curation flow: a markdown workspace compiles a 'Filtered Research Digest' with criteria, paper counts, and summaries, demonstrating how automation turns raw literature into actionable insights.
To uncover opportunities, I simply ask, over and over: Can AI help with this? As you go about your work today, keep asking yourself: How can AI help with this?
Evaluating if a task is a good candidate for an AI workflow
Through trial and error, I now run new tasks through a quick filter:
• Is this a one-time task or do I do it often?
A clean, workshop-style slide asks the pivotal question: "How can AI help with this?" Use it to spark automation ideas, map steps, and decide where generative AI can accelerate research, drafting, analysis, and repetitive work.
• Do I enjoy doing this task or would I give it to someone else if I could?
• How complex is the task?
• Can I articulate how I would do the task step-by-step?
• Does completing the task require my human judgment?
• Can I define what "done successfully" looks like?
• How much risk is there if the task is not done well?
This checklist takes minutes and pays off quickly. The answers tell me whether to automate, augment, or keep a task human-only for now—and they guide how much process and guardrailing to build around each workflow.
From here, I’ll walk through how to answer these questions in practice, how the answers map to different levels of automation or augmentation, and how I prioritize which workflows to invest in. I’ll also share 41 of my own AI workflows (noting which are automated versus augmented) plus 9 discovery-related workflows currently in development so you can steal shamelessly and ship your first one today.
The rest of this article requires a paid subscription. This publication is reader-supported. If you’ve benefited from my writing, please subscribe today.
I recently revisited a topic I once pushed back on: using AI to analyze (and maybe even synthesize) customer interviews. After six months of real-world experiments and countless conversations with seasoned product leaders, I’ve evolved my perspective. There is meaningful value here—but only when we’re clear about where AI helps and where it quietly erodes the hard-won customer understanding that powers great product decisions.
If you want to experience the conversation that sparked this reflection, you can listen to the episode on Spotify or Apple Podcast, and watch the discussion here: YouTube. It’s a candid, practical exploration of AI’s role in continuous discovery, and it mirrors what I’m seeing on the ground with product trios and empowered product teams.
Here’s the crux: AI raises the floor for beginners but accelerates experts even more. That matches my experience—early-career PMs get structure, momentum, and a confidence boost, while experienced interviewers can move faster without sacrificing nuance. But there’s a catch. If your interviewing skills aren’t solid yet, AI can create a veneer of insight that masks shallow understanding. In other words, it can help you go wrong more efficiently.
The conversation makes an important distinction between analysis and synthesis. Analysis is about extracting signals from the interview. Synthesis is about building meaning—connecting patterns, weighing contradictions, and deciding what to do next. AI can speed up the former with summaries and highlights. The latter—true synthesis—still demands expert judgment, context, and empathy.
One line from the episode stuck with me: your unpolished interview skills matter more than any shiny new AI workflow. I’ve felt that firsthand. When interview quality is uneven, dropping transcripts into an LLM won’t save you. You still need to synthesize every interview individually so the signals remain traceable and credible. That discipline keeps teams aligned, prevents overfitting to noise, and builds the organizational memory that fuels better bets.
We also explored the operational reality most teams face: interviews pile up. Backlogs grow. Leaders want speed. This is where “expert + AI” shines. With the right prompts, templates, and context, tools like ChatGPT and Claude can help transform raw transcripts into structured artifacts you can trust—provided a strong interviewer sets the frame and makes the calls. That balance preserves both velocity and quality.
What changed my mind most was the evidence from experiments—running sets of interviews through different LLMs and comparing outcomes. The patterns were consistent: beginner + AI is usually better than nothing, but the real performance gains come from expert + AI. When experts guide the process, AI becomes an accelerant rather than a crutch.
A favorite story in the episode takes a detour into building a gaming PC—an unexpected but perfect metaphor for AI’s limits. You can get great step-by-step guidance from a model, but when context shifts or edge cases appear, expertise is what keeps you from making expensive mistakes. Customer interviews are like that. Empathy comes from human interaction; AI can’t replace the experience of talking directly to your customers.
My practical guidance for teams integrating AI into continuous discovery: start with interviewing fundamentals, separate analysis from synthesis, and standardize how you capture single-interview learnings. If you need a tight template for this, refer to “The Interview Snapshot: How to Synthesize and Share What You Learned from a Single Customer Interview.” Use AI for summaries, clustering, and draft artifacts—but have an expert finalize the narratives, evaluate trade-offs, and document assumptions.
If you’re scaling this across an organization, invest in training first, then in workflows. Build a lightweight operating system for discovery: consistent interview guides, “story-based” techniques, and a shared library of prompts. Consider resources like “The Interview Coach,” as well as practical write-ups such as “Customer Interview Analysis: Where AI Helps and Hurts.” These help teams avoid common pitfalls and make better use of AI in high-judgment moments.
My bottom line: AI isn’t magic. It can help, but only if your interviews are strong and you provide the right context. Customer understanding is a competitive moat; outsourcing it entirely will cost you in the long run. Use AI to accelerate—not replace—the human judgment that makes product discovery work.
Resources and links worth exploring: ChatGPT, Claude, The Interview Snapshot: How to Synthesize and Share What You Learned from a Single Customer Interview, The Interview Coach, and Customer Interview Analysis: Where AI Helps and Hurts.
I’d love to hear how your team is using AI in discovery. What’s working, what’s risky, and where do you draw the line between automation and judgment? Share your experiences in the comments—our community learns faster when we compare notes.
When I push our organization to adopt the product operating model, I’m emphasizing a foundational shift—from “shipping roadmaps of features (output)” to solving real customer and business problems, measured by “business results (outcomes)”. That’s the difference between activity and impact, and it’s the only way to build durable value at scale.
This change inevitably reaches beyond the product organization. It reshapes how company stakeholders in Sales, Marketing, Customer Success, Finance, Legal, Security, and Operations engage with product teams, and it reframes what they expect from us. Instead of asking, “When will feature X ship?” they learn to ask, “How will we move the outcome that matters?”
In practice, the product operating model is a contract: product teams commit to outcomes, and stakeholders commit to partnership. That partnership means we co-own the problem, align on evidence, and share accountability for results. The reward is clarity—everyone sees how their work ladders to strategy and why the sequence of work makes sense.
Here’s how I align stakeholders around this model. First, I ground everything in outcomes vs output OKRs. We replace feature roadmaps with a clear strategy, prioritized problems, and measurable objectives. Our product roadmapping and sprint planning then serve the objectives—not the other way around—so capacity is allocated to the highest-leverage bets.
Second, I build empowered product teams around product trios (product, design, engineering). We practice continuous discovery with stakeholders: we share opportunity trees, test riskiest assumptions early, and bring partners into research when it informs go-to-market strategy, pricing, or enablement. This keeps us honest and avoids late-stage surprises.
Third, I establish operating rhythms that make outcomes visible. Monthly stakeholder reviews focus on progress toward objectives and what we’re learning—not status theater. Quarterly, we connect OKRs to business performance so leaders can see the throughline from discovery and delivery to pipeline, retention, or margin. If priorities shift, we renegotiate objectives explicitly.
Fourth, I define metrics that stakeholders trust. We use a balanced set of leading indicators (activation, engagement, cycle time) and lagging indicators (revenue, retention, unit economics). We socialize definitions early so no one debates the scoreboard mid-game. The result: faster decisions and less “data whiplash.”
Fifth, I invest in change management. Moving from outputs to outcomes can feel threatening if your success has historically been measured by launch volume or roadmap commitments. I address this head-on with training, transparent comms, and clear decision rights. The message is simple: outcomes create more autonomy for empowered product teams and more predictability for stakeholders.
At HighLevel, this approach has been especially powerful when cross-functional dependencies are high. For example, when we set an objective to improve user activation for a new CRM integration, we didn’t promise a bundle of features. We committed to a measurable lift in activation and a shorter time-to-value, co-owned with Customer Success and Marketing. That alignment unlocked smarter experiments, tighter enablement, and a more credible launch narrative.
The anti-patterns are predictable: treating OKRs as a renaming of the roadmap, equating discovery with indecision, or isolating product decisions from go-to-market strategy. The cure is equally consistent: bring stakeholders into discovery, attach every bet to an objective, and show progress with evidence—not just demos.
Ultimately, the product operating model is a leadership choice. It asks us to trade certainty theater for learning velocity, and feature checklists for business impact. When stakeholders see that shift pay off—in faster cycles, clearer priorities, and results that matter—support for the model moves from compliance to conviction.
