I look for product marketing leaders who translate market noise into clear decisions that move roadmap, revenue, and relationships. In that context, Darshil Gandhi exemplifies how competitive rigor and technical depth can sharpen product strategy and accelerate go-to-market strategy across empowered product teams.
Darshil leads competitive intelligence, partner product marketing and technical marketing at Amplitude. He is a former solutions engineering team principal.
That blend matters: a solutions engineering mindset grounds messaging in real implementation details, while competitive intelligence and partner product marketing align product positioning, points of parity, and competitive differentiation with what buyers actually evaluate. At a company centered on Amplitude analytics, that cross-functional view helps transform behavioral data into a crisp value proposition customers can feel in evaluations and expansions.
In practice, I prioritize a few patterns when partnering with leaders who span these domains: align on a single competitive narrative using driver trees that connect capabilities to outcomes; use Amplitude analytics to validate claims and win themes; co-create partner playbooks that make integrations repeatable; and ensure technical marketing closes the loop by pressure-testing demos, docs-as-code, and reference architectures with field feedback. This strengthens stakeholder management across sales, solutions engineering, and product trios, reducing ambiguity and speeding decisions.
The net effect is clarity: sharper differentiation in the field, cleaner handoffs between teams, and faster feedback cycles that de-risk launches. It’s a model I trust when stakes are high—use the truth of implementation to tell a compelling story, then let the market confirm it.
Inspired by this post on Amplitude – Perspectives.
I’m celebrating the five-year anniversary of Continuous Discovery Habits by inviting you to read it with me this June. As someone who leads product management and coaches product trios, I’ve seen how a shared discovery practice tightens alignment, speeds up learning, and drives outcomes. This month, we’ll go deep on prioritizing opportunities—not solutions—and I’ll guide you step by step so you can apply the ideas on your own team.
Each month, I’m releasing an in-depth reading guide that includes:
We’ll discuss each month’s reading in the comments, and we’ll gather quarterly on a live call to unpack real-world applications, trade wins and missteps, and keep the momentum going.
Joining late? No problem. I monitor the comments on each reading guide throughout the year. Start with the current month or go back to January—whatever works for you. Ask for help, share what’s working, and connect with other readers at any point.
If you want to participate, grab a copy of the book (or dust off your old copy), share the “Spread the Love” videos with your team, block time for the exercises, and register for the community sessions. Let’s do this.
This Month’s Reading
Chapter:
Estimated reading time: ~16 minutes
This month's chapter will introduce you to:
Need a copy? Grab the book
Share the Love with Friends and Colleagues
We learn best in community. Use these short videos to spread the key ideas across your product trios, engineering partners, and stakeholders. Invite them to read along with you so your discovery cadence—and your product strategy—advance together.
Reflect & Discuss What You Read
When we reflect and discuss what we read, we absorb more and apply it faster. This chapter challenges a deeply ingrained habit: prioritizing solutions. I’ve been in those meetings—spreadsheets full of features, heated roadmap debates, and a creeping sense that we’re optimizing outputs rather than outcomes. The shift to opportunity-first thinking changed how my teams frame bets, sequence discovery, and communicate product strategy.
Individual Reflection
Team Discussion
Put It Into Practice
This month is all about shifting from solution-first to opportunity-first thinking. These short, focused exercises will help your product trio practice opportunity prioritization and improve decision speed without sacrificing product discovery rigor.
Exercise: Map Your Roadmap to Opportunities
Time: 45 minutesDo this: With your product trio
Take your current roadmap or backlog and work backwards. For each planned feature or solution:
This exercise often reveals that you're either:
Use these insights to inform your next prioritization conversation.
Exercise: Practice Two-Way Door Thinking
Time: 30 minutesDo this: With your product trio
Choose 3-5 recent or upcoming product decisions. For each one, discuss:
The goal is to calibrate your team's decision-making speed. Two-way door decisions should be made quickly with "just enough" evidence. One-way door decisions deserve more deliberation and data.
Go Deeper: Additional Reading
If you prefer an audio summary of this month’s reading, including the book chapters and the following resources, I’ve included an audio version for members at the bottom of this post.
Related In-Depth Guides
Supplementary Reading
Related Courses
Our Live Discussion Schedule
Our live discussion sessions are for registered members. Sessions are not recorded. Invitations will go out two weeks before the scheduled event—reserve time now.
Audio Summary
Prefer to listen? Stream the audio overview here: June — Prioritizing Opportunities (audio).
Ready to put continuous discovery into action? Grab the book, share the videos with your team, schedule the exercises, and join the community sessions. Opportunity-first product strategy is a muscle we can build together.
The chapters we will be readingA preview of the most important concepts we'll be learning aboutShort videos you can share with friends and colleagues to help spread the ideasIndividual and team discussion questions to help you absorb and engage with the readingTeam exercises to help you put the ideas into practiceAdditional reading to help you go deeper on the core ideasChapter 7: Prioritizing Opportunities, Not SolutionsWhy product strategy happens in the opportunity space, not the solution spaceHow to focus on one target opportunity at a time to deliver value iterativelyUsing the tree structure to simplify prioritization decisionsThe four criteria for assessing opportunities: sizing, market factors, company factors, and customer factorsWhy treating prioritization as a messy, subjective decision leads to better outcomes than scoring formulasThe concept of two-way door decisions and how they apply to opportunity prioritizationWork on one small opportunity at a time – Reduce your batch sizeGetting started with compare and contrast decisions – Choose the right target opportunityTurn big intractable problems into smaller, more solvable problems – The power of decompositionThink about your team's current roadmap or backlog. How much of your time is spent prioritizing features versus understanding and prioritizing customer opportunities? What would change if you flipped that ratio?Reflect on the last time you made a product decision. Did you treat it as a one-way door (irreversible) or a two-way door (reversible)? How did that framing affect your decision-making process and timeline?Consider the four assessment criteria (opportunity sizing, market factors, company factors, customer factors). Which of these does your team currently emphasize most? Which do you tend to overlook or underweight?As a team, list the top 5-10 items on your current roadmap or backlog. For each one, try to identify the underlying customer opportunity it addresses. If you can't clearly articulate the opportunity, what does that tell you about how you're making decisions?The chapter argues against scoring formulas (like RICE or ICE) for prioritization, calling them "made-up math." If your team uses a scoring system, discuss: What is it really measuring? Does it help you make better decisions, or does it just make subjective decisions feel more objective?Walk through a recent prioritization decision. Did you assess options in isolation ("should we build this?") or compare and contrast them? How might your decision have been different with a compare-and-contrast approach?Identify the customer opportunity it's meant to addressWrite it as something a customer might say (e.g., "I can't find anything to watch" not "We need better search")Look for patterns: Are multiple solutions addressing the same opportunity? Are some solutions disconnected from any clear customer need?Spreading yourself thin across too many opportunitiesOver-investing in a single opportunity with multiple solutionsBuilding solutions with no clear opportunity attachedIs this a one-way door decision (hard to reverse) or a two-way door decision (easy to reverse)?If it's a two-way door, what's the smallest step we could take to learn whether we're on the right track?What would we need to see to know we made the wrong choice?If we realize we're wrong, how quickly could we course-correct?Opportunity Solution Trees: Visualize Your Discovery to Stay Aligned and Drive OutcomesCustomer Interviews: Uncover Hidden Insights from Every ConversationPrioritize Opportunities, Not Solutions7 Key Benefits of Using Opportunity Solution TreesProduct in Practice: How 2-Way Door Decisions Helped Simply Business Learn FastProduct in Practice: Getting Started with Opportunity Solution Trees at SuperAwesomeProduct Discovery Fundamentals: Learn a structured and sustainable approach to continuous discovery.Tuesday, June 16, 2026: 9am-10am PDTThursday, September 17, 2026: 9am-10am PDTWednesday, December 16, 2026: 9am-10am PST
I keep meeting talented product teams who can demo impressive proof-of-concepts but can’t get durable business impact into production. The difference isn’t raw ingenuity—it’s the operating model. As I’ve scaled AI initiatives in my own organization, one sentence has proven painfully accurate: "What the top 1% of AI-native product teams are doing differently – and why most won't catch up without rebuilding the operating model."
When I say “AI operating model,” I mean the end-to-end way we set strategy, discover value, build, ship, govern, and learn—specifically adapted for AI systems. If we try to bolt AI onto a classic software cadence, we stall. If we rebuild our operating model around AI’s unique constraints and compounding advantages, we accelerate.
It starts with strategy. I anchor our portfolio to explicit outcomes, not features—tying every initiative to measurable customer and commercial impact. Driver trees and an opportunity solution tree make tradeoffs transparent, while outcomes vs output OKRs prevent us from celebrating activity over results. This is how empowered product teams earn autonomy without losing alignment on the AI Strategy.
Next is discovery. Continuous discovery reframes “can we ship a model?” into “can we change a behavior or decision with acceptable risk?” I pair customer interviews with in-product telemetry and journey mapping to qualify moments of high value and high frequency. The litmus test: can we describe the target workflow in plain language and simulate success before training models? If not, we’re not ready.
Data foundations come third. A retrieval-first pipeline is now my default, not an afterthought. We invest in data governance, privacy-by-design, and observability so we can explain where answers come from, prove consent, and debug drift. Without trustworthy data and clear lineage, every downstream AI promise is fragile—and your AI readiness is mostly theater.
Then I insist on eval-driven development. Before we optimize prompts or tune models, we define offline and online evals that represent the real task, including safety and “gotcha” cases. We treat prompt engineering, context window management, and agentic AI patterns as hypotheses that must beat a baseline under repeatable tests. This moves debate from opinions to evidence.
Shipping is where most teams quietly stall. We integrate AI into our CI/CD with feature flags, shadow modes, and progressive rollouts, building MLOps into the same platform that runs our services. I watch DORA metrics to keep delivery velocity healthy, but I also watch AI-specific signals—input distribution shifts, response variance, and time-to-mitigation—so we catch regressions before customers do. Platform scalability matters more when inference costs and latency can spike overnight.
Governance isn’t a gate at the end; it’s a runway from the start. We operationalize AI risk management with tiered reviews, model and data cards, and clear escalation paths. The goal is not to slow down, but to reduce surprise—so product managers, engineers, and legal share the same playbook for safety, fairness, and regulatory compliance.
Value capture closes the loop. We connect product metrics to commercial levers like Net Recurring Revenue (NRR) and retention analysis, then shape packaging so customers pay for outcomes, not raw compute. This is where product-led growth meets sales-led growth: we demonstrate value in-product, then arm go-to-market teams with unambiguous proof.
So why are 80% of teams stuck? Three patterns recur: technology FOMO masquerading as strategy, fragmented data that can’t support high-quality retrieval, and a lack of evals that forces decisions by vibes. Add ad hoc governance and you get pilots that impress in slides but wither under real-world variance.
How do the top 1% think differently? They rebuild the operating model first. They position discovery around workflows, not models. They invest in retrieval-first architectures early. They standardize evals. They ship with guardrails. And they treat “learning per week” as a sacred metric—because compounding insight beats sporadic heroics.
If you need a 90-day plan, here’s the sequence I use. Week 1–2: run a content audit of data sources and map the top five repeatable workflows ripe for AI leverage. Week 3–4: define success metrics and offline evals for one beachhead use case. Week 5–8: build the retrieval pipeline, implement prompt baselines, and instrument observability. Week 9–12: ship behind feature flags, run A/B testing with safety thresholds, and iterate on failure cases. By the end, you’ll have a reusable blueprint—not just a demo.
Team design matters. I staff product trios (PM, design, tech lead) with forward deployed engineers or solutions engineering partners who sit with customers. That proximity reduces spec ambiguity and accelerates learning. It also sharpens our product roadmapping and sprint planning because we plan against outcomes, not outputs.
The hardest part is emotional, not technical: letting go of familiar software rituals that don’t serve AI. Once we accept that AI demands a different operating rhythm, progress feels lighter. The top 1% don’t have secret models; they have disciplined systems. Rebuild yours, and the compounding benefits will outpace any single model upgrade.
I’ve learned that customers don’t just buy features—they buy the way we discover, decide, build, ship, and support. In other words, the operating model is the product. That realization has shaped how my team and I at HighLevel translate product strategy into tangible, repeatable outcomes that show up in quality, reliability, onboarding, and consultative support every single day.
We created Product Partners to codify that operating model and scale it with discipline. It’s a blueprint and operating rhythm that unifies product strategy with go-to-market strategy, customer success, and solutions engineering—so empowered product teams can move faster without sacrificing clarity, governance, or customer trust.
First, we anchored on continuous discovery. Product trios work shoulder-to-shoulder with customer-facing teams to run customer interviews, journey mapping, and A/B testing, then validate insights with session replay and behavioral analytics. We use driver trees and opportunity solution trees to connect problems to outcomes, ensuring prioritization is evidence-based and aligned to product-market fit—not just output.
Second, we elevated delivery excellence. Our practices emphasize CI/CD, feature flags, observability, SRE-informed incident management, and DORA metrics to shorten feedback loops while raising the bar on stability. Privacy-by-design, data governance, and regulatory compliance are built into our workflows, and we make deliberate build vs buy decisions to protect platform scalability and long-term velocity.
Third, we integrated go-to-market alignment from day one. Solutions engineering and customer success shape requirements early, so launches include in-app guides, product tours, onboarding paths, and consultative support that accelerate user activation. We tie outcomes vs output OKRs to stakeholder management rituals, ensuring sales-led and product-led growth motions reinforce each other instead of competing for focus.
Finally, we closed the loop with a unified analytics platform. Activation, retention analysis, and Net Recurring Revenue (NRR) sit alongside qualitative signals from customer interviews and support. This single source of truth helps us refine product positioning, sharpen value propositions, and improve roadmapping and sprint planning with clear, testable hypotheses.
What does this mean for our partners and customers? Faster time-to-value, fewer handoffs, clearer expectations, and a shared lens on the metrics that matter. Product Partners isn’t a side program; it’s how we operationalize trust—through transparency, consistent rituals, and a bias toward learning that compounds.
If this resonates, you’ll feel it in how we discover, build, and support together. I’ll continue to share our playbooks—covering continuous discovery, onboarding, and outcome-based planning—so we can keep raising the standard for product management leadership and product-led growth, one operating rhythm at a time.
I recently spent time with the debate behind the "product builder" trend—asking whether it’s the future of product management or just another wave of tech FOMO. The conversation featuring Teresa Torres and Petra Wille is a useful prompt, but what matters most is how we translate these ideas into healthy product practices inside our own organizations.
Here’s my take: the product builder movement is neither a mandate nor a fad—it’s a tool. The right question isn’t "should product managers code?" but whether leaning into building advances outcomes for our customers and our teams. In practice, that means letting interest and skill—not pressure—set the pace.
Petra captured it perfectly: "Just because I can do it — is it something I enjoy doing? And do I have enough experience to really get into the flow?" Those two tests—joy and depth—are underrated filters. I’ve seen PMs light up when prototyping or vibe coding a thin slice, and I’ve also seen well-meaning dabbling create hidden complexity that slows everyone down later.
Org design determines whether this works. It’s not about the tools—it’s about clarity of roles, healthy interfaces between product, design, and engineering, and explicit guardrails for where experiments stop and production begins. AI has raised the stakes: "AI can make unskilled work look polished. That’s a feature and a bug — executives see the shine, engineers inherit the mess." If you’ve ever watched a glossy demo turn into weeks of refactors, you know exactly what this looks like.
To avoid that trap, I deliberately separate the three layers where AI is changing product work: personal productivity, team process, and product strategy. Treating these as different stacks keeps expectations clean: a prompt that accelerates personal workflows isn’t the same as an AI-enhanced process that reshapes delivery, and neither automatically produces durable product advantage. Don’t conflate them.
Discovery remains stubbornly human. "Why discovery still requires talking to your customers (sorry)" is more than a friendly nudge. AI can broaden our search space and sharpen analysis, but it doesn’t replace qualitative conversations or the judgment that comes from pattern recognition across real customer contexts. Continuous discovery and disciplined customer interviews are still the most reliable compasses we have.
Where does "vibe coding" fit? It’s great for roughing out concepts, de-risking slices, and communicating intent when words or static mocks won’t cut it. Tools like Claude Code make this faster than ever, and familiar stacks like Ruby on Rails lower the bar for spinning up functional prototypes. But remember the design system trap: AI can make bad decisions look good on the surface. If you don’t control for architecture, accessibility, data contracts, and handoff quality, your team pays the integration tax later.
In well-set-up orgs, the output-oriented muscle memory gets rewired. When AI frees up time, strong teams reinvest it into better problem framing, sharper opportunity solution trees, and tighter product strategy—rather than simply chasing more output. That’s a leadership challenge, not a tooling problem, and it shows up quickly in how teams make trade-offs.
Here’s how I operationalize this with empowered product teams: we articulate clear boundaries for prototypes versus shippable code, define decision rights for when PMs or designers "build," and align on review gates that protect quality without stifling speed. We also make the three AI layers explicit in roadmapping and retros, so improvements to personal workflows don’t get mistaken for strategic advantage.
My distilled guidance echoes the episode’s throughline. The product builder trend isn’t a mandate — it’s a tool. Let enjoyment and skill guide who on your team leans into it. Organizational readiness determines whether AI empowers your team or creates chaos. Don’t conflate personal efficiency, process change, and product impact—they require different responses. Discovery fundamentals haven’t changed; AI helps you go deeper, not skip the work. And the real takeaway on product builders: not everyone has to build, but everyone can if they want to.
If you want to hear the full discussion that sparked these reflections, listen on Spotify or Apple Podcasts. Then tell me: where will you apply builder energy in your team—and where will you deliberately say no?
Resources & Links: Follow Teresa Torres: https://ProductTalk.org. Follow Petra Wille: https://Petra-Wille.com. Mentioned in this episode: Claude Code, Vibe coding, Ruby on Rails.
One more quote I loved because it centers autonomy and craft: "It’s a tool in our toolbox. We can decide who on our team has fun with it, wants to do it, wants to contribute." That’s the mindset that sustains both momentum and morale.
I just finished listening to "Taste – All Things Product Podcast with Teresa Torres & Petra Wille," and as a product leader shipping AI-powered capabilities at HighLevel, Inc., I wanted to pressure-test the sudden obsession with "taste."
If you're curious, you can listen to this episode on Spotify or Apple Podcasts.
The core question landed perfectly for our moment: Is "taste" the must-have skill of the AI era — or just the latest tech buzzword in a world where AI is eating through design, delivery, and discovery?
Teresa pushes back hard, highlighting how slippery the term can be. "It's just this month's flavor of founder mode." She points out that "taste" is rarely defined, can't be easily taught, and too often becomes shorthand for "my preference trumps yours." Just as importantly, "It's not about your taste. It's about your customer's taste."
Petra adds needed nuance from years in the craft: pattern-recognition is real, and some people do develop sharper product sense over time. As she put it, "I am a strong believer that you develop product sense and taste over time. It's never finished."
Both threads lead back to familiar roots in product: product sense, founder mode, and the enduring myth of the lone visionary. They even grapple with the big question on everyone’s mind—Will AI Eat Taste Too?—and where that leaves product teams navigating GenAI, LLMs for product managers, and evolving product strategy.
Here’s my take. "Taste" can be useful as a personal north star, but it is not a decision system. In my teams, we bias toward evidence: continuous discovery, customer interviews, discovery synthesis with opportunity solution trees, and tight collaboration in product trios. Opinion can start the conversation, but evidence should end it.
Practically, that means investing in the skills that compound: Discovery skills — understanding customers, matching solutions to real needs. Human-to-human interaction skills. Learning to collaborate with AI effectively. Critical thinking and judgment grounded in evidence.
On AI collaboration specifically, we treat GenAI as a force multiplier, not a decider. We prototype with AI to explore breadth, then narrow with qualitative and quantitative signals, ablation-style experiments, and clear success criteria. The bar I hold myself to is simple: taste without evidence is just opinion.
Three lines I underlined from the conversation:
"It's just this month's flavor of founder mode." — Teresa Torres
"It's not about your taste. It's about your customer's taste." — Teresa Torres
"I am a strong believer that you develop product sense and taste over time. It's never finished." — Petra Wille
If you want to go deeper, these references are helpful for sharpening judgment without falling into the "great man" theory trap.
Follow Teresa Torres: https://ProductTalk.org
Follow Petra Wille: https://Petra-Wille.com
Founder mode
Marty Cagan: Founder-Style Leadership
Vercel/v0 CEO Guillermo Rauch on building taste: from Lenny Rachitsky’s Linkedin post
Continuous discovery (Read Teresa’s Everyone Can Do Continuous Discovery—Even You! Here’s How
The "great man" theory
Steve Jobs and the myth of the lone product visionary
Have thoughts on this episode? Leave a comment below and share how your team balances product sense with evidence in the age of AI.
Five years in, Continuous Discovery Habits continues to be one of the most practical frameworks I use to align empowered product teams, sharpen product strategy, and convert customer interviews into outcomes. To celebrate its impact, I’m hosting a community read-along and inviting you to dig in with me this May.
Each month, I’m releasing an in-depth reading guide to make learning stick. You’ll find the chapters we’ll be reading, a preview of the essential concepts, short videos to help you spread the ideas across your organization, individual and team discussion prompts, team exercises to put the concepts into practice, and additional reading if you want to go deeper. My goal is simple: help you turn product discovery into a steady habit, not a once-a-quarter activity.
We’ll discuss each month’s reading in the comments, and we’ll gather quarterly on a live call to compare notes and share what’s working. Joining late is absolutely fine—I monitor the conversation throughout the year. Start with the current month or rewind to January; you can ask for help, share wins and roadblocks, and connect with other readers anytime.
If you want to participate, grab a copy of the book (or dust off your old one), share the "Spread the Love" videos with your team, block focused time for the exercises, and register for the community sessions. Let’s do this together.
This Month’s Reading
Chapter: Chapter 6: Mapping the Opportunity Space
Estimated reading time: ~23 minutes
This month’s chapter will introduce you to why opportunity mapping is critical for structuring the ill-structured problem of reaching your desired outcome; how to move from overwhelming opportunity backlogs to well-structured opportunity spaces; the power of tree structures for depicting parent-child and sibling relationships between opportunities; how to identify distinct branches in your opportunity space using key moments in time; common anti-patterns to avoid when building your first opportunity solution tree; and why structure "gets done, undone, and redone" as you continue to learn.
Need a copy? Grab the book.
Share the Love with Friends and Colleagues
We learn best in community. Use these short videos to spread the core concepts from this chapter—then invite your team to join the book club with you.
The need for opportunity mapping – You will never fully satisfy your customers' desires
Understanding the structure of an opportunity solution tree – Depicting two types of relationships
Turn big intractable problems into smaller, more solvable problems – The power of decomposition
How to map an opportunity space – Getting started with opportunity solution trees
A well-structured opportunity space has distinct branches – Identify key moments in time
Reflect & Discuss What You Read
Reflection turns reading into capability. This chapter asks us to shift from reacting to every request to deliberately structuring the opportunity space. If you’ve ever felt overwhelmed by a never-ending backlog or pressure to ship output over outcomes, this is where the fog starts to lift. As you read, focus on how your team currently organizes (or doesn’t organize) what you hear from customers.
Individual Reflection
1) Think about your current product backlog or opportunity list. Is it a flat list, or do you have some structure to it? If you were to group similar opportunities together, what patterns would emerge?
2) When was the last time you heard a customer need and immediately jumped to a solution without exploring whether there were related opportunities? What would change if you took the time to map how that opportunity connects to others?
3) Review the anti-patterns from the chapter (opportunities framed from your company's perspective, vertical opportunities, opportunities with multiple parents, etc.). Which of these do you recognize in how your team currently talks about opportunities?
Team Discussion
1) As a team, pick a top-level opportunity you're currently working on. Try breaking it down into sub-opportunities together. Where do you struggle? Where do you disagree about how to frame or group opportunities? What does that tell you about gaps in your shared understanding?
2) Look at your experience map (from Chapter 4) and identify 3-5 distinct moments in time during your customer's experience. Could these become the top-level branches of your opportunity solution tree? Where do you see overlap, and where are there clear distinctions?
3) Discuss the quote from Barbara Tversky: "Structure gets done, undone, and redone." How does your team currently respond when you discover new information that changes how you understand the opportunity space? Do you treat your opportunity map as fixed or as something that evolves?
Put It Into Practice
Reading is step one; building your first opportunity solution tree is where the real learning happens. The exercises below are exactly how I coach product trios to transform ambiguous problems into aligned action.
Exercise: Build Your First Opportunity Solution Tree
Time: 60 minutes. Do this: With your product trio.
Start by reviewing your interview snapshots from the past few weeks. For each opportunity you captured, ask the three questions from the chapter:
Is this opportunity framed as a customer need, pain point, or desire (not a solution)?
Is this opportunity unique to one customer, or have we seen it in more than one interview?
If we address this opportunity, will it drive our desired outcome?
Then, using your experience map, identify 3-5 distinct moments in time to serve as your top-level opportunities. Group the opportunities from your interviews under these top-level branches.
Look for opportunities to add structure to each branch. Group similar opportunities together and identify a parent opportunity. Look for vertical stacks (one parent, one child) and fill in missing siblings. Reframe opportunities that are too broad or that could live in multiple branches.
Don’t aim for perfection. Get something on paper (or a digital canvas) and iterate the tree with every new interview.
Exercise: Practice Framing Opportunities from Your Customer’s Perspective
Time: 30-45 minutes. Do this: With your product trio.
Take 10-15 opportunities from your current backlog or list. For each one, ask: "Can I imagine a customer saying this?" If the answer is no, reframe it from your customer’s perspective. For example:
"Increase subscription conversions" becomes "I want to know if this product is worth paying for"
"Reduce support tickets" becomes "I can't figure out how to do X"
"Improve onboarding completion" becomes "I'm not sure what to do next"
This exercise helps you spot business-centric opportunities disguised as customer opportunities. It also trains your team to listen for opportunities in interviews that are framed from the customer’s point of view.
Go Deeper: Additional Reading
If you prefer an audio summary of this month’s reading, including the book chapters and the following resources, I’ve included an audio version for paid subscribers at the bottom of this post.
Related In-Depth Guides
Opportunity Solution Trees: Visualize Your Discovery to Stay Aligned and Drive Outcomes
Customer Interviews: Uncover Hidden Insights from Every Conversation
Supplementary Reading
Prioritize Opportunities, Not Solutions
Product in Practice: Opportunity Mapping at Grailed
Product in Practice: Opportunity Mapping at trivago
7 Key Benefits of Using Opportunity Solution Trees
Getting Started with Opportunity Solution Trees at SuperAwesome
Bringing Order to Chaos: Using Opportunity Solution Trees in Everyday Life
Other Voices
Why Groups Struggle to Solve Problems Together by Al Pittampalli
More PM Problem Areas by Marty Cagan
Five Superpowers of Diagrams by Abby Covert
Critical Thinking is Product Management by This Is Product Management
Our Live Discussion Schedule
Our live discussion sessions are for paid subscribers. Sessions are not recorded. Invitations will go out to Supporting Members and CDH Members two weeks before the scheduled event. But reserve the time on your calendar now.
Tuesday, June 16, 2026: 9am-10am PDT
Thursday, September 17, 2026: 9am-10am PDT
Wednesday, December 16, 2026: 9am-10am PST
Audio Summary
This summary was produced by NotebookLM. The sources supplied were the book chapters as well as all of the additional reading.
I used to treat the roadmap like a sacred artifact. Over time, I learned the uncomfortable truth: the best product leaders stop obsessing over the roadmap and start obsessing over ambition. My number one job isn’t shipping features—it’s raising the bar for what the team believes is possible and carving out the time to think deeply. When I spend half my time thinking (not doing), the business moves faster, customers feel the lift, and outcomes finally outpace output.
The impact of a great product leader starts with context-setting. Under a founder, the role often skews toward influence without deference—pressure-testing ideas, bringing data and customer insight, and helping translate founder vision into a portfolio and product strategy. Under a hired CEO, it’s about aligning capital allocation, setting clear investment theses, and ensuring product roadmapping and sprint planning connect directly to financial and go-to-market realities.
Ambition beats activity. I push teams beyond “what we can fit this quarter” and anchor on value creation: how does this create net-new customer advantage? We measure with outcomes vs output OKRs, tie initiatives to activation, retention, and Net Recurring Revenue (NRR), and celebrate learning velocity as much as shipping velocity. When the narrative moves from features to outcomes, customers notice—and so does the business.
I’m demanding without breeding fear. The trick is a high bar plus psychological safety: crisp quality standards, blameless postmortems, and an expectation of intellectual honesty. I separate people from problems, model curiosity over certainty, and use stakeholder management to align early, not late. The result is a culture where empowered product teams volunteer for the hard problems because the path to excellence is transparent.
Most “politics” is an incentives problem. When functions optimize for different scorecards, status games fill the vacuum. I fix this with a shared driver tree, clarified decision rights, and compensation aligned to company-wide outcomes. Once incentives match the strategy, alignment stops being a meeting and starts being momentum.
I use a three-bucket framework to delegate decisions. Bucket 1: I decide (irreversible, cross-company implications, or existential risk). Bucket 2: Team decides; I’m consulted (reversible or scoped risk with clear guardrails). Bucket 3: Team decides; I’m informed (local optimization and execution details). This creates speed without surrendering strategic coherence, and it’s a practical approach to building empowered product teams.
I’m militant with my calendar to protect thinking time. I block two to three mornings per week for deep work, partner with executive assistants to defend those blocks, and aggressively prune low-ROI rituals. “Thinking time” isn’t a luxury; it’s where product strategy is forged, complex bets are sequenced, and product-market signals get synthesized. I also fly at a low altitude—joining customer calls, reviewing designs and PRDs weekly—so judgment stays grounded without micromanaging.
The AI era demands more risk in our roadmaps. I place a few venture-like bets, timebox them, and instrument eval-driven development so we can kill or scale quickly. The concept of an app is changing—from static screens to adaptive workflows, assistants, and agentic AI. This shifts product roadmapping and sprint planning toward capabilities, data leverage, and safety systems (privacy-by-design, data governance, and AI risk management) rather than a linear feature list.
Innovation teams need shelter from the core. I separate their KPIs from immediate monetization, create technical sandboxes with clear guardrails, and run a parallel discovery track. Forward deployed engineers sit with customers; continuous discovery ensures we converge on problems worth solving; and when something works, we integrate it into the core without smothering it with legacy processes.
I use a barbell planning horizon: 12 weeks of executional clarity and 12–24 months of strategic theses. Anything beyond that is scenario planning, not a promise. We revisit the theses quarterly, tie them to product strategy and go-to-market strategy, and ensure each increment is measurable. This balances focus with optionality.
Excellence in 2026 looks different. It requires fluency in AI Strategy, strong data governance, and the ability to move from feature leadership to system leadership. Product leaders must be bilingual—equally comfortable discussing LLMs and retrieval-first pipelines as they are speaking in NRR, gross margin, and payback periods. The job is to translate technology shifts into durable customer advantage.
Being a great C-suite partner means acting enterprise-first. I co-own capital allocation with finance, sequence hiring with people and engineering, and encode our strategy into operating cadence. I treat sales-led growth and product-led growth as complementary systems, not rival religions, and I bring clarity to trade-offs with driver trees and scenario plans.
Chase impact, not titles. The fastest growth I’ve seen comes from optimizing for scope, learning rate, and mentors—not for role labels. If you want comp and career to compound, maximize the value you create: fix activation, improve retention, unlock expansion, or reduce cost-to-serve. Titles follow impact, not the other way around.
Four bottlenecks stall careers repeatedly. First, a scope ceiling—holding too much IC work and not scaling through delegation. Second, stakeholder friction—underinvesting in alignment and communication. Third, weak people leadership—not hiring, coaching, and performance-managing decisively. Fourth, fuzzy strategy—if your strategy can’t be drawn as a driver tree, your teams can’t execute it. Remove these bottlenecks and your trajectory changes fast.
In the end, the roadmap is an instrument, not the strategy. Raise the team’s ambition, align incentives, protect deep work, and take smarter AI-informed risks. Do that consistently and the roadmap stops being a crutch—it becomes a flywheel.
I’ve learned that the Principal Product Manager role is the crucible where strategy, execution, and leadership meet. It’s less about owning a backlog and more about owning an outcome—aligning a portfolio of bets to a clear vision, then guiding empowered product teams to deliver measurable impact at pace.
Unlike a Senior PM who may anchor a single area or a Group PM who often has direct people management, I operate as a force multiplier. I set product strategy, shape cross-functional operating rhythms, mentor PMs and product trios, and influence executives and partners—without relying on formal authority. The bar is outcomes over output, clarity over activity, and learning over certainty.
My first move is to define a crisp North Star and the driver tree beneath it. I translate company goals into outcomes using outcomes vs output OKRs, ensuring every roadmap item ties to a measurable lever (conversion, retention, activation, expansion). This structure prevents feature factory drift and creates a shared language for prioritization and trade-offs.
Discovery is continuous, not a phase. I run weekly customer interviews, synthesize insights with journey mapping, and map opportunities with an opportunity solution tree so teams solve the right problems before building the right solutions. I use the Kano Model to calibrate expectations on “delighters” versus “must-haves,” and I document assumptions so we can invalidate them early instead of discovering them late.
Data sharpens judgment. I rely on Amplitude analytics for behavioral analytics, retention analysis, and funnel diagnostics, pairing this with A/B testing to validate causal impact. I size experiments with minimum detectable effect (MDE) to reduce false negatives, and I instrument leading indicators to shorten feedback loops—so we can pivot weeks earlier, not quarters later.
Execution is where strategy earns its keep. I plan in outcomes-based quarters and deliver in two-week sprints, keeping a living roadmap that reflects new learning. Product trios (PM, design, engineering) co-own problem framing and solution shaping, while I maintain stakeholder management with transparent trade-offs and crisp decision records. This balance preserves autonomy while ensuring alignment.
High standards spread through coaching. I mentor PMs on writing testable bets, crafting compelling problem statements, and telling a metrics-first narrative. I champion empowered product teams because autonomy plus accountability consistently outperforms mandate-driven delivery—and because it attracts and retains top talent.
As scope scales, so does storytelling. I align leaders through a brief, repeatable operating cadence: monthly business reviews tied to driver trees, quarterly OKRs grounded in outcomes, and QBRs vs OKRs alignment to keep customer-facing teams in lockstep. I choose first principles decision making for high-ambiguity calls, and I make risks explicit early.
Go-to-market is part of product, not an afterthought. I partner with marketing and customer success to craft value propositions, then validate them in-product with in-app guides and product tours. We define user activation precisely, instrument it, and iterate messaging and onboarding until time-to-value collapses. This is how product-led growth compounds.
Technical excellence reduces product risk. I advocate for feature flags to decouple release from launch, CI/CD to increase deployment frequency, and observability to catch regressions fast. These practices make experimentation cheaper and safer, which in turn makes bold bets possible.
My 30-60-90 framework is simple. In 30 days, clarify outcomes, baselines, and constraints; in 60, run discovery sprints and ship the first experiments; in 90, land two to three measurable wins, prune low-signal bets, and scale the operating cadence. The goal is momentum with meaning—evidence, not theater.
At HighLevel, I’ve seen that the Principal Product Manager unlocks leverage by combining strategic clarity with disciplined learning and empathetic leadership. When we align on outcomes, instrument for truth, and empower teams, we don’t just ship features—we shift the trajectory of the business.
Inspired by this post on Amplitude – Best Practices.
When uncertainty spikes, I notice many organizations snap back to "Command and control." It feels fast, safe, and decisive—especially when the stakes are high. But in product management leadership, speed without shared context is often an illusion, and control without trust rarely scales. I’ve learned that what looks like strength from the top can quietly create bottlenecks, missed signals, and disengaged teams.
Why do smart companies revert in tough times? Familiarity. Centralizing decisions can reduce short-term cognitive load and signal clarity. Yet the cost shows up quickly: leaders become single-threaded on context they cannot possibly hold, and teams spend cycles asking for permission rather than creating value. The result is slower learning and weaker product strategy just when continuous discovery and iteration matter most.
Here’s the hard truth: no single leader can hold all the context required to make every decision in a modern, cross-functional environment. The hidden complexity of customer segments, technical debt, data signals, and go-to-market constraints outstrips any one person’s bandwidth. That’s why empowered product teams, staffed with domain experts, outperform command centers—provided they’re aligned on outcomes and guardrails.
I like the burning house analogy: in a true emergency, crisp direction helps—"take the stairs, not the elevator"—because the problem is clear, the time horizon is short, and the action is obvious. But most product work is not a single burning house; it’s a city with evolving fire codes, shifting weather, and neighborhoods that look different block to block. In that environment, distributed action scales better than centralized control.
Strong leadership is not the same as command-and-control. In practice, it means setting a compelling direction, defining guardrails, and running tight feedback loops. I aim for what I call the "Flotilla of kayaks": we’re all headed to the same lighthouse, but each kayak navigates its own currents based on local information. That’s aligned autonomy—fast, resilient, and deeply accountable.
People often ask why some command-and-control companies still succeed. My view: beneath the surface, there’s usually more trust and unofficial autonomy than their org charts suggest. Teams earn freedom by shipping reliably, sharing decision rationales, and showing outcomes. Leaders tolerate—and even quietly endorse—those pockets of autonomy because they see the results.
It’s a spectrum, not a binary. I flex my style based on risk, reversibility, and time horizon—what I’d call spectrum thinking. Early in a bet, or when risks are existential, I raise the altitude and tighten the cadence. As confidence builds, I widen autonomy and shift the team to outcomes over outputs. Beware "Founder mode" when it drifts from vision-setting into day-to-day decision vetoes; it’s intoxicating early and suffocating at scale.
On decision-making, I prefer a simple principle: let the person with the most relevant expertise decide, while incorporating the right input. That’s "Consultative decision-making" in practice. In some regions, you’ll hear it called "Konsultativer Einzelentscheid." The point is to seek counsel without defaulting to consensus that bogs down speed. One person owns the call, and everyone commits to the decision once it’s made.
Practically, here’s what works for my teams: we clarify decision rights up front, draft pre-reads with clear options and risks, involve the smallest set of stakeholders required, and document the decision and expected signals ahead of time. Product trios keep discovery tight with design and engineering, while stakeholder management focuses on context, not sign-offs. We track outcomes vs output OKRs and hold regular decision reviews so we can reverse or double down fast.
My key takeaways are consistent: "Command and control" can feel efficient, but it doesn’t scale in complex environments. No leader can hold all the context. Strong leadership is about direction, guardrails, and feedback loops—not control. High-performing teams balance autonomy with alignment. Decision-making should sit with the person closest to the problem, supported by the right input and transparent reasoning. Trust is built and earned over time—and it changes how teams operate.
Reflection prompts I use with my leads: Where does your team sit on the command-and-control ↔ autonomy spectrum? Are the highest-context people truly making the decisions? What would it take to increase trust and autonomy—better instrumentation, clearer guardrails, or tighter cadences? Which calls require consensus, and which deserve a decisive, single-threaded owner?
If you’re wrestling with speed, alignment, and autonomy in your organization, start small: pilot "Consultative decision-making" on one consequential decision, set explicit guardrails, and measure the outcome. You may be surprised how quickly aligned autonomy compounds into better product discovery, sharper product strategy, and stronger execution.
In the age of AI, I’ve come to believe we’re all builders—yet not all building is the same. There is a very meaningful difference between building to learn (known as product discovery) versus building to earn (known as product delivery). When we confuse the two, we waste precious time, budget, and team energy on output over outcomes. My goal in this FAQ-style reflection is to clarify when and how to choose each mode so we can make smarter, faster, more confident product decisions.
Why does this distinction matter so much right now? Because as the cost of product delivery continues to drop, the scarce resource shifts from shipping capacity to clarity of problem, solution, and value. Cloud infrastructure, CI/CD, feature flags, and even gen AI code assistance have made it cheaper to launch. That’s great—but if we don’t learn the right things before we scale, we’ll efficiently deliver the wrong product. Discovery is how we de-risk that.
What do I mean by build to learn? I use discovery to quickly validate problems, test value, and shape solutions before committing delivery teams to scale. In practice, that means continuous discovery with customer interviews, rapid prototyping, and lightweight experiments that put us in front of real users fast. I rely on product trios and empowered product teams to co-own outcomes, not just output, and I anchor decisions with outcomes vs output OKRs so we stay focused on measurable impact.
How do I structure discovery sprints? I start with an opportunity solution tree to map customer pain points and candidate solutions, then select the smallest test that can invalidate a risky assumption. When signals are ambiguous, I refine the questions and instrument better learning loops rather than pushing harder on delivery. For experiments, I keep a bias to speed: clickable prototypes, concierge tests, or gen ai for product prototyping often reveal more in days than a coded MVP does in weeks. When experiments go live, I use a clear minimum detectable effect (MDE) and resist reading noise as signal.
Where does AI change the calculus? LLMs for product managers are turbocharging discovery by accelerating research synthesis, persona drafts, and early concept validation. I pair that with eval-driven development to set crisp acceptance criteria for AI behaviors before any production integration. Prompt engineering and conversation design are part of the toolkit, but the same rule applies: prototype to learn, not to impress. AI can make bad ideas cheaper to build—so disciplined discovery matters more than ever.
So when do I switch to build to earn? Once I have evidence of value and feasibility, I shift into product delivery to scale with quality, security, and reliability. This is where I bring in product roadmapping and sprint planning, DORA metrics to monitor deployment frequency and lead time, and strong SRE and observability practices to safeguard the user experience. The handoff isn’t a wall; discovery continues inside delivery to refine scope, reduce risk, and maintain momentum.
What pitfalls do I watch for? The biggest is treating delivery as discovery—shipping features to “see what happens” without a clear learning thesis. Another is tech-first decisions driven by technology FOMO instead of product strategy and customer value. I also see teams set output-based commitments that crowd out learning; outcomes vs output OKRs keep us honest. And when considering build vs buy, I evaluate whether the capability differentiates us; if not, I’ll buy to preserve discovery capacity on what truly matters.
My operating conviction is simple: invest early and deliberately in build to learn so build to earn becomes high-confidence, high-velocity, and high-impact. In practical terms, that means smaller bets, faster feedback, clearer outcomes, and tighter collaboration across product, design, and engineering. If we get discovery right, delivery feels inevitable—and customers feel understood.
Every week, I field the same question from product leaders and engineers: should we deploy an AI agent here, or are we overfitting the problem to a shiny solution? Learn when AI Agents actually help product teams—plus a simple framework to decide when not to use them.
When I say “AI agents,” I’m talking about autonomous or semi-autonomous systems that can perceive context, plan steps, and take actions across tools and data sources with minimal supervision—what many now call agentic AI. In product management terms, they’re not just another feature; they’re an operating model shift. Used well, they compound team leverage. Used poorly, they add invisible complexity, new failure modes, and governance headaches.
To make the call with confidence, I use a straightforward VITAL framework that my team can apply in minutes. It keeps us honest about where AI agents are a force multiplier—and where a simpler automation, rule, or in-product UX is the better choice.
V is for Volume. Agents shine where there’s sustained, repetitive, high-throughput work: triaging inbound support, cleansing CRM records, orchestrating QA checks, or synthesizing weekly research summaries. If the workflow happens rarely or ad hoc, an agent is often overhead in disguise.
I is for Instructions. Can I specify success in clear, testable terms? Strong instructions include measurable acceptance criteria and constraints. If I can’t articulate what “good” looks like without hand-waving, the task likely needs product discovery, not autonomy.
T is for Tolerance. What is the blast radius if the agent makes a wrong call? Low-stakes, reversible actions with tight guardrails are ideal. If the tolerance for error is near zero (e.g., irreversible financial transactions or sensitive regulatory actions), favor human-in-the-loop, stronger approvals, or defer agents entirely.
A is for Access. The agent needs the right data, tools, and permissions, with privacy-by-design and data governance in place. If telemetry is sparse, integrations are brittle, or you can’t enforce least-privilege access, you’ll fight fragility more than you’ll gain leverage.
L is for Learning loop. Agents require eval-driven development, Agent Analytics, and continuous feedback to stay accurate as reality shifts. If you can’t measure quality, latency, and cost per outcome—or you lack a retrieval-first pipeline to ground responses—expect drift and stakeholder distrust.
Now, the counterweight. Don’t use agents when the problem is novel or strategically ambiguous and you still need exploratory research; when outcomes are unmeasurable or subjective without heavy context; when stakes are high and the acceptable error rate is effectively zero; when data is siloed, stale, or legally constrained; when the work is one-off or low-volume; or when your team can’t commit to instrumentation, evaluations, and ongoing maintenance. In these cases, a simpler rules engine, a clearer UX, or a well-defined workflow usually beats agentic complexity.
Here’s how this plays out in practice. We’ve seen agents materially improve customer support triage (categorization, priority, and next-best-action suggestions), CRM hygiene (deduplication, enrichment, and routing), and release QA (regression check orchestration with human sign-off). Conversely, we avoid agents for nuanced pricing decisions, sensitive risk scoring without robust datasets, or any workflow where “explainability” and auditability trump speed.
Operationalizing agents is a product problem before it’s an ML problem. Start narrow with a retrieval-first pipeline and rigorous prompt engineering, define success metrics upfront (quality, latency, cost per task), and run head-to-head evaluations against human baselines. Ship behind feature flags, monitor with Agent Analytics, and graduate from assisted to autonomous modes only after you’ve proven stability. Align this with product roadmapping and sprint planning so the work lands as durable capability, not a lab demo.
Finally, be honest about build vs buy. If the workflow is a point of parity, consider buying and focusing your team on integration quality and governance. If it’s a potential source of competitive differentiation, invest in a modular architecture with clear context window management, strong observability, and a feedback loop tightly coupled to your empowered product teams.
The bottom line: AI agents unlock leverage when there’s volume, clarity, tolerance, access, and a learning loop. If any of those pillars is missing, pause. Your best next move is likely better instrumentation, sharper problem framing, and continuous discovery—not more autonomy. That discipline is how product teams turn agentic AI from hype into habit.
