Tag: stakeholder management

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Inspired by this post on Product Talk.

November 18, 2025
Global Invoicing Nightmares: Hard-Won Product Lessons on EU Tax, Compliance, and Customer Value

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

Listen to this episode on: Spotify | Apple Podcasts

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

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

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

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

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

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

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

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

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

Have thoughts on this episode? Leave a comment below.

Full transcripts are only available for paid subscribers.

Inspired by this post on Product Talk.

November 11, 2025
Build a Company You’ll Run Forever: Bootstrapping vs VC, PMF, and the Art of ‘Eating Glass’

I’ve spent my career building products and teams that I intend to steward for the long haul, and I’m drawn to founders who treat company-building as a craft you can practice forever. In this analysis, I break down a journey that crystallizes what it takes: going from a teenage wholesale hustle to an API-first healthcare clearinghouse, and in the process, learning why execution isn’t a moat, why venture capital is “going pro,” and how “eating glass” can become a durable advantage.
Here’s the arc that anchored my thinking: a founder who, at 16, turned $2,500 into a wholesale empire; later bootstrapped a wildly profitable auto-parts business; then sold it to tackle “the most complicated problem” he’d ever encountered: business-to-business transaction exchange. He spent years building EDI infrastructure, threw away the entire codebase eight times, and found extraordinary traction in healthcare. The company recently raised a $70M Series B co-led by Stripe and Addition. The throughline is a consistent, high-agency approach to product management and go-to-market strategy, guided by first principles decision making.
The first customer is often the trickiest—not because demand doesn’t exist, but because the product’s value proposition, points of parity, and competitive differentiation are still coalescing. I push teams to do founder-led GTM early, speak in the user’s language, and orchestrate high-signal conversations that expose real switching costs. That’s how we avoid mistaking polite interest for product-market fit.
Bootstrapping forces rigor, but it also means being “constrained by capital.” There’s a ceiling to the speed at which you can iterate, validate, and scale. Venture capital, in the right context, is like “going pro”: you trade a bit of optionality for time, talent density, and a faster feedback loop. I often see confusion between ownership vs. control; structurally, you can design for alignment while still moving with the urgency a competitive market demands.
One theme I return to with my own teams: execution is never actually a moat. Processes can be copied. Culture can be mimicked superficially. What can’t be easily replicated is the willingness to do the unglamorous, compounding work—what the founder here called “eating glass.” It’s the daily discipline of simplifying the system, instrumenting the edge cases, and standing up operational excellence that compounds into true competitive differentiation.
When product-market fit hits in enterprise infrastructure, it can feel like “the snake swallowing a deer.” Capacity, process, and architecture are stretched to their limits all at once. I’ve experienced the same pattern: everything slows down so the organization can re-architect for scale. The trick is to make those constraints visible—measure service levels, queuing, and error budgets like you would in a production system—so you’re not flying blind.
Some of the strongest product-management instincts I’ve seen borrow from discount retail and Toyota. From discount retail, we learn to obsess over unit economics, operational throughput, and ruthless simplification. From the Toyota production system, we adopt Kanban / TPS (Toyota), continuous improvement, and respect for constraints. In software terms, this becomes fast deployment frequency, small batch sizes, and defect prevention at the source—because “All software is a cascade of miracles.”
Scaling decision-making is where most teams stall. I favor clear ownership, lightweight written narratives, and a bias for first principles decision making over committee compromise. That structure lets high-agency individuals move quickly while keeping cross-functional stakeholders aligned on outcomes vs output OKRs. It’s how you build empowered product teams without sacrificing focus.
Hiring is where philosophy becomes practice. I resonate with the onboarding mantra “everything’s your fault now”—not as blame, but as an invitation to own outcomes end to end. I look for high-agency people who demonstrate systems thinking and the capacity to simplify. Manager hiring should lag role clarity; bring in managers when coordination overhead is the limiting factor, not when it merely feels uncomfortable.
Longevity comes from founder-approach fit as much as product-market fit. Build a company you don’t want to leave by aligning operating cadence, decision rights, and cultural norms with how you actually work best. Maintain conviction in unconventional practice when the evidence supports it, while remembering that “Reality has a surprising amount of detail.” The more I zoom in on the real work—interfaces, edge cases, workflows—the more the right design emerges.
In healthcare EDI, that realism matters. HIPAA overview (HHS) sets the compliance baseline. Payer integrations with Aetna, Blue Cross Blue Shield, and Cigna demand reliability and deep domain fidelity. Cloud and back-office ecosystems—from AWS and NetSuite to Slack, Microsoft Teams, Zapier, and Clay—shape the surrounding workflow. Lessons from Amazon, Target, Walmart, and Costco inform operational rigor; supply chain analogies from Ford Motor Company and GM clarify interface contracts. Porter’s five forces helps frame market structure; perspectives from Jeff Bezos and Peter Thiel sharpen strategic posture.
If you’re building for the long run, here’s the blueprint I use with product leaders: validate painfully specific jobs-to-be-done before you scale; prefer founder-led GTM until messaging closes the intent-to-adoption gap; instrument throughput and quality like a production system; invest in people who treat ambiguity as a chance to lead; and don’t confuse speed with hurry. When the “snake swallowing a deer” moment arrives, re-architect deliberately, protect your margins, and let operational excellence carry you from product discovery to durable product-led growth.
References and resources: Aetna: https://www.aetna.com/, Amazon: https://www.amazon.com/, AWS: https://aws.amazon.com/, Blue Cross Blue Shield: https://www.bcbs.com/, Change Healthcare: https://www.changehealthcare.com/, Cigna: https://www.cigna.com/, Clay: https://www.clay.com/, Costco: https://www.costco.com/, Ford Motor Company: https://www.ford.com/, GM: https://www.gm.com/, HIPAA overview (HHS): https://www.hhs.gov/hipaa/index.html, Jeff Bezos: https://x.com/JeffBezos, Kanban / TPS (Toyota): https://global.toyota/en/company/vision-and-philosophy/production-system, Microsoft Teams: https://www.microsoft.com/microsoft-teams, NetSuite: https://www.netsuite.com/, O’Reilly Auto Parts: https://www.oreillyauto.com/, Peter Thiel: https://x.com/peterthiel, Porter’s five forces: https://www.isc.hbs.edu/strategy/pages/the-five-forces.aspx, “Reality has a surprising amount of detail”: https://johnsalvatier.org/blog/2017/reality-has-a-surprising-amount-of-detail, Slack: https://slack.com/, Stedi: https://www.stedi.com/, Summit Racing: https://www.summitracing.com/, Target: https://www.target.com/, Walmart: https://www.walmart.com/, Zapier: https://zapier.com/

November 6, 2025
From Code to Roadmaps: My Proven Playbook for Engineers Becoming Product Managers

"From code commits to boardrooms. Here are real stories of software engineers who swapped bugs for roadmaps on the road to product manager." I’ve made that leap myself and helped many engineers do the same. In this piece, I share the playbook I use to guide high-potential ICs into impactful product management roles—without losing the engineering rigor that makes them special.

Engineers make exceptional product managers because we’re trained to decompose complex systems, debug ambiguity, and reason from first principles. The transition isn’t about abandoning code; it’s about expanding your scope from implementation details to customer outcomes, market context, and business impact.

The first shift is mental: move from shipping outputs to driving outcomes. Features are a means; value is the end. I anchor this change with outcomes vs output OKRs, ensuring every roadmap item ties to a measurable user or business result rather than a checklist of tickets.

Next, upskill deliberately in three areas: product discovery, product positioning, and stakeholder management. Learn to design unbiased customer interviews, synthesize patterns from qualitative and quantitative signals, and craft crisp value propositions that resonate with real segments. Then practice executive-ready communication—clear decisions, concise narratives, and no jargon crutches.

Here’s the practical, low-risk way to get PM experience without changing your title: form a product trios working group (design, engineering, product) around a real problem. Lead discovery with a weekly cadence, run lightweight experiments, and translate insights into a draft product roadmapping and sprint planning artifact. Ship small, learn fast, and narrate the learning.

Build a simple portfolio that proves product judgment. Include one-page problem briefs, discovery notes, customer quotes, prioritized opportunity trees, and a before/after roadmap snapshot. For each artifact, quantify the impact: activation lift, support ticket reduction, conversion improvement—whatever outcome your work influenced.

If you want to pivot internally, propose a 90-day experiment. Volunteer to own a well-bounded problem, commit to an outcomes dashboard, and set a weekly stakeholder update. Keep a minimal engineering contribution during the trial to de-risk the transition for your team while you demonstrate PM leverage across the squad.

If you’re interviewing externally, prepare two deep case studies: one discovery-led (how you reduced uncertainty) and one delivery-led (how you aligned stakeholders and shipped). Be explicit about trade-offs, risks you retired, metrics you moved, and lessons learned. The best signals of product sense are clarity under constraints and an ability to say “no” for good reasons.

Once you land the role, use a 30-60-90 plan. In the first 30 days, map users, workflows, metrics, and decision rhythms; in 60, run a focused discovery sprint and align on your hypothesis-led roadmap; by 90, deliver a thin slice that proves value and establishes credibility with empowered product teams. Keep your communication tight, your dashboards honest, and your customers close.

Common pitfalls: translating directly from solution space to roadmap without validating problems; equating stakeholder satisfaction with customer value; and mistaking velocity for progress. Avoid them by running small tests early, revisiting segment-specific value propositions, and anchoring trade-offs to product-market fit lessons.

If you’re standing at the edge of this transition, start where you are: choose one user pain, one measurable outcome, and one small bet. Treat it like a product: define success, experiment thoughtfully, and learn in public. The road from engineer to product manager isn’t a title change—it’s a shift in how you create value.

Inspired by this post on Product School.

November 3, 2025
Impact Analysis Mastery: Proven Steps to Predict, Measure, and Maximize Product Outcomes

When I think about the difference between a roadmap that moves the business and one that simply ships output, impact analysis is the habit that changes everything. It gives me and my product trios a disciplined way to forecast value, align stakeholders, and de-risk bets before a single sprint starts. Over the years, I’ve seen great ideas fail not because they were bad, but because we couldn’t articulate, test, and track their true impact. That’s the problem impact analysis solves.

Impact analysis, in practice, is a structured method for predicting how a proposed change will influence user behavior and business outcomes—and then validating those predictions with data. Uncover what impact analysis is, why it matters, and how to do it with proven methods and clear steps for product teams. When done well, it translates strategy into evidence-backed choices that strengthen our value proposition and accelerate product-led growth.

I use impact analysis at three key moments: during product discovery to vet opportunities, in product roadmapping and sprint planning to prioritize, and post-launch to confirm that outcomes beat expectations. It is equally useful for net-new features, UX improvements, pricing changes, and even enablement like in-app guides or product tours.

Step 1: Define the outcome with precision. I anchor every proposal to outcomes vs output OKRs, choose one primary success metric, and record the current baseline. If we plan to experiment, I estimate the minimum detectable effect (MDE) to ensure our A/B testing can actually validate the expected lift. This protects us from investing in ideas that are too small to measure or too broad to manage.

Step 2: Map the causal chain. I translate the idea into a simple impact map: feature change → user behavior (activation, frequency, conversion, retention) → business outcome (revenue, cost, risk, satisfaction). This clarifies what must change in user behavior and why users would care—forcing us to revisit our value proposition if the link feels thin.

Step 3: Size the upside and reach. I estimate who will be exposed (reach), how often (frequency), and the expected behavior change (conversion delta). I complement this with RICE (reach, impact, confidence, effort) or cost of delay to compare options. The goal isn’t perfect math; it’s consistent, transparent assumptions that we can pressure test with data.

Step 4: Evaluate risk, complexity, and dependencies. I assess technical effort, privacy-by-design considerations, data governance needs, and cross-team sequencing. This is where stakeholder management becomes essential—aligning Engineering, Design, GTM, and Security early so we don’t discover hidden blockers mid-sprint.

Step 5: Design the evidence plan. For changes where causality matters, I prefer A/B testing with the right MDE and guardrail metrics. I instrument events and set up dashboards in a unified analytics platform (Amplitude analytics, Pendo, or a homegrown stack) so we can monitor leading indicators quickly. If experiments aren’t feasible, I use sequential rollouts, synthetic controls, or pre-post analyses with clear caveats.

Step 6: Communicate the decision. I share a one-page impact brief that summarizes objectives, hypotheses, metric choices, expected lifts, risks, and the test plan. This reduces debate time, improves stakeholder trust, and enables empowered product teams to move faster with clarity.

Step 7: Ship, monitor, and learn. After launch, I track leading indicators within days and validate lagging outcomes over weeks. I run retention analysis and cohort reviews to confirm that behavior change sticks, and I write a short learning memo—especially when we miss—so future bets get sharper.

On a recent initiative, our team debated whether to build a new onboarding flow or invest in targeted in-app guides. The impact analysis showed the guide approach would reach 3x more users in the next quarter, require half the effort, and be easier to A/B test end-to-end. We shipped the guides, saw a measurable lift in activation, and then recycled those insights to inform the broader onboarding redesign. The analysis didn’t just pick a winner—it created a faster path to compounding outcomes.

Common pitfalls I watch for: chasing vanity metrics, assuming linear impact at scale, ignoring confidence and variance, and skipping instrumentation. Another trap is treating impact analysis as a heavyweight doc—keep it lightweight, comparable across initiatives, and tightly tied to decision-making.

My lightweight template: one sentence on the desired outcome and OKR; a causal chain with the key behavior change; a simple sizing with reach, impact, and confidence; risk and dependency notes; the experimentation plan; and the decision. If we can’t write that in one page, we probably don’t understand the bet well enough to pursue it yet.

The next time you review your roadmap, pick your top three bets and run this playbook. You’ll sharpen your prioritization, increase stakeholder confidence, and give your team a clear line of sight from product discovery to measurable outcomes. That’s how we build momentum, quarter after quarter.

Inspired by this post on Product School.

November 3, 2025
Product Tree 101: The Visual Prioritization Framework I Rely on to Align Teams Fast

When my team is drowning in requests, the Product Tree is the visual tool that brings clarity and momentum. "Learn what a product tree is, how to use the product tree framework, and why it’s a powerful tool for smarter product prioritization." That’s exactly what I aim to share here—how I use it to align stakeholders, sharpen product strategy, and translate ideas into outcomes.

A product tree is a simple yet powerful metaphor for your product. The trunk represents the core value, the roots are the technical foundations and platform capabilities, the branches are product areas or themes, and the leaves are features, experiments, or opportunities. By placing ideas as leaves on the right branches—and making sure roots can actually sustain that growth—we turn a messy backlog into a coherent product roadmap.

Why do product managers swear by it? Because it forces outcomes over outputs, exposes trade-offs visually, and reveals where strategy is thin or overgrown. In one view, you see customer value, technical debt, and strategic focus—crucial for empowered product teams, product discovery, and stakeholder management. It’s also an excellent way to connect outcomes vs output OKRs to tangible delivery paths.

Here’s how I set it up. First, I define the trunk with a crisp product value proposition and the minimum set of experiences that make the product viable. This anchors everything else so we don’t mistake a shiny leaf for the core of the tree.

Next, I map branches to clearly named themes that mirror how customers perceive value—onboarding, activation, collaboration, analytics, or reliability. I keep branches aligned to outcomes to avoid feature-first thinking; this pays dividends during product roadmapping and sprint planning.

Then I add leaves: research insights, customer requests, experiments, and enabling features. I note intent (e.g., drive activation, reduce churn), expected impact, and a rough effort signal. This quickly surfaces which leaves grow the product and which are just twigs.

Finally, I draw roots—the enabling platform work and technical investments that make the branches sustainable. Performance, data governance, privacy-by-design, and scalability belong here. If the roots can’t support the canopy, the tree is at risk, and that becomes a visible, prioritizable problem rather than an invisible liability.

Once the tree is sketched, I facilitate a collaborative session with product trios and cross-functional partners. We prune low-impact leaves, cluster work by outcomes, and explicitly link branches to OKRs. In QBRs vs OKRs reviews, the tree becomes our single source of truth for trade-offs, helping stakeholders see why some requests move up and others wait.

In practice, I use the Product Tree to shape a near-term delivery plan and a longer-horizon narrative. Near term, it informs sprint planning and sequencing by ensuring the right roots land before the heavier branches. Longer term, it clarifies the growth story for product-led growth—what we’ll grow next and why it matters for customers.

A few tips from the trenches: anchor branches to customer outcomes, not internal org charts; spotlight enabling work so platform investments aren’t deprioritized; and revisit the tree after each discovery cycle to keep it fresh. The moment the tree feels lopsided, that’s your signal to rebalance bets or revisit assumptions in product discovery.

If you’re preparing for your next planning cycle, try a 60-minute Product Tree workshop. You’ll come away with a shared mental model, sharper prioritization, and a roadmap that is easy to communicate and defend—because everyone can see the product’s future taking shape right in front of them.

Inspired by this post on Product School.

November 3, 2025
Your Ultimate ProductCon San Francisco 2025 Guide: Best Hotels, Eats & Drinks

Heading to ProductCon San Francisco 2025? I approach conference travel the same way I approach product strategy: optimize for outcomes, reduce friction, and invest in high-signal experiences. Here’s the playbook I use to choose the right hotel, find memorable meals, and make the most of every hour in the city.

For lodging, I prioritize walkability, safety, and quiet rooms so I can focus during sessions and recover at night. If you want to be steps from most venues and meetups, SoMa and the Yerba Buena corridor are ideal. InterContinental San Francisco, W San Francisco, and The Clancy (Autograph Collection) are reliable, business-friendly picks with strong Wi‑Fi and ample lobby space for impromptu one‑on‑ones. If you prefer classic energy and transit access, Union Square hotels like Hotel Nikko and The Westin St. Francis work well. For waterfront views and a calmer vibe, Hyatt Regency Embarcadero puts you by the Ferry Building with easy BART and Muni access.

My booking checklist is simple: reserve early, target a high floor away from elevators, and request early check‑in or late checkout around your session schedule. Loyalty programs often unlock better rates and quiet‑room preferences. If you need heads‑down time between talks, ask about day‑use meeting rooms or find a corner of the lobby with stable bandwidth. I also pack a compact power strip and a long USB‑C cable—two small upgrades that routinely save a day.

Coffee is the fuel of great product conversations. Near SoMa, I rotate between Blue Bottle (Mint Plaza), Sightglass (7th Street), and Philz (Front Street) for pre‑session caffeine and quick stand‑ups. If I’m on the Embarcadero side, the Ferry Building’s roasters are perfect for early starts, and morning lines move faster than you’d expect if you arrive just after opening.

For efficient lunches, I favor fast‑casual spots that can handle volume without sacrificing quality. Mixt, Souvla, Sweetgreen, Super Duper Burgers, and The Grove are dependable within a short walk of most downtown venues. When I need a higher‑signal lunch with a partner or prospect, I book a table slightly off the main corridor to avoid the rush—think Mourad for elevated Moroccan in SoMa or Boulevard along the Embarcadero for a polished, quiet conversation.

Dinner is where the best networking often happens, so I plan for atmosphere, acoustics, and a menu that works for mixed dietary needs. Kokkari Estiatorio (FiDi) excels for executive dinners. Liholiho Yacht Club is a creative, memorable choice for cross‑functional teams. Waterbar or Angler near the waterfront pair great food with views that impress visiting colleagues. For something more casual but still conversation‑friendly, Nopa or Sorella deliver consistently.

When it’s time for drinks, I think in terms of groups and goals. For panoramic views and small group catch‑ups, The View Lounge (Marriott Marquis) is a classic. For wine‑forward conversations with a quiet ambiance, Press Club near Yerba Buena works well. If you’re hosting a more energetic crew, Charmaine’s (SF Proper Hotel), Dirty Habit (Hotel Zelos), or 25 Lusk offer space, good music, and reliable service. For craft cocktails, Pacific Cocktail Haven and ABV are standouts if you don’t mind a short ride.

Transit and timing matter. From SFO or OAK, BART is often the fastest, most predictable route downtown; rideshare is convenient late at night. I walk whenever possible, but I time routes along well‑lit, busier streets and avoid sprinting between neighborhoods tight on time. Microclimates are real—bring layers, comfortable shoes, and a compact umbrella. I schedule 15‑minute buffers around key sessions to handle inevitable friend‑of‑a‑friend introductions.

If you need a professional setting for a quick working session, many hotels will extend lobby seating to guests and their visitors. For dedicated space, day passes at coworking operators like Industrious, CANOPY, or Regus are worth it when you’ve got a client briefing or board prep. For a more casual backdrop, Sightglass and Blue Bottle locations typically have reliable Wi‑Fi and just enough outlets if you arrive off‑peak.

Finally, a word on intent: I set a simple goal for each day—one meaningful connection, one surprising insight, and one concrete action to bring back to my team. ProductCon San Francisco 2025 is a catalyst if you design your experience with the same rigor you apply to your roadmap. If you spot me in a session or at a nearby cafe, say hello—I’m always up for trading notes on product strategy, pricing experiments, and what’s working in the field right now.

Quick note: restaurants and hours can change quickly—make reservations where possible and double‑check opening times the week of the event.

Inspired by this post on Product School.

November 3, 2025
Organizational Development Demystified: The Engine Behind Smarter Teams, Culture, and Growth

When people ask me how product organizations actually scale what works, I point them to a simple truth: organizational development is the operating system that makes strategy executable, teams empowered, and outcomes repeatable.

It turns out that organizational development isn’t just HR lingo. It’s the engine behind smarter teams, better culture, and long-term growth.

In practice, I think of organizational development as the discipline that aligns structure, incentives, rituals, and learning loops so empowered product teams can do their best work. It connects product management leadership with execution through clear decision rights, transparent roadmapping, and ways of working that reduce friction across product, design, and engineering.

On the ground, this looks like moving from activity measures to outcomes vs output OKRs, forming durable product trios to own customer problems end to end, and tightening stakeholder management so priorities don’t whipsaw week to week. It also means investing in onboarding that accelerates time-to-impact, creating feedback rituals that surface risks early, and using retention analysis to make smarter bets about where to double down.

The payoff is tangible: faster decision-making, fewer handoffs, and clearer accountability. Teams ship with confidence, leaders get leading indicators instead of lagging surprises, and employee retention at startups improves because people see how their work connects to a meaningful value proposition and product-led growth.

In my own practice, shifting to outcomes-first planning, establishing product trios, and clarifying interfaces across functions reduced decision latency, improved deployment frequency, and made ownership unmistakable. The organization became more resilient because the culture, processes, and metrics reinforced one another instead of competing for attention.

If you’re starting from scratch, begin by aligning on a small set of outcomes that matter, then redesign ceremonies and artifacts to serve those outcomes. Next, empower teams with clear autonomy and constraints—enough freedom to discover, enough guardrails to focus. Finally, make learning visible: use lightweight postmortems, discovery reviews, and customer signal dashboards so your operating system continuously improves.

Organizational development isn’t a one-time reorg; it’s a habit. When we treat it as a product—iterating on roles, rituals, and metrics just like we iterate on features—performance compounds, culture strengthens, and growth becomes sustainable.

Inspired by this post on Product School.

November 3, 2025
Upskilling vs. Reskilling: My Playbook to Future‑Proof Teams, Boost Retention, and Ship Faster

In fast-moving product organizations, the skills that got us here won’t carry us through the next wave of change. I’ve learned that future-proofing a team is less about hiring unicorns and more about deliberately growing the skills we already have—and doing it with intention.

Upskilling and reskilling aren’t the same. Knowing the difference can help you build smarter teams and avoid costly missteps in your L&D strategy.

Here’s how I frame it with my leaders: upskilling deepens capability in the role someone already holds—think strengthening discovery, data fluency, or stakeholder management inside an existing lane. Reskilling pivots talent into a new lane—say, a support engineer into data engineering or a product marketer into product operations. Both are essential to building empowered product teams, but they solve different problems.

Deciding which path to take starts with the roadmap and strategy. If your outcomes vs output OKRs signal a need for better execution in current domains, upskilling is the lever. If your strategy introduces new bets—gen AI, privacy-by-design, or a shift to platform architecture—reskilling becomes a strategic investment. I run a simple gap analysis: inventory current skills, map them to near-term outcomes, and identify high-leverage gaps by team.

When I upskill, I prioritize learning in the flow of work. That means structured practice—not just courses—embedded into product discovery, product trios rituals, and code reviews. Shadow sessions, lightweight playbooks, and in-app guides turn new concepts into repeatable muscle memory. For new managers, I add targeted coaching for the IC to manager transition, because role clarity and feedback fundamentals compound quickly.

When I reskill, I treat it like a product launch. There’s a clear charter, staged milestones, a mentor, and onboarding tailored to the new role. I timebox practice projects, use product tours and internal sandboxes, and pair people with forward deployed engineers or senior PMs to accelerate context. The goal is confidence and competence, not just completion.

Measurement keeps the investment honest. I track time-to-productivity during onboarding, deployment frequency and DORA metrics for engineering-heavy paths, and retention analysis for people outcomes. For product and design, I look at decision quality in discovery, reduced cycle time from insight to iteration, and the clarity of written strategy. All of it rolls up into OKRs so learning is tied to business outcomes, not just activity.

The AI wave has made this even more urgent. I’m deliberately upskilling PMs on LLMs for product managers, responsible AI Strategy, and data governance, while reskilling a subset of engineers and analysts into applied gen AI roles. We cover prompt design, evaluation frameworks, and privacy-by-design basics, then ship small internal tools to turn theory into practice.

Culture makes or breaks all of this. I set explicit learning budgets, protect focus time, and model the behavior—publishing my own learning roadmaps and post-mortems. Stakeholder management matters too: I align expectations in QBRs vs OKRs, broadcast progress, and celebrate skill gains the same way we celebrate product wins. When people see that growth is visible and valued, momentum builds.

One example that sticks with me: we reskilled a cross-functional cohort into analytics and experimentation while simultaneously upskilling our existing PMs in discovery synthesis. Within a quarter, decisions got crisper, experiments shipped faster, and collaboration across product trios felt effortless. The compounding effect was unmistakable.

If you’re starting from zero, keep it simple: map the skills you have, the outcomes you need, and choose one upskilling and one reskilling initiative you can deliver in the next 90 days. Make learning visible, measure what matters, and iterate. The teams that master this discipline won’t just keep up—they’ll set the pace.

Inspired by this post on Product School.

November 3, 2025
11 Unconventional Product Management Moves That Supercharge Strategy, Teams, and Impact

I’ve spent years leading product strategy at HighLevel, Inc., and the patterns I rely on don’t always show up in the usual playbooks. In practice, the moves that compound impact are often the quiet ones—unsexy, rigorous, and relentlessly customer-centered.

These product management best practices challenge the norm. Read and you’ll sharpen your strategy and elevate your impact beyond just features.

What follows are the 11 under-discussed habits I return to when the stakes are high and the path is foggy. They help me ship meaningful outcomes, develop empowered product teams, and align our go-to-market strategy without getting trapped in feature theater.

Best practice 1 — Anchor goals to outcomes, not output. I frame “outcomes vs output OKRs” so teams focus on behavior change and business results, not ticket counts. Activation rate, retained revenue, and cycle time beat launch volume every time.

Best practice 2 — Run discovery with product trios. I put design, engineering, and product in the same room early, often with forward deployed engineers. This trio model accelerates product discovery, uncovers risks faster, and builds shared ownership.

Best practice 3 — Decide from first principles, then apply the try do consider framework. I separate points of parity from true differentiation and protect our value proposition. The result: clearer choices, less rework, and a strategy that compounds.

Best practice 4 — Be statistically honest with A/B testing. I size experiments by minimum detectable effect (MDE), guard against peeking, and follow through with retention analysis. This discipline prevents false positives from steering the roadmap.

Best practice 5 — Treat delivery as a learning engine. CI/CD, feature flags, and progressive rollouts let us learn without gambling the brand. I track deployment frequency and DORA metrics to raise quality while increasing the tempo of validated learning.

Best practice 6 — Build a unified analytics backbone. I connect product telemetry to a unified analytics platform and CRM integration so we can see the full funnel. Amplitude analytics, Pendo, and Intercom help us tie behaviors to value realization and inform prioritization.

Best practice 7 — Make onboarding a first-class product. In-app guides, product tours, UX writing, and thoughtful tooltip design shorten time-to-value and lift user activation. This is the quiet lever behind sustainable product-led growth.

Best practice 8 — Systematize stakeholder management. I pair QBRs vs OKRs to balance narrative and numbers, keep board management transparent, and align sequencing through product roadmapping and sprint planning. Clear rituals minimize thrash and build trust.

Best practice 9 — Connect strategy to positioning early. I pressure-test product positioning, clarify our value proposition, and deliberately choose which points of parity to match and which to ignore. This reduces me-too work and sharpens competitive differentiation.

Best practice 10 — Use AI as a responsible force multiplier. I employ LLMs for product managers and gen ai for product prototyping while enforcing privacy-by-design, AI risk management, and strong data governance. The goal is leverage without compromising trust.

Best practice 11 — Write it down to move faster together. I keep crisp decision logs, assumptions, and pre-mortems so empowered product teams can act with context. This simple habit makes onboarding easy, reduces re-litigating, and keeps momentum through change.

When I apply these practices consistently, the team ships less noise and more value. The compounding effect is real: clearer priorities, faster learning cycles, stronger alignment, and a roadmap that tells a coherent story from discovery to adoption.

Inspired by this post on Product School.

November 3, 2025
9 Corporate Innovation Trends Redefining Business—and How I’m Turning Them into Wins

Corporate innovation isn’t a side project anymore—it’s the operating system for how we build, scale, and win. In my product leadership work, I’ve watched the pace of change accelerate across every function, from engineering and data to go-to-market and customer success. The companies pulling ahead are the ones translating trends into execution with clarity, speed, and measurable outcomes.

We researched corporate innovation to reveal top trends, types, and examples that can spark growth and keep your business ahead.

Here’s how I’m seeing that play out right now—and the nine trends I’m actively using to guide roadmaps, prioritize bets, and ship value faster.

Trend 1: Generative AI is moving from pilots to products. Teams are evolving beyond demos into durable capabilities powered by gen ai, LLMs for product managers, and agentic AI patterns that automate workflows end-to-end. The winners pair bold AI Strategy with AI risk management, privacy-by-design, and clear value propositions so customers trust what we ship and can see its impact on outcomes, not just outputs.

Trend 2: Product-led growth is becoming the default go-to-market motion. I’m doubling down on onboarding, in-app guides, product tours, and activation loops that reduce time-to-value. We back this with disciplined A/B testing, well-chosen minimum detectable effect (MDE), and retention analysis to prove what actually moves the needle. PLG isn’t a tactic—it’s a cultural shift toward continuous learning and self-serve experience design.

Trend 3: Unified analytics and experimentation are the new backbone. A unified analytics platform, instrumented with tools like Amplitude analytics, Pendo, and CRM integration via HubSpot or Intercom, gives us a single source of truth from acquisition through expansion. I push teams to connect user journeys to revenue and to operationalize insights into roadmapping and sprint planning—not monthly reports that sit on a shelf.

Trend 4: Outcome-driven operating models are replacing feature factories. We align on outcomes vs output OKRs, empower product teams, and structure product trios to balance customer insight, technical feasibility, and commercial impact. First principles decision making helps us cut through noise, set sharper points of parity, and focus on differentiation that customers will pay for.

Trend 5: Velocity and reliability matter more than ever in engineering. Continuous delivery via CI/CD, healthy deployment frequency, and DORA metrics are my leading indicators for a team’s ability to learn fast. I’ve seen forward deployed engineers and thoughtful developer evangelism tighten the feedback loop with customers and speed up iteration without compromising quality.

Trend 6: Data governance and security are strategic differentiators. Trust is a product feature. I prioritize data governance, cybersecurity, and threat detection and response alongside usability. Privacy-by-design isn’t a compliance checkbox; it’s table stakes for enterprise adoption and a durable moat when paired with transparent controls and auditability.

Trend 7: Pricing and packaging innovation is unlocking growth. We’re testing SaaS pricing models, including consumption SaaS pricing, to align value delivered with value captured. Clear articulation of the value proposition and thoughtful packaging reduce friction in sales and support product-led expansion. Pricing experiments belong in the product backlog—not just in finance spreadsheets.

Trend 8: Customer-in-the-loop discovery is the fastest path to relevance. I treat product discovery as a continuous practice, weaving QBR-style business reviews into roadmaps and using stakeholder management to align incentives across sales, success, and product. Customer support ai strategy helps surface high-signal insights from tickets and conversations, turning support into a discovery engine.

Trend 9: Open platforms and ecosystems amplify innovation. From API-first thinking and ChatGPT connector patterns to integrations that meet customers where they work, ecosystems drive stickiness and reduce time-to-value. The strongest roadmaps combine a focused core with extensibility that partners and customers can build on.

How to act now: I recommend a simple try do consider framework. Try one high-conviction AI use case with clear guardrails. Do instrumented experiments across onboarding and activation to fuel product-led growth. Consider pricing and packaging tests tied to measurable outcomes. With disciplined learning cycles and empowered teams, these trends stop being headlines—and start becoming compounding advantages.

Innovation favors teams that ship, learn, and adapt. If these trends are on your roadmap, align them to outcomes, measure obsessively, and keep customers in the loop. That’s how we turn momentum into durable growth.

Inspired by this post on Product School.

November 3, 2025
8 Proven Strategies I Use to Upskill Teams Fast and Future-Proof Our Edge in the AI Era

Your team’s skills have an expiry date. Here’s how to upskill employees before the clock runs out and your edge goes with it.

I’ve learned that upskilling isn’t a one-off training day—it’s an operating system for building resilient, empowered product teams. When we treat learning as a product, with clear outcomes, feedback loops, and constant iteration, we future-proof both our people and our roadmap. Below are the eight strategies I rely on to upskill employees quickly and sustainably while strengthening employee retention and execution quality.

1) Anchor upskilling to strategy and outcomes. I start by mapping critical capabilities to our company strategy and outcomes vs output OKRs. This makes learning unambiguously relevant: every course, cohort, and coaching session ladders up to measurable value. If a skill doesn’t advance our north-star metrics or customer outcomes, it doesn’t make the cut.

2) Build a learning operating system, not a library. Content without cadence is shelfware. I establish a predictable rhythm—monthly skill sprints, short microlearning modules embedded in workflows, and quarterly capability reviews during planning. We integrate upskilling into onboarding, QBRs vs OKRs check-ins, and product roadmapping so learning time is protected, visible, and non-negotiable.

3) Design role-based paths with clear ladders. I create skill matrices for PMs, designers, engineers, and GTM partners, then craft levelled learning paths to close gaps. We use the 70-20-10 model (doing, coaching, coursework) and pair it with individual development plans, so growth is personalized but standardized enough to scale. This clarity boosts motivation and speeds up onboarding.

4) Learn by shipping real value. The fastest learning happens on real products. I pair courses with stretch assignments tied to live initiatives—product discovery sprints, customer shadowing, rapid prototyping with gen ai, and cross-functional product trios. We treat these as safe-to-try experiments with clear success criteria, so teams upgrade skills while moving the roadmap forward.

5) Institutionalize coaching and peer learning. I formalize mentorship, guilds, and weekly critique sessions to turn tacit knowledge into shared practice. We run cross-team demos and communities of practice so lessons travel fast. Managers coach to outcomes, not checklists, and we reward people who teach—because knowledge multiplied beats knowledge hoarded.

6) Measure capability, not attendance. I avoid vanity metrics. Instead, I look for leading indicators that learning is changing behavior and outcomes: higher quality product discovery, clearer product positioning, tighter stakeholder management, improved deployment frequency, and stronger retention analysis. Where appropriate, we set a minimum detectable effect (MDE) for skill experiments to ensure we can actually see impact.

7) Fund time, not just tools. Upskilling dies when calendars are full. I carve out recurring maker time for learning, set explicit expectations in performance plans, and tie promotions to demonstrable capability growth. We provide stipends for courses and certifications, but the real unlock is creating space and manager accountability so learning sticks.

8) Use AI strategically to accelerate practice. We embed AI Strategy thoughtfully: gen ai co-pilots for research synthesis, scenario role-plays for stakeholder conversations, and guided feedback for UX writing and product tours. The rule is simple—AI should compress cycle time and elevate judgment, not replace it. I encourage teams to document prompts and playbooks so good patterns compound.

To align and de-risk, I bring stakeholders into the loop early—finance to co-own ROI, HR to integrate paths into career frameworks, and functional leaders to ensure parity across teams. This alignment reduces friction, strengthens product-led growth, and keeps the effort resilient through reorgs and strategy shifts.

The outcome of this approach is simple: faster time to competency, higher confidence, and a culture where learning is part of how we build. Upskilling is the most durable competitive advantage I know—because tools change, but teams that learn together win together. If your edge feels like it’s slipping, start small, make it visible, and iterate. Your future roadmap—and your people—will thank you.

Inspired by this post on Product School.

November 3, 2025