Last week, a license plate frame that read “Happiness is being Norwegian” gave me a reaction I didn’t choose. It was already formed before I knew it was happening, small and quick, the kind of thing you’d miss entirely if you weren’t paying attention. Norway is one of my favorite destinations in the world, a place that lingers in my mind long after my trip. So when I saw the frame, my reaction was an instant warmth towards a stranger, with whom I shared a fondness for Norway.

And then a thought: what if that plate had reflected something I had no connection to? The warmth wouldn’t have been there. Same stranger, same car, same morning. The only thing that changed was whether I saw myself in them.

That’s when something clicked. I needed a way to get under reactions like that one in real time, before they hardened into decisions.

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The mechanism

Most bias training asks you to suppress the snap judgment, override the instinct, and apply the framework. That approach misreads what a reaction is. Your reactions are data that hasn’t been decoded yet, and treating them like noise just buries the signal.

The variable swap is how you decode it. Take the situation you’re reacting to and swap one variable. Keep everything else identical. Then observe what changes.

If your reaction changes, you’ve found a gap between your stated principles and your actual behavior. Something worth examining, not because it makes you a bad person, but because the gap is information. Stay curious, instead of defensive.

If your reaction stays the same, you’ve found a genuine principle. And now you can ask a sharper question. Why does the world respond differently to identical things?

If you learn nothing, you swapped the wrong variable. Try another. The recursion is what gives this tool its depth. Run it enough times, and it becomes a path toward first principles, one swap at a time.

You can run this two ways. The first is after the fact. You catch yourself with a reaction you didn’t choose, a smile, a judgment already made, and you run the swap to find out what your instincts were carrying.

The second is before the fact. Before a hire, before a promotion call, before championing one person’s idea over another’s, you run the swap on yourself first. Same tool either way. The first builds self-awareness over time. The second builds integrity into a decision while still allowing you to change it.

This matters most in the decisions leaders think are objective. Hiring. Promotion. Whose idea gets championed in a meeting? Who gets the benefit of the doubt when a project misses? The answer should be independent of who’s asking and who’s being evaluated. Run the variable swap, and you’ll find out whether it actually is.

The limit nobody talks about

There’s a ceiling on this tool that’s worth naming.

You can only swap variables that you can imagine. The ones you’re most motivated to avoid testing are exactly the ones you won’t think to generate. The constraint here is the operator, every time. The unknown unknowns aren’t random. They cluster around the places where examination would be most uncomfortable.

I’ve run this sweep enough times to know that my variable list is not neutral. It reflects my frame of reference, my experiences, and the categories that feel salient to me. When I swapped the plate frame for other possibilities, I picked the ones already in my mental model. I didn’t pick the ones I had no instinct about, because I didn’t know they were missing.

That’s a real ceiling. And it’s where things get interesting.

What AI actually does here

There’s a version of the AI argument I find unconvincing: that AI can help you examine your reactions because it’s unbiased. It isn’t. AI is trained on human-generated data, which means it reflects the aggregate of human perception, not its absence.

Which is exactly what makes it useful.

The honest version of the argument is narrower. AI expands the variable set beyond your blind spots into what the world around you actually perceives. The variables you wouldn’t think to test. The variables that millions of people, with different frames of reference, different experiences, and different cultural contexts, would notice immediately.

I came across a parallel method recently from on Substack: tell AI where you want to end up, then ask it to work backward and map the chain of assumptions you never examined. Different entry point, same underlying insight. AI as a tool for surfacing what you couldn’t see from where you were standing.

AI is widening the aperture of the test, nothing more. The reaction is still yours. The noticing is still yours. The examination is still yours. What AI contributes is a variable set that reaches beyond your own perception into the broader landscape of how the world sees things.

What this looks like in real life: you bring a decision to AI before you make it. You don’t ask “Am I biased?” because that question produces nothing useful. You ask: “Here is the situation. What variables might be influencing my reaction that I haven’t thought to test?” Let it generate the list. Then run your reactions against that list honestly.

What this builds over time

Running this sweep on every decision isn’t realistic, and it isn’t the goal anyway. The goal is to run it often enough, in hiring reviews, in performance cycles, in the five minutes after a meeting where something felt off but you couldn’t name why, so that over time, your instinct itself becomes more principled.

The human does the noticing. AI widens the aperture. Repetition does the recalibration.

You don’t become unbiased. No one does. But you can become someone whose instincts have been tested enough times, against enough variables, that they’re carrying less you don’t know about. What the license plate really taught me is that the instincts I trust most are exactly the ones that have never been stress-tested. If warmth can be trained into you without your noticing, then any reaction can. The plate frame just happened to be the one I caught.

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There’s a harder problem waiting. Running the variable swap outward and backward mapping inward on the same decision, and watching what happens when they converge. That’s next.

If you've run a version of this swap on yourself, I'd love to hear what variable you landed on. Reply or leave a comment

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Someone I coach has been juggling a lot. Interview prep for competitive roles, a startup she’s building with a co-founder, coursework, and leadership commitments. We’ve had three sessions together, and the work has been good. She’s executing, absorbing feedback, and making visible progress.

After our last session, I tried something I’ve been experimenting with. I fed all three sets of meeting notes to Claude and asked it to find patterns I was too close to see. The output was long, and most of it confirmed what I already knew.

Then I hit this line.

“You are both using the same observation to reach different conclusions, and you have not noticed you are reaching different conclusions.”

I read it twice.

She had been saying (albeit in different ways across all three sessions) that now is the time to focus on building a startup, since she has fewer constraints and personal obligations at this point in her life.

I had been saying: “Since you have fewer constraints and personal obligations at this point in your life, you should keep your options wide open and build a diverse skill base before committing to one path. Now is the time to explore professionally.”

Same facts. Opposite takeaways. Three sessions in, and neither of us had named that gap. I was steering her toward breadth. She was building toward depth. We were both nodding at each other’s reasoning and walking away with completely different conclusions about what it meant.

That is the kind of thing that lives quietly in a relationship for months. You don’t notice it because you’re inside it.

The practice

For any recurring meeting, create a shared document that stays open between sessions. As topics come up during the week, add them. Invite the other party to do the same. When you meet, that doc is your agenda. Run an AI note taker and drop the summary into the same doc afterward.

Over time, you accumulate something most relationships don’t have: a record of what keeps surfacing, how topics evolved, and what never got resolved.

Then feed it to Claude with a prompt that asks for the interpretation you can’t give yourself. You don’t need a summary; you already know what’s in it. Here is the exact prompt I used:

The prompt

I’m sharing a record of meeting notes and agenda topics from recurring conversations with [person/group]. Read through the full document and help me see patterns I’m probably too close to notice myself. Skip the summary. I already know what’s in it.

Specifically, I want to know:

1. What topics keep recurring? For each one, what does the recurrence tell me? Is it an unresolved issue, a relationship dynamic, a structural problem, or something I keep revisiting without fully closing?

2. Where am I the bottleneck? Look for topics where progress seems to stall on my end, where I’m asking for the same things repeatedly, or where I seem to be creating work for others.

3. What am I not bringing to these meetings that the pattern suggests I should be? What’s conspicuously absent?

4. How am I showing up in these conversations — as a problem-solver, an escalator, a decision-maker, a blocker, something else? What does the language and topic mix tell you?

5. If you had to name one thing I’m avoiding based on what’s in this doc, what would it be?

6. What patterns do you see in what the other party brings? What does their topic mix tell me about what they’re worried about and what they need from me?

7. Where are we both circling the same issue from different angles without naming it directly?

8. If you were watching this relationship as an outside observer, what would your impressions be? What am I missing?

Tell me what this looks like from outside the room.

What to expect from the output

It will be long. Some of it will be wrong, because Claude is working from notes, not from being in the room.

Your job is to filter. The observation you wouldn’t have surfaced on your own is usually buried somewhere in the middle, and that alone is worth the twenty minutes it takes to set this up. Look for surprises, where the observation does not match your mental model of what you were trying to accomplish or how you intended to be perceived. Dig into those, and verify that it’s a genuine conclusion and not a misinterpretation of the notes.

When you are satisfied with how it works, ask Claude to create a skill for you and save it. When you paste meeting notes in the input, the skill will trigger automatically.

While questions 1 through 5 improve self-awareness, questions 6 and 7 in the prompt above are where the most uncomfortable and useful observations tend to live. Most people don’t think to ask about patterns in the other party’s communications at all. They feed in their notes and ask Claude to reflect their own behavior back at them, which is useful, but it’s only half the picture.

The failure modes

Notes have a structural problem that transcripts partly solve, but neither eliminates all of these:

1. Frequency isn’t significance

If a topic appears in all three sessions, Claude will flag it as a pattern. But some topics recur because they’re genuinely unresolved. Others recur because they’re standing agenda items, relationship rituals, or topics one party always raises regardless of context. Claude can’t tell the difference without more context than notes typically provide.

2. Salience bias in what gets written down

You write down what struck you. What didn’t strike you doesn’t make it into the notes, which means Claude is analyzing a filtered sample and has no way to know what was filtered. The silence in the document isn’t neutral. It’s shaped by what you thought mattered. Claude can’t see what you left out.

3. The prompt shapes the output

The prompt asks Claude to find where you’re avoiding something, where you’re the bottleneck, what’s conspicuously absent. Those are leading questions. Claude will find answers to them because you asked for answers, not because the answers are necessarily real. A different prompt framing would surface different patterns from the same notes.

4. The notes are already interpreted

Most people don’t write raw notes. They write their version of what happened. “She seemed resistant” is already a conclusion. “He kept circling back to resourcing” might mean he’s worried about resourcing, or it might mean that’s the frame you applied to three different comments he made. When you feed interpreted notes to Claude, you’re not getting a second perspective. You’re getting your own perspective reflected back with more structure.

Using the raw transcript eliminates salience bias and improves frequency signal, but increases output volume and doesn't resolve the interpretation problem or the prompt-shapes-output problem.

The output that feels most insightful is the output most worth interrogating, because insight-feel and accuracy are not the same signal.

Where to start

Pick one recurring meeting where you’ve felt like something is slightly off but you can’t name it. Could be a direct report, a peer, a stakeholder, a mentor. Create the shared doc today. Add the first topic. Invite the other party.

You don’t need three sessions’ worth of notes to run the prompt. Even one session with a few agenda items will start to show you something. The pattern gets sharper over time.

The gap between what you think is happening and what is happening has been there the whole time. Now you can see it.

“Great data,” he said. “This really makes a lot of sense.”

I walked out of that meeting feeling cautiously optimistic. The Head of Marketing had just sat through my full analysis: customer counts, revenue baseline, industry benchmarks, and top-of-funnel projections, all the way down to the revenue number I’d been hired to deliver. The logic was airtight, the numbers were clean, and he’d told me in his own words that it made sense. I figured we were close.

He did nothing. The next meeting, same reaction. “Great data.” Nothing again.

I was three months into a new VP role at a Fortune Global 500, hired mid-year into a product portfolio that had been planned without me. The top-of-funnel was entirely organic, and the product wasn’t anywhere close to a state where it could drive meaningful organic traffic. I had 90 days to show results and 120 days to show revenue, in a portfolio that had essentially zero demand generation infrastructure in place when I arrived.

The one thing I needed was a demand generation budget. The one person who controlled that budget kept telling me my data was great and doing nothing about it.

So I built a better deck. More benchmarks, tighter projections, cleaner visuals, and a more explicit line from top-of-funnel investment to bottom-of-funnel revenue. I kept sharpening the argument because I was convinced the problem was that I hadn’t made it clear enough yet. As a logical person, when I face a problem, I go to data first, because in most situations throughout my career, a well-reasoned, evidence-backed argument is what moves people. That’s the tool I know how to use, so that’s the tool I kept picking up.

The problem was that I was using a screwdriver on a nail.

What was actually happening in those meetings

The Head of Marketing had planned his entire year before I was hired. His budget was fully allocated. He was privately worried it wouldn’t be enough for the goals he’d already committed to, and every dollar he had was tied to products the C-suite was actively and visibly tracking. My products were showing up in monthly reviews, and anyone paying attention could reasonably infer they needed support. The Head of Marketing chose not to make that inference.

From where he sat, the calculus looked like this:

• Reallocating budget to my portfolio meant pulling from initiatives tied to his existing commitments

• My portfolio had no explicit executive mandate behind it, so supporting it was a discretionary risk

• If the demand gen worked, the credit would flow to my products recovering, not to marketing

• If it didn’t, he’d burned budget on something nobody had formally asked him to prioritize

“Great data” was not confusion, and it was not a request for more information. It was a professional way of saying: I see your logic, and it has nothing to do with my goals or incentives. He wasn’t going to say that out loud, of course, but it changed nothing for him. The logic was fine. The incentives pointed somewhere else entirely, and no amount of better analysis was going to change that.

What I needed to see earlier, and didn’t, was that I was asking someone to take on personal risk for a portfolio that had no explicit cost attached to ignoring it. Until that changed, his rational move was to stay exactly where he was.

The part that’s genuinely hard for logical people

When we’ve spent years building the skill of making a tight case, when being the clearest thinker in the room has historically been what moved things forward, it becomes almost automatic to reach for data when we hit resistance. We assume the resistance is informational. We assume that if we just find the right angle, the right benchmark, the right framing, the other person will see what we see and act. I am guilty of this. That’s how I know.

But there’s a category of problems where data is genuinely irrelevant to the outcome. It’s the category where the person you’re trying to move has incentives pointing in a different direction than your ask, not because they’re irrational, but because they’re rational about a completely different set of constraints than the ones you’re reasoning from. Their budget, their performance review, their existing commitments, their relationships upward: all of that is a system they’re optimizing for, and your ask either fits into that system or it doesn’t. Better data doesn’t change the system. It just gives them more to politely acknowledge before they don’t move.

The diagnosis that actually matters is not “do they understand my argument?” Before you build the next deck, ask yourself:

• Does saying yes cost them something?

• Does saying no cost them anything?

• What are they being measured on, and does my ask connect to that or compete with it?

• Who above them has explicitly said my work matters, not implied it, not included it in a slide, but said it out loud in a way that created accountability?

If the answers to those questions are uncomfortable, you are not in a logical conversation. You are in a political one, and the sooner you know that, the sooner you can stop building decks and start thinking about what actually needs to change.

How it ended

I escalated. Got my boss involved, got time in front of the CEO, and watched how fast things shifted once the CEO made it unambiguous that my products needed attention. Budget appeared almost immediately after that conversation, which told me everything I needed to know about where the real blockage had been all along.

What I felt in that moment was not relief. It was frustration, and honestly, some embarrassment. I was a VP at a Fortune Global 500 company, as was he, and we both reported directly to the C-suite. Needing my manager to intervene to get a peer to act on something that was clearly in the company’s interest made me look like I couldn’t operate independently, which, at that level, is not a reputation you want. The budget I finally got was the bare minimum for a pilot. I wasn’t allowed meaningful input into how it was spent, and the money got directed in ways that didn’t move the needle. The products didn’t recover.

The story doesn’t have a good ending. I’m telling it anyway, because the lesson inside it is about what I should have done months earlier, before any of this became necessary.

What I do differently now

That experience rewired how I walk into any conversation where I need something from someone who has no obvious reason to give it to me.

I don’t start with my ask anymore. I start with their world.

Before I walk in, I try to understand what they’re measured on, what they’re worried about, and what goals they’re actively trying to hit. Sometimes I can figure that out from context, from how their team talks, from what gets mentioned in all-hands, from what their manager cares about. Sometimes I don’t have enough access and I have to ask directly, which is a conversation in itself and usually a useful one. It depends on the relationship and how much visibility I have into their situation.

Once I have a picture of their incentives, I work through four questions in order:

• What do they actually care about right now?

• Is there a way to reframe what I need so it connects to something on their list?

• If there isn’t a natural connection, is there a way to get my goal onto their list, not through pressure, but by making it genuinely worth their while?

• Only then: how do I state my ask in a way that lands in their world, not mine?

Step three is the one most people skip. It requires accepting that sometimes the problem isn't that your ask is poorly framed. It's that the other person has no structural reason to say yes, and until that changes, better framing won't help. In my case, that meant finding a way to change the budget situation entirely before I walked back into that room. I'll come back to what that looked like in a moment. It's not manipulation. It's the work of figuring out how to make the right outcome work for both people, which is what I should have been doing from the start.

The honest version of “influence without authority” is not a communication technique. It’s the work of understanding someone else’s constraints well enough to find an outcome they’d actually choose.

What I should have done instead

In hindsight, there were two moves I should have made at the very beginning, before I walked into a single meeting with the Head of Marketing.

The first was going to the CEO early and asking for an explicit budget earmarked specifically for demand gen tied to my new revenue goals. Not assuming that because my products were in the monthly review deck the resources would materialize. Actually asking, directly, for money to be allocated to make the goal achievable. That’s not an unreasonable ask when you’ve just been hired with a specific revenue target attached to your name. The deck I built wasn’t the mistake, by the way. What I should have done was use it differently, not to justify my ask to the Head of Marketing, but to build the demand gen number together with him, so that the goal and the resource request felt jointly owned rather than handed down.

The second was using part of that budget as an offer rather than a request. If I’d walked into his office and said: here is additional budget for your team to support my products, and I’d like your team to lead the demand gen strategy, that conversation looks completely different. His risk calculation changes. Instead of being asked to pull from existing commitments, he’s being handed new resources with a clear mandate attached. He might actually become an ally. What I had instead, for my entire tenure at that company, was a marketing team with every structural reason to deprioritize my portfolio and no reason not to.

I turned a solvable resource problem into an entrenched political one, mostly by arriving with logic when I should have arrived with a different kind of proposal entirely.

Where to go from here

Think about wherever you’re stuck right now. Think about the person who keeps nodding and not moving. Before you go back with more analysis, ask yourself honestly whether this is a comprehension problem or a consequence problem. Ask what they’re being measured on and whether your ask connects to that or competes with it. Ask who above them has explicitly said your work matters, not implied it, not included it in a slide deck, but actually said it out loud in a way that created accountability.

And then ask whether there is a version of this where you come in with resources rather than requests. Whether there is a way to make saying yes easier for them rather than just more logical for you.

The data was never the obstacle in my story. The incentive structure was, and the incentive structure was always going to stay exactly as it was until something changed the consequences of not acting. I kept trying to change minds when what I actually needed to change was the conditions under which saying yes became the easier choice.

I write about strategic product thinking for leaders in resource-constrained environments. If this resonated, subscribe below.

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I was hoping for a better price and decided to check other sites. I then opened an incognito window to shop, and lo and behold, the price was now $369!

The company that just announced a $12 billion strategy to get existing customers to buy more... was giving new customers a better deal.

I had to laugh. Not because it was funny, but because I have seen this movie before.

You probably have too.

Your CEO announces a strategic pivot in the quarterly all-hands. “We’re shifting from acquisition to retention.” Or “Enterprise is now the priority.” Or “We’re going upmarket.”

The strategy sounds great. The logic is sound. Leadership is aligned and all-in.

Then nothing changes.

Marketing keeps running acquisition campaigns. Sales keeps closing SMB deals. Product continues to add features for new users. Your roadmap gets shuffled. Again.

Three months later, leadership is frustrated that “the strategy isn’t working.” Six months later, they announce yet another pivot. Your roadmap gets reshuffled. Again.

And you’re stuck in the middle, watching the pattern repeat.

Here’s what I’ve learned:

You can’t always change the systems. But you can learn to read them. And reading them correctly by spotting the gap between stated strategy and embedded incentive is one of the most valuable skills you can develop as a product leader.

It’s the difference between:

• Thrashing your roadmap every quarter chasing strategic announcements

• Building credibility by predicting what will actually happen

• Protecting your team’s focus for work that will actually get resourced and rewarded

Let me show you how.

The Pattern: Three Levels of Misalignment

When leadership announces a strategic pivot, misalignment happens at three levels. Learn to spot them, and you’ll know within 30 days whether the pivot is real or performative.

Level 1: Who Owns the New Number?

What to look for:

When strategy changes, someone needs to own the new primary metric. Not track it on a dashboard. Own it. As in: they get headcount for it, their roadmap prioritizes it, their 1:1s focus on it, their bonus depends on it.

The diagnostic questions:

After the strategic announcement, ask yourself:

• Who owns [new strategic priority] as their primary KPI?

• Is it their #1 metric or their #5 metric?

• Did they get headcount allocated to support it?

• Did their roadmap shift to reflect it?

• Do they have the budget authority to move it?

• Does the sales comp plan actually reflect it?

Example from my advisory practice:

My client, a SaaS company, announced that it was “shifting to enterprise.” I asked the VP of Product: “Who owns enterprise revenue as their primary KPI?”

Silence.

Then I looked at what actually happened:

• Product roadmap: Still 70% features for SMB users (”protect existing revenue”)

• No new PMs hired specifically for enterprise features

• Sales comp still paid the same for $10K SMB deals as $100K enterprise deals

• Marketing still measured on MQLs (which favored high-volume SMB)

• Sprint planning: Enterprise features consistently deprioritized when timelines got tight

Nobody owned enterprise revenue. Which meant enterprise wasn’t actually the strategy—it was an aspiration.

What you can’t control:

You can’t assign ownership to others. You can’t allocate headcount to other teams. You may not even be able to influence those decisions.

What you can control:

You can spot the gap.

And you can use it to predict what will actually happen.

When no one owns the new metric, and there are no actual resources behind it, the old strategy continues. Save your team’s energy for work that aligns with what’s actually being resourced and measured, not what’s being announced.

Level 2: What Are the System Constraints?

What to look for:

Strategic pivots don’t happen in a vacuum. They happen inside systems that have inertia — roadmaps committed to customers, sprints already planned, engineering capacity already allocated, technical debt that cannot be delayed, comp. cycles that lock in annually, partner relationships built over years.

The diagnostic questions:

• What’s already on the roadmap that can’t be stopped? (Customer commitments, contractual obligations, dependencies)

• Where is engineering capacity already allocated? (What gets cut to make room for the new priority?)

• What sprint/planning cycles constrain rapid change? (Are you mid-quarter? Mid-release cycle?)

• How long ago was this year’s comp plan locked in? (Usually 3-6 months before the fiscal year)

• What partner/customer commitments constrain pivoting? (APIs, integrations, SLAs)

• Who has the authority to override these constraints? (And will they actually use it?)

Example from above, continued:

The company announced that it was “going upmarket so they can focus on quality over speed.”

But:

• The roadmap had 3 major features committed to existing customers for the next two quarters

• Engineering had just staffed two new teams focused on SMB feature velocity

• Product demos in all-hands still celebrated “features shipped this quarter.”

• The Q3 OKRs (set 6 weeks ago) were all about shipping velocity

• No one got approval to slip committed dates in the name of quality

• There was a hiring freeze with no signs of a thaw

Even if the product team wanted to pivot immediately, they had:

• 24 weeks of committed work over the next 2 quarters that they couldn’t stop

• A team structure optimized for the old strategy

• OKRs locked until next quarter

• No air cover to miss commitments

The strategy was right. The timeline was impossible.

What you can’t control:

You can’t change the constraints. Roadmap commitments, team structures, and planning cycles, especially, are often locked in at the C-suite level.

What you can control:

You can map the constraints and predict a realistic timeline.

If roadmap commitments run through Q1 next year, and team reorgs happen in Q2, and OKRs are updated by then, the earliest you’ll see actual execution shift is 6 to 9 months from now.

Plan accordingly. Don’t burn your team out trying to execute a strategy that the systems won’t support for another two quarters.

Level 3: What Behavior Is Actually Being Rewarded?

What to look for:

This is the most important level because it reveals the truth.

Forget what the strategy says. Watch what gets demo’d in all-hands. Watch what gets people promoted. Watch what wins engineering resources in sprint planning when there’s a conflict. Watch what gets deprioritized when timelines slip.

The diagnostic questions:

• What work got someone promoted recently?

• What wins get celebrated in all-hands or demo days?

• When engineering capacity is constrained, what gets staffed and what gets backlogged?

• What does leadership actually review in planning meetings? (Look at their agendas)

• What “misses” get you in trouble vs. excused?

• When features compete for resources, which type wins?

Another example from my practice:

A company said, “customer experience is everything” and “quality over speed.”

Then they:

• Cut the support team first during a downsizing, replacing them with AI agents

• Promoted the Product Leader whose team shipped fastest, even though their features had the highest bug rates

• Celebrated “features shipped this quarter” in every all-hands, not customer satisfaction improvements.

• Consistently staffed new feature work over tech debt or quality improvements

• Excused bugs as “we’ll fix it in the tech. debt sprint” (which never came)

• When asked to choose between hitting a launch date or improving quality, always chose the date, citing competition

The stated priority: quality and customer experience. The rewarded behavior: ship fast, worry about quality later.

What you can’t control:

You can’t change what gets rewarded at the company level. That’s culture and leadership, which moves slowly.

What you can control:

You can align your roadmap and team’s work to what’s actually rewarded, not what’s stated.

If velocity is what gets celebrated and resourced, optimize for shipping. If acquisition features are what get staffed and demo’d, focus there. If you optimize for the stated strategy while everyone else optimizes for the embedded incentives, your roadmap will get deprioritized, and your team will get frustrated.

This isn’t cynical. It’s realistic.

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How to Use This as a Product Leader

You’re not the CEO (no, not even of your own product). You can’t change comp. plans, rewrite OKRs, or restructure the organization.

But you can use pattern recognition to:

1. Protect Your Team from Roadmap Whiplash

When leadership announces a pivot, your team looks to you for direction. They’re asking: “Should we drop everything and reorganize the roadmap around this new priority?”

Instead of immediately reshuffling sprints, pause. Run the diagnostics above. Then tell your team:

If systems are aligned: “Yes, this is real. Here’s how we’re reprioritizing. Here’s what we’re stopping and what we’re starting.”

If systems aren’t aligned: “Leadership is serious about this direction. But the systems won’t support it until [Q3/next planning cycle/after this release]. Here’s what we’re continuing now, and here’s when we’ll shift.”

This prevents your team from:

• Thrashing between priorities every quarter

• Starting work that won’t get resourced or won’t make it to launch

• Becoming cynical about every strategic announcement

• Losing focus on work that’s actually getting measured

2. Build Credibility by Predicting Reality

The leaders who advance aren’t the ones who repeat strategy slides. They’re the ones who accurately predict what will actually ship and what will get deprioritized.

When you can say: “Leadership announced X, but based on [roadmap constraints/resource allocation/rewarded behaviors], here’s what I think will actually happen”—and then be proven right—you build enormous credibility.

Example:

Leadership announces: “We’re enterprise-focused now.”

You say (in your 1:1 with your director): “I think we’ll see some enterprise features ship in Q3-Q4 next year, but we won’t see the roadmap meaningfully shift until next planning cycle, when we can reallocate teams. In the meantime, we have 12 weeks of SMB commitments we can’t break. I’m planning to keep 70% of my roadmap on committed SMB work and explore 30% on early enterprise validation.”

Six months later, you’re proven right. Your director remembers that you called it. Your team stayed focused instead of thrashing.

3. Choose Your Battles Wisely

Not every strategy-incentive gap is worth fighting.

Some gaps are temporary (systems just need time to catch up). Some gaps are intentional (leadership knows but isn’t ready to communicate the constraints publicly). Some gaps are permanent (conflicting priorities that will never fully align).

Temporary gaps: Acknowledge them, plan for the transition timeline, protect your roadmap from short-term thrashing.

Intentional gaps: Work the back channels to understand the real constraints, plan your roadmap accordingly.

Permanent gaps: Accept that you’re operating in a company with conflicting priorities. Optimize for what’s actually rewarded, not what’s stated. Or start looking for a company with better alignment.

The mistake is treating every announcement as gospel and immediately reorganizing your roadmap. The wisdom is recognizing which announcements have teeth and which are aspirational.

4. Surface Contradictions (Carefully)

You can’t change the systems, but you can make the contradictions visible. But be careful, and do it right.

Bad approach: “This strategy won’t work because we don’t have the engineering capacity.”

(This makes you sound like a naysayer. Leadership already knows this. They’re either working on it or dealing with constraints you don’t see.)

Better approach: “I’m excited about this direction. To help my team plan the roadmap around it, I need clarity on a few things: Who owns [metric] as their primary KPI? When can we expect the headcount/capacity allocated to this? What existing roadmap commitments can we renegotiate? What’s the timeline for the shift?”

Frame it as seeking clarity to execute, not as poking holes in the strategy.

If you get clear answers, great—you have a timeline to plan against. If you get vague answers, that’s also useful data. It indicates the systems aren’t yet aligned, so you should plan conservatively.

5. Document the Pattern

When you spot strategy-incentive misalignment, document it. Not to be vindictive, but to build your own mental models.

Keep a simple log:

• Date: Strategic announcement

• Stated priority: What leadership said

• System changes: What actually changed in roadmap/resourcing in the following 30/60/90 days

• Outcome: What actually shipped vs. what was announced

Over time, you’ll see patterns:

• Does your company typically align systems within one quarter? Two quarters? Never?

• Which types of pivots get operationalized vs. which stay on slides?

• Which leaders actually drive roadmap change vs. which just announce strategy?

This data makes you better at prediction. And prediction is power when you’re planning a roadmap.

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The Sonos Example: Analysis in Action

Back to my Sonos speaker shopping experience. I dove into their February 2026 investor presentation. Slides 14, 15, and 16 summarize their growth strategy.

Sonos claims a $12B expansion opportunity targeting existing customers, selling direct to capture retail margin.

Three months later: New customers get 26% off. Existing customers get 15% off.

Running the diagnostics:

Level 1 - Ownership: Who owns “expansion revenue per existing customer” as the primary KPI? Unclear from public data. The new CMO just started in January (1 month before my screenshots). Metrics in last quarter’s earnings calls still emphasize new household growth, not expansion.

Level 2 - System Constraints:

• CMO announced Nov 2025, started Jan 2026 (barely 1 month in role)

• Promotional calendars are typically planned 6-12 months in advance

• Best Buy drives 14% of Sonos’ revenue — can’t undercut them without consequences

• Direct-to-consumer is 27% of revenue and declining, not growing

• Pricing coordinated across all channels to maintain retailer relationships

Level 3 - Rewarded Behavior: Earnings calls celebrate “Era-started households” (new customer acquisition). Promotional pricing coordinated across channels (retail harmony prioritized over DTC margin). Customer forums show existing customers gaming the system, waiting for sales, and deleting cookies.

Prediction:

Systems won’t align until at least Q4 2026. Existing customer expansion won’t become the actual priority until:

• CMO rebuilds go-to-market strategy (6+ months)

• Comp plans cycle to reward expansion (next fiscal year)

• Pricing strategy shifts (requires retailer renegotiation)

• Marketing campaigns and promotional calendar reflect new priorities

In the meantime, new customer acquisition will remain the priority, regardless of the stated strategy.

Am I upset with Sonos?

No. They’re operating under real constraints. They’re in transition. The CEO has been in the role for 7 months. The CMO has been in the role for 1 month.

This is what strategic pivots look like in practice: messy, slow, contradictory.

But as a customer, I learned to read the incentives.

And the incentives told me: wait for the 26% sale, comparison shop across channels, act like a new customer.

So I did.

What This Means for You

You’re probably living some version of this right now.

Leadership announced a strategic pivot. Maybe it was quarters ago. Maybe it was last week.

You’re wondering: Is this real? Should I reorganize my roadmap? Should I reprioritize the backlog? Should I push back on commitments we made under the old strategy?

Here’s my advice:

Run the three-level diagnostic. It takes 30 minutes.

Level 1: Who owns the new metric as the primary KPI? (Ask your manager, check org announcements, watch for headcount/capacity allocation changes)

Level 2: What system constraints exist? (Check roadmap commitments, team allocations, planning cycle timing, comp cycle locks)

Level 3: What’s actually rewarded? (Watch what gets demo’d, who gets promoted, what wins engineering resources when there’s a conflict)

The answers tell you everything.

If all three align quickly (ownership is clear, capacity is being reallocated, behavior is shifting in planning meetings), the pivot is real. Reorganize your roadmap accordingly.

If one or two are misaligned but leadership is actively working on them, the pivot is real but slow. Plan for a 6-12 month transition. Keep current commitments while testing the new direction.

If none of them align and nothing is changing, the pivot is performative. Protect your roadmap. Execute on what’s actually rewarded. Don’t burn your team’s focus.

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The Uncomfortable Truth

Here’s what nobody tells you about being a product leader at a growth-stage company:

Most strategic announcements don’t fully operationalize.

Not because leadership is incompetent or disingenuous. But because changing systems is hard, slow, and constrained by forces you can’t see from the middle.

Your job isn’t to reorganize your roadmap for every strategic announcement as if it’s gospel.

Your job is to:

• Read the signals correctly

• Predict what will actually happen

• Protect your team from whiplash

• Build credibility by being right about what matters

• Prioritize work that’s actually getting resourced and rewarded, not just announced

This isn’t cynical. It’s realistic.

The PMs who thrive in the messy middle aren’t the true believers who reorganize their roadmaps every quarter. They’re the pattern recognizers who know how to read incentives and plan accordingly.

Strategy isn’t what gets announced. It’s what gets operationalized.

And until systems align with strategy, the systems win every time.

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A Note for Leaders Reading This

If you’re announcing a strategic pivot, your PMs and product leaders are running these diagnostics whether you know it or not.

The best thing you can do is get ahead of it:

• Explicitly state who owns the new metric as primary KPI

• Allocate headcount and engineering capacity to support it

• Share the timeline for system changes (roadmap shifts, resource reallocation, comp changes)

• Acknowledge the constraints and what can’t change in the short term

• Be transparent about what existing commitments you’re willing to renegotiate

Your PMs aren’t asking for perfection. They’re asking for clarity so they can make good decisions about how to prioritize their roadmap and allocate their team’s finite capacity.

Give them that clarity, and they’ll execute. Leave them guessing, and they’ll predict based on patterns—and their predictions might not be what you want.

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But here’s what was missing: what to do when you don’t have time for all six steps. Because in the real world, you almost never do.

Leadership wanted answers yesterday and you’re still investigating why conversion dropped today. Or you’re debugging a metrics spike while also shipping a feature, running a hiring process, and putting out three other fires.

The frameworks everyone shares assume you have the luxury of time and resources to “get it right.” But most product leaders work in what I call the messy middle. You don’t have unlimited resources. You have competing priorities. Every hour you spend investigating one metric is an hour you’re not spending on something else that also matters.

So the question isn’t just “how do I investigate metrics?”, it actually is “what does good enough look like for THIS situation?”

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The Trap of “Complete Analysis”

Most product leaders I work with fall into the same trap. They think incomplete analysis risks making them looking incompetent.

They’ve been trained as individual contributors to be thorough, to cross all t’s and dot all i’s. That instinct served them well when they were PMs building features. But as they move into leadership roles, that same instinct becomes a liability.

Because here’s what actually tanks their credibility: spending three days to get to 95% confidence when your exec team needed to make a decision two days ago with 70% confidence.

The skill that separates good product leaders from great ones isn’t just knowing how to investigate deeply. It’s possessing the judgment to know what level of investigation is appropriate for each situation.

What Leadership Actually Needs

When your VP asks for answers by Monday, they’re almost never asking for 100% certainty.

They’re asking for your best thinking with the time and information you have.

What they actually need:

• Confirmation the metric change is real (not a data issue or seasonality)

• Your top 2-3 hypotheses about what happened

• Which hypothesis seems most likely and why

• What you’re doing next to confirm

• When you’ll have more certainty

Notice what’s NOT on that list: a complete root cause analysis with every edge case investigated.

This is 4-6 hours of work, not 3-4 days.

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A Story About Getting It Wrong

Friday, 4:30 PM. A Director I advise discovered revenue had dropped 8% that week.

Her VP’s response to the status update: “I need answers before I walk into Monday’s exec meeting. What happened and what are we doing about it?”

My phone at 8 PM: “This is impossible. My VP wants me to work all weekend and I STILL won’t have time to finish the analysis properly. I’ve given him wrong answers before and it’s affected my credibility already. But if I don’t give him something before he walks into his meeting Monday morning, I will look totally incompetent. How do I win?”

I could hear the panic in her message. She was trapped between two fears: looking incompetent for sharing an incomplete analysis and potentially an incorrect recommendation, or looking incompetent for having nothing to say.

But she was asking the wrong question.

The question should not have been “How do I finish the analysis by Monday?”

It should have been “What does my VP actually need to walk into that room with?”

The 4-Hour Version

Here's the framework I gave her:

30 minutes: Confirm the drop is real

1. Check for data issues (logging problems, definition changes)

2. Compare to the same week every quarter in the last year, not just last week (matching the revenue cadence for her product)

3. Check if it’s across all segments or specific to one segment

1 hour: List possible drivers

1. Don’t analyze yet, just brainstorm

2. Get to 5-7 possibilities

3. Think about what changed across different functional areas that may have had an impact: engineering, product, marketing, sales, operations in her case

2 hours: Do a quick analysis on top 3

1. Not exhaustive, just directional

2. Enough to say “this seems likely” or “this seems unlikely”

3. Use existing dashboards and queries, don’t build new infrastructure

4. If the data is not easily accessible, consider how existing data can be used to rule out options at a minimum

45 minutes: Write it up

1. Here’s what we know (the drop is real, here’s the scope)

2. Here’s what we think (top hypothesis with directional evidence)

3. Here’s what we’re doing next (specific investigation plan)

4. Here’s when we’ll know more (timeline)

Then stop. You’re done.

Monday morning, she walked into her VP’s office with exactly what was needed. The VP walked into the exec meeting with a clear point of view and a plan.

Her top hypothesis was not right, but her second pick was. But even if it wasn’t, she’d given leadership what they actually needed: enough information to make decisions and a clear path to more certainty.

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The Pattern I See Repeatedly

I see this happen regularly with product leaders who are moving from IC roles into Director+ positions. As ICs, they were rewarded for thoroughness, for finding the edge cases and for being right. It was important to have all the answers before they spoke up.

As leaders, those same instincts hold them back. They spend too long investigating. They wait to share findings until they’re certain. Terse updates or routine non-updates become a trust/credibility issue where the leadership default is no progress update is bad news. They miss the window when leadership actually needs the information.

The mindset shift they need: Strategic leadership isn’t about having perfect information. It’s about knowing what level of confidence is appropriate for each decision, and how to communicate uncertainty without losing credibility.

When to Do the Full Investigation

Some situations deserve the full treatment.

Do a complete investigation when:

1. You’re making an irreversible decision based on the findings

2. There’s regulatory or legal implications

3. You have evidence of a systemic problem, not just a one-time blip

4. You have time before anyone needs to make a decision

But most metric drops don’t fit these criteria.

Most of the time, you’re dealing with:

• Week-over-week fluctuations that need monitoring

• Decisions that can be adjusted if you learn more later

• Situations where waiting three days for certainty costs more than acting on 70% confidence

• Questions from leadership who need enough information to prioritize, not perfect information to commit

The Critical Question

Before you start investigating, ask yourself: “What does good enough look like for THIS situation?”

Not what does perfect look like. Not what would you do if you had unlimited time. What does good enough look like given the time you have and the decision that needs to be made?

That question forces you to think strategically about your investigation, not just execute a process.

It forces you to ask:

1. Who needs this information and when?

2. What decision are they trying to make?

3. What level of confidence do they need?

4. What’s the cost of waiting for more certainty?

5. What’s the cost of being wrong?

These are the explicit questions that surface the most important considerations that should inform your strategy and approach.

How to Communicate Incomplete Findings

When the findings are incomplete, it’s more critical than ever to communicate what you have learned so far effectively. You don’t lose credibility by admitting uncertainty. You lose credibility by pretending to be certain when you’re not.

Here is how to do this:

1. Be clear about what you know: Lead with “We confirmed the drop is real. It’s not a data issue. It’s across all segments, not isolated to one area.”

2. Be clear about what you think: “Based on the analysis so far, it appears the drop correlates with the pricing change we made two weeks ago. We see a 15% decrease in trial-to-paid conversion since then.”

3. Be clear about what you don’t know yet: “We haven’t ruled out seasonality or the impact of the marketing campaign that also launched. We’re investigating those next.”

4. Be clear about what you’re doing: “By Wednesday, we’ll have the segmented analysis that shows if it’s the pricing change or something else. If it’s pricing, I’ll have a recommendation on whether to revert or adjust.”

5. Be clear about the trade-off: “We could spend another week to be 95% certain, or we could make a call now with 70% confidence. Given that we’re mid-quarter and each week costs us approximately $X in revenue, I recommend we make the call now.”

That’s leadership. That’s strategic communication. That’s what actually builds credibility.

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What This Looks Like in Practice

After that Friday night conversation, the Director sent me a message on Tuesday: “You were right. My VP didn’t want certainty. He wanted my best thinking. He actually said ‘I trust your judgment on this’ when I walked him through the hypotheses. I’ve been chasing the wrong thing all along.”

She’d been trying to be the smartest person in the room by having all the answers. What her VP actually valued was her judgment about what mattered and what didn’t.

That’s the shift from doing the work to leading the work. From executing frameworks to applying strategic judgment about when frameworks matter.

The Real Skill

The real skill is knowing what good enough looks like for each situation. It lies in being able to communicate uncertainty without losing credibility.

That’s what I call strategic metric investigation. Not just following a process, but applying judgment about what level of process is appropriate given your constraints.

The question is “What does good enough look like for THIS situation, given the time I have, the decision that needs to be made, and the cost of waiting for more certainty?”

Answer that question first. Then choose your investigation approach.

That’s strategic judgment. And it’s what separates good product leaders from great ones.

If you have a question or a topic in mind, drop it here. I often pick questions from my reader mailbag to address via a post.

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I help product leaders in resource-constrained environments develop pattern recognition, strategic communication, and judgment under uncertainty. If this resonates, you can find more at productunblocked.com.

Earlier this year, I started working with Sarah, a VP Product at a Series C SaaS company, in both an advisory and coaching capacity. In our first advisory session with her leadership team, we reviewed their strategic initiatives. In our first one-on-one coaching session the next week, Sarah showed me something that had her worried.

Her Notion workspace had 47 research spikes documented over six months. Each one meticulously captured AI conversation transcripts, key findings, and useful links.

“I’ve gotten so good at gathering insights,” she told me. “I can research any topic in 15 minutes now. But I’m not making decisions any faster.”

She pulled up a spike from four months earlier about AI coding tools and development velocity. Comprehensive notes. Thoughtful analysis. I was impressed. She then mentioned that the topic had been added to the agenda for the exec. team meeting next week, but does not feel prepared.

“I knew I’d done this before,” she said, scrolling through her notes, “but I couldn’t remember what I’d concluded. Just what I’d learned. So I updated my notes with the latest information to prepare for the meeting.”

That’s when I realized: Sarah had optimized for exploration, not conviction.

The Wake-Up Call

Two weeks later, during our advisory check-in with the executive team, Sarah’s CEO shared a competitor’s product announcement. Three features Sarah’s team had been planning for Q3 and Q4 had already been shipped.

After the meeting, the CEO pulled Sarah aside: “They’re using AI to build faster. Why aren’t we? What’s our strategy here?”

In our coaching session that afternoon, Sarah was visibly frustrated. She pulled up her research from four months ago and walked me through them:

Research Topic: “How should we think about AI-generated code in our development workflow?”

Key Findings:

• GitHub Copilot can increase developer productivity 30-50%

• Cursor and other AI coding tools accelerating adoption

• Some companies shipping features in days instead of weeks

• Quality concerns around AI-generated code

• Security/licensing questions still evolving

• Testing becomes even more critical

Conclusion: “Worth revisiting as tools mature and best practices emerge”

The notes told her what AI coding tools could do. They didn’t tell her what her company should do about it.

“I have all this research,” Sarah said, “but when the CEO asks ‘what should we do?’ I don’t have an answer.”

The Real Problem

In our next coaching session, we dug into why this kept happening.

Sarah’s research spikes were thorough. But they were optimized for learning, not deciding.

When she documented her exploration, she captured:

• What the technology could do

• What other companies were doing

• What the risks and trade-offs were

• What integrating the technology entailed

• Typical costs and timelines

What she didn’t capture:

• What she believed

• Why she believed it

• What would change her mind

Without decision architecture, every spike was a one-time knowledge exploration. When the topic resurfaced months later, the research had to start over because there was no scaffold to build on.

“You’re creating a library of things you’ve learned,” I told her, “not a framework for what you believe and what needs to be true to act.”

The Complexity She Was Actually Facing

As we prepared for the next executive advisory session, Sarah and I mapped out the real tensions in the AI coding tools decision. This wasn’t simple.

Engineering’s Perspective: “We want to use these tools—they’re genuinely powerful. But we need guard rails. Who’s responsible when AI-generated code has a security vulnerability? What happens to our code review standards?”

Sarah’s Product Perspective: “This isn’t just about velocity. If we can generate code faster, we have a choice: ship more features, or finally address the technical debt that’s been slowing us down for two years. Both matter. We can’t do both. Which creates more value?”

Quality/Engineering Lead’s Concern: “Our test coverage is already struggling to keep up with our pace. If we’re generating code faster, how do we ensure we’re not just shipping bugs faster? Our testing infrastructure wasn’t built for AI-speed development.”

CEO’s Pressure: “Our competitors are moving at AI speed. We need to match that or we’ll fall behind. But I also don’t want to sacrifice the quality that’s been our competitive advantage.”

Customer Reality: “They don’t care how it’s built. They care that it works reliably and solves their problems.”

In our advisory session, I could see the tension in the room. Everyone wanted to move faster. No one wanted to break things. The decision felt urgent and overwhelming at the same time.

“This is a classic messy middle problem,” I told the team. “You can’t just adopt everything because competitors are. You can’t ignore it because there are risks. You need conviction about what’s right for your situation.”

What We Changed: Decision-First Spikes

In our next coaching session, Sarah and I redesigned her approach. Before her next exploration, we would frame the decision first.

I walked her through three questions:

1. What decision does this inform?

Sarah’s first instinct: “Should we adopt AI coding tools?”

“Too vague,” I pushed back. “What are the actual choices on the table?”

After some discussion, Sarah framed it more precisely:

Decision: How aggressively should we adopt AI coding tools in our development workflow: conservative (limited pilot), moderate (team-by-team adoption with guard rails), or aggressive (company-wide standard with velocity targets)?

Now we had something specific to build conviction around.

2. What would need to be true for each path?

We mapped out what Sarah would need to believe for each approach:

For Conservative Approach (limited pilot):

• Current velocity is sufficient for competitive positioning

• Quality and security risks outweigh speed benefits in the short term

• We can learn from other companies’ mistakes before scaling

• Our technical debt problem won’t get worse while we wait

For Moderate Approach (team-by-team with guard rails):

• Some domains are safer for AI acceleration than others (new features vs. core infrastructure)

• We can establish and enforce guard rails as we scale

• Mixed adoption won’t create code quality inconsistency

• We can dedicate AI velocity gains to EITHER new features OR tech debt systematically

For Aggressive Approach (company-wide with velocity targets):

• Speed to market is an existential competitive advantage right now

• We can manage quality/security with improved testing infrastructure

• Team is ready for rapid workflow change

• We can use velocity gains for BOTH new features AND tech debt

“This is good,” Sarah said, “but how do I figure out which approach to recommend?”

3. What would make you change your mind?

We identified the evidence that would push the decision one way or another:

Conservative Approach:

Deal Breakers:

• Competitors demonstrably pulling 2+ quarters ahead on features

• Customer churn attributable to slow feature delivery

• Team survey shows >70% ready and eager for AI tools

Deal Sealers:

• Current velocity meets business objectives

• Competitive positioning remains strong

• Quality or security concerns emerge in other companies’ AI adoption

• Team prefers to learn from others before committing

Moderate Approach:

Deal Breakers:

• Cannot establish effective guard rails that work consistently

• Quality inconsistency across AI-enabled vs. non-enabled teams

• Split adoption creates significant team friction or confusion

Deal Sealers:

• Pilot shows 30-40% velocity increase with quality maintained

• Some domains prove safer for AI acceleration than others

• 60/40 features/debt split achievable with structured discipline

• Team readiness mixed but workable (50-70% positive)

Aggressive Approach:

Deal Breakers:

• Security audit reveals unacceptable risks in AI-generated code

• More than 30% of team strongly resists workflow change

• Testing infrastructure cannot keep pace with code velocity

• Legal identifies licensing/IP concerns we cannot mitigate

Deal Sealers:

• Pilot shows 40%+ velocity increase without quality degradation

• Security approves with manageable guard rails

• Team readiness above 80%

• 60/40 features/debt split works naturally without heavy oversight

• Competitive pressure demands immediate response

“Now this,” Sarah said, “this I can actually work with. Each path has clear conditions.”

The Structure That Persisted

After Sarah did her research spike with AI (which took about 20 minutes), she didn’t just document findings. She captured the decision scaffold:

Decision Being Informed:
How aggressively to adopt AI coding tools: conservative (limited pilot), moderate (team-by-team), or aggressive (company-wide)

Evidence Gathered:

• 3 similar-stage companies using AI tools: 2 report 35-50% velocity increase, 1 had quality issues and pulled back

• Security concerns manageable with proper code review + automated scanning

• Testing infrastructure needs upgrade regardless (already a Q3 initiative)

• Team survey: 65% excited, 25% cautiously optimistic, 10% skeptical

• Current tech debt backlog: estimated 8 engineer-months of work

• Legal: licensing concerns exist but manageable with tool selection

• Security scanning cost: $50K/year (already budgeted)

What This Evidence Tells Us:

• Team resistance lower than feared (10% vs. 30% threshold) → doesn’t rule out any approach

• Velocity gains are real but quality risks exist → moderate makes sense as starting point

• Security/legal concerns are manageable → doesn’t block any path

• Testing infrastructure needs work but timeline aligns → not a blocker

Still Need to Validate:

• Can we maintain quality with higher velocity? (need pilot data)

• Can we establish discipline to split gains 60/40 features/debt? (need commitment from leadership)

• Will our current testing infrastructure support moderate adoption? (need technical assessment)

Recommendation:
Start with moderate approach:

• 60-day pilot with 2 teams (1 on new features, 1 on tech debt refactoring)

• Measure velocity, quality, team satisfaction

• Test whether we can maintain features vs. debt allocation discipline

• Decision point at day 60: stay moderate or move to aggressive based on pilot results

Next Steps When Revisited: Not “what’s changed in AI tools?” but:

• Did pilot validate velocity gains without quality loss?

• Could we maintain features/debt discipline with clear guidelines?

• Have competitive dynamics changed our urgency?

• Is testing infrastructure upgrade complete?

Has team sentiment shifted after seeing pilot results?

If you’re facing similar challenges—research without conviction, stakeholder ambiguity, or decisions that keep resurfacing—this framework can help.

Want help building decision frameworks?

The Advisory Session That Changed Everything

Six weeks later, we reconvened the executive team for Sarah’s recommendation.

Sarah opened: “Thank you for your time. Six weeks ago, you asked about our AI coding strategy. Today I’m recommending a path forward based on data from our pilot.”

Her presentation was crisp:

What We Tested:

• 2 teams, 60 days

• Team A: Used AI tools for new feature development

• Team B: Used AI tools for tech debt reduction

Results:

• Team A velocity: +42% (shipped 2.3x more feature work)

• Team B velocity: +38% (cleared 2.1x more tech debt items)

• Bug escape rate: No increase (actually slight decrease)

• Security incidents: Zero (with new scanning in place)

• Team satisfaction: 8.2/10 (up from baseline 7.1/10)

• Code review time: +15% (expected, manageable)

What We Learned:

• The velocity gains are real and sustainable

• Quality doesn’t suffer with proper guard rails

• Teams can maintain discipline on features vs. debt allocation

• Testing infrastructure needs the planned upgrade (doesn’t block this)

Recommendation: Moderate Approach with Q3 Expansion

Now (Q2):

• Expand to 4 more teams (total 6 of 12 teams)

• Maintain 60/40 split: features/tech debt

• Implement guard rails company-wide

Q3 Decision Point:

• If 6-team results hold → expand to aggressive (all 12 teams)

• If quality degrades → stay at moderate

• Reassess features/debt split based on Q2 outcomes

What Would Change This Recommendation:

• Quality metrics decline in expanded pilot

• Testing infrastructure upgrade delays

• Security incident related to AI-generated code

• Team resistance increases above 20%

The CEO leaned forward: “How confident are you in this?”

Sarah didn’t hesitate: “Highly confident in the moderate approach. The pilot data is strong. We’ll know in Q3 whether aggressive makes sense based on what we learn from 6 teams.”

The CFO asked: “What’s the ROI?”

“At moderate adoption,” Sarah answered, “we’re looking at 4-6 additional engineer-months of capacity per quarter. We can use that to ship 2-3 more features AND clear roughly 40% of our tech debt backlog over the next year. At aggressive adoption, those numbers roughly double.”

The decision took five minutes. Approved unanimously.

After the meeting, the CEO pulled me aside: “Sarah’s gotten really decisive. What changed?”

The Results Three Months Later

In our Q3 advisory check-in, Sarah shared the outcomes:

Moderate Adoption (6 of 12 teams):

• Velocity gains holding at 40% average

• Quality metrics stable

• Tech debt backlog reduced 35%

• 3 features shipped that wouldn’t have fit in timeline otherwise

• Team satisfaction remained high

Organizational Learning: The company:

• Established AI coding standards and guard rails

• Created “velocity allocation” framework (features vs. debt)

• Upgraded testing infrastructure (already needed, now critical)

• Developed training program for remaining teams

• Built confidence in using AI as accelerator, not crutch

Most importantly: Sarah’s credibility as a product leader grew significantly.

The CEO’s feedback in her review: “Sarah has developed the ability to navigate complex, high-stakes decisions with clarity and conviction. She doesn’t just present options—she builds cases backed by data and clear decision frameworks.”

Why This Approach Works

Sarah’s transformation wasn’t about researching faster. It was about building conviction systematically.

Her old approach:

• Gather information

• Document findings

• Hope insights lead to decisions

Her new approach:

• Frame the decision first

• Identify what needs to be true

• Gather evidence to validate or invalidate beliefs

• Build conviction that persists over time

The difference for her stakeholders was dramatic.

Before: “Here’s what I learned about AI coding tools. There are pros and cons. What do you think we should do?”

After: “Here’s what we need to decide, here’s what we tested, here’s what we learned, here’s my recommendation, and here’s what would make me change my mind.”

The first creates ambiguity. The second creates confidence.

When to Use Decision-First Spikes

In our coaching work together, Sarah now uses this approach selectively.

She uses decision-first spikes when:

• Multiple stakeholders need to align

• The decision involves significant investment (time, money, or political capital)

• The topic will resurface in 3-6 months

• She needs conviction, not just information

• There are multiple valid paths depending on what you believe

She skips it when:

• She’s satisfying curiosity with no decision pending

• The decision is clearly hers alone and low-stakes

• She needs landscape understanding before she can even frame a decision

• The exploration is for learning, not deciding

The Real Leverage of AI

In our coaching sessions, Sarah and I often discuss what AI actually enables.

“AI doesn’t just help me explore faster,” Sarah told me recently. “It helps me think more clearly about what I’m trying to decide.”

She’s right. Here’s why:

The discipline of framing “what would need to be true” before you start forces clarity. You can’t hide behind vague goals or open-ended exploration.

The speed of getting answers lets you test multiple decision frames quickly. If your first frame doesn’t yield useful validation criteria, you can reframe and try again in minutes, not days.

The ability to have AI stress-test your logic builds conviction faster. Sarah now regularly asks: “What am I missing in this decision framework?” or “What would a skeptic challenge in my assumptions?” She gets thoughtful pushback in real time, which strengthens her thinking before she enters a stakeholder conversation.

This is the real power of AI-augmented strategic thinking. It’s not just execution velocity. It’s clarity velocity.

Try This

If you use AI for strategic exploration, here’s what I recommend:

Next time you’re about to ask AI a research question, pause.

Ask yourself: “What decision am I actually trying to inform?”

Then ask: “What would need to be true to say yes? What would make me say no?”

Then ask AI to help you validate or invalidate those beliefs.

See if it changes what you learn and how you use it.

I suspect you’ll find what Sarah found: you’re not just exploring faster. You’re deciding smarter.

And in the messy middle, that’s what actually matters.

I work with leaders operating in resource constrained environments in both advisory and coaching capacities. If you want to build this capability in yourself or your team, let’s talk.

Work with me: Advisory & Coaching

What decisions are you circling without clear conviction? What would need to be true to move forward? I’d love to hear what you’re working through.

It’s performance review season, time to write self-appraisals.

One year, one of my best product people wrote a self-appraisal that opened with “Shipped onboarding flow.” What she didn’t mention: she navigated three competing stakeholder priorities, translated technical debt into business risk for execs, and found a solution that served engineering velocity AND customer activation AND sales enablement.

Luckily for her, I knew all this. And she submitted early enough for me to send it back with coaching: “Rewrite this so it shows the strategic thinking, not just the task completion.”

She rewrote it. I took the new version to our calibration meeting where we decided on promotions. She got hers.

I’ve watched this pattern play out dozens of times. Talented senior product people undersell their accomplishments because they don’t know how to articulate strategic thinking. They do the work but just don’t document it in ways that show pattern recognition, multi-stakeholder strategy, or adaptive decision-making.

And when performance review time comes, their managers describe them as “solid executors who ship features” instead of “strategic leaders who see around corners.”

The Problem Isn’t the Work—It’s How You Frame It

Most well know PM advice comes from Big Tech where resources are less constrained and leadership has already established clear success metrics that make sense for companies at that size and scale. But when you who work in what I call the “messy middle” on resource-constrained teams, environments where every feature needs to serve multiple objectives simultaneously, it’s particularly important to frame one’s contribution thoughtfully.

You’re making high-stakes decisions with incomplete information. You’re navigating competing stakeholder priorities. You’re finding creative solutions when you can’t just “throw resources at it.” That’s strategic work.

But if you describe it as “shipped feature X” or “communicated with stakeholders,” your manager won’t know how to advocate for you.

Over the years, I developed five frameworks to help product people document the strategic thinking behind their work. Not the tasks they completed but the capabilities they demonstrated.

These are the things that separate senior product leaders from the rest. And they’re exactly what your manager needs to justify a promotion.

Here’s what to capture and how to frame, so your performance review reflects your strategic approach.

1. The Multi-Stakeholder Win

What most PMs write: “Shipped new onboarding flow. Increased registration by 12%.”

What strategic PMs document:

“Redesigned onboarding flow to solve three problems simultaneously:

Customer: Cut onboarding steps from 8 to 3, improving mobile registration rates

Business: Created foundation for international expansion (internationalization and localization)

Engineering: Reduced technical debt by sunsetting legacy code”

See the difference? The first version describes task completion. The second describes strategic thinking. One feature serving multiple objectives is the ultimate “messy middle” PM skill. It’s important to explicitly articulate it.

How to document this:

Keep a running doc throughout the review period. Every time you ship something, capture:

• Customer win: What pain point did this address?

• Business win: What metric did this move or what strategic option did it create?

• Engineering win: What constraint or technical problem did it solve?

Add to it weekly. By review time, you’ll have a portfolio of strategic thinking, not just a list of launched features.

This is pattern recognition in action: seeing opportunities where adjacent pieces (technology capabilities, customer behaviors, business constraints) fit together into solutions that create cascading value across multiple stakeholders.

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2. Translation Moments Over Task Lists

What most PMs write: “Communicated with stakeholders about roadmap changes.”

What strategic PMs document:

“Translated technical debt into business risk:

Engineering said: ‘Our test coverage is at 40% and the codebase is fragile’

I reframed for exec team: ‘Every new feature now takes 2x longer to ship and carries 30% risk of customer-facing bugs. We’re trading speed today for compounding slowness tomorrow.’

Result: Got 2 sprint cycles for refactoring approved.”

This is strategic communication—the skill that actually gets you promoted. You’re not just moving information around. You’re translating complexity into clarity for different audiences. You’re framing technical realities in terms of business outcomes. You’re helping executives understand why something matters.

Most PMs do this in the moment and forget about it by review time.

How to document this:

Capture 3-5 translation moments throughout the year:

• Situation: What needed to be communicated?

• Technical reality: What did engineering/design/data actually say?

• How you reframed it: What language did you use for this specific audience?

• Why it worked: What made this land?

• Outcome: What decision was made, what alignment was gained, what resource was approved?

Each one proves you can operate at the strategic level—connecting technical complexity to business value in ways that get things unstuck.

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3. The “I Changed My Mind Because...” Collection

This one feels counter-intuitive. But it’s powerful.

What most PMs hide: Times they changed direction, killed features, or reversed decisions. They see it as weakness.

What strategic PMs highlight:

“Q2 Planning: I Changed My Mind on Notifications Strategy

Originally proposed: Push notifications for every user action

Three weeks in, I killed it based on:

• User research showing notification fatigue in target segment

• Engineering feedback that the backend wouldn’t scale to our growth projections

• Support data revealing our users valued ‘calm’ product experience

Pivoted to: Smart digest (daily summary) + critical-only real-time alerts

Result: 40% better engagement than original plan would have achieved

What this showed: I optimized for outcomes over being right. I incorporated feedback fast. I didn’t fall in love with my first idea.”

Changing your mind based on new information isn’t weakness. It’s judgment.

It shows you’re not attached to ego, you’re attached to impact. It demonstrates adaptive thinking—a capability that’s critical when you’re operating under uncertainty.

How to document this:

Keep 2-3 examples ready:

• Decision: What did you originally propose?

• New information: What data or feedback emerged?

• Why you changed course: What was your reasoning?

• New approach: What did you do instead?

• Outcome: Why was the pivot right?

• Learning: What did this reveal about your strategic thinking?

“Strong opinions, loosely held” is a key criterion for promotion to senior roles. This framework proves you have it.

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4. Product Decisions Under Uncertainty

What most PMs write: “Made decision to deprioritize Feature X.”

What strategic PMs document:

“Decision: Deprioritized Social Sharing Feature

What I knew at decision time:

• 60% of user interviews mentioned wanting it

• Engineering estimate: 8 weeks

• We had 6 weeks until competitor launched similar feature

• Analytics showed only 12% of users completed current sharing flow

What I didn’t know:

• Whether faster launch would actually matter competitively

• If interview feedback would translate to actual usage

The constraint that shaped my call:

• Shipping fast but broken would hurt brand trust more than being second

• Our users valued reliability over novelty (based on churn analysis)

My decision:

• Deprioritize social sharing

• Ship performance improvements instead

• Let competitor go first, learn from their mistakes

What happened:

• Competitor’s version attracted particularly vocal criticism, flooded their support

• Our version (shipped 2 months later) had 31% adoption because we learned from their UX failures

• Performance improvements drove our best NPS quarter

What I’d do differently with perfect information:

• Still the same call. Speed without quality doesn’t serve our users.”

This is critical because most performance reviews judge decisions with hindsight bias. But that’s not how strategic thinking works. You made a decision with incomplete information, under real constraints, optimizing for multiple objectives. This framework shows your reasoning at decision time, which is what actually matters.

How to document this:

Pick 1-2 big decisions from the review period:

• Decision: What did you choose?

• What you knew: What data was available?

• What you didn’t know: What uncertainty did you face?

• Key constraint: What shaped your call?

• Your reasoning: Why did you choose this path?

• Outcome: What actually happened?

• Hindsight: What would you do with perfect information?

This reframes “mistakes” as strategic thinking under real-world conditions. It shows you can operate effectively when you don’t have all the answers—which is the reality of product leadership.

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5. The “Constraint That Made Us Better” Story

What most PMs write: “Worked with limited resources.”

What strategic PMs document:

“How Having No Design Support Made Us Ship Faster

The constraint:

• Design team was at capacity for 6 weeks

• Feature was needed for customer pilot in 4 weeks

• Waiting meant losing $150K pilot opportunity

What I did:

• Learned enough Figma to create low-fi mockups

• Ran mockups through 8 user tests myself

• Partnered with an engineer to iterate directly in code

• Used design system components (no custom work)

What happened:

• Shipped in 3.5 weeks

• Customer pilot converted to $150K annual contract

• Learned I could move faster by reducing handoffs

• Built stronger engineering partnership through collaboration

The unexpected win:

• This became our model for future small bets

• We now ship experiments 40% faster using same approach

• Design team focuses on high-impact strategic work”

Every PM has resource constraints.

The difference between complaining about constraints and demonstrating strategic thinking is how you frame them.

A constraint became a forcing function. It pushed you to find a creative solution. And that solution created a pattern you could replicate.

How to document this:

Pick one constraint story:

• The constraint: What didn’t you have?

• The stakes: Why did it matter?

• Your approach: How did you work around it?

• The outcome: What did you achieve?

• The pattern: What did you learn to replicate?

One well-documented constraint story beats ten “I worked hard” statements.

It shows resourcefulness, creativity, and the ability to find solutions when you can’t just throw resources at the problem. That’s exactly what execs want in senior leaders.

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Why This Matters Right Now

Your performance review conversation is happening whether you document this stuff or not.

The question is: Will your manager describe you as “a competent PM who ships features” or “a strategic leader who sees around corners and finds solutions where multiple stakeholders win”?

If you don’t capture the strategic thinking behind your work, no one else will.

Your manager sees the features you shipped. They don’t necessarily see the three stakeholder problems you solved simultaneously. They don’t see the moments you translated technical complexity into business language that got resources approved. They don’t see the constraint that forced you to find a better solution.

Here’s what happens if you don’t do this:

You’ll walk into your performance review with a list of shipped features. Your manager will say “great execution” and give you a solid rating. You’ll wonder why you didn’t get promoted when you know you’re doing strategic work. And next year, you’ll have the same conversation.

Here’s what happens if you do this:

You’ll submit a self-appraisal that shows pattern recognition, strategic communication, and adaptive thinking. Your manager will have the language they need to advocate for you. And when the promotion conversation happens, they’ll have concrete examples of why you’re ready.

Start This Week

You don’t need to document everything all at once. Pick one framework from this list. Document one example this week.

Did you ship something that solved problems for multiple stakeholders? That’s Framework #1.

Did you translate something complex into language that got an executive to say yes? That’s Framework #2.

Did you change your mind based on new information? That’s Framework #3.

Did you make a decision under uncertainty? That’s Framework #4.

Did a constraint force you to find a better solution? That’s Framework #5.

Capture it while it’s fresh. Add to your doc weekly.

By performance review time, you’ll have a portfolio that shows the strategic PM you actually are—not just the features you shipped.

Quick question: What’s the biggest challenge you face with self-appraisals? Hit reply and let me know - I read every response and often share insights in future posts.

Want Help With This?

I work with product leaders 1:1 on strategic communication to document and articulate your strategic thinking on a limited basis.

We’ll work through:

• Your actual performance review using these frameworks

• How to translate product instincts into executive-ready narratives

• How to position yourself for the next level

About me: I help product leaders see around corners through pattern recognition, strategic communication, and AI-augmented thinking. I’ve been VP/GM at Vonage, Mapbox, and SiriusXM, and I currently teach AI + Business Communication at Diablo Valley College. I work with product leaders navigating the “messy middle”—resource-constrained environments where every decision needs to serve multiple objectives.

That resonated with a lot of people. But one comment went deeper.

One commenter Victoria said their frustration wasn’t just about people being emotionally invested in their projects. It was about people actively pursuing personal agendas and advancement at the expense of what’s right for the business and customers. “I want to grow the business” conflicted with “I want to advance my personal agenda.” “I want to do what’s right for customers” conflicted with “I want to look good to my boss.”

Then she added another layer: “There’s not enough space for everyone here, so I’ve got to make sure my chances are safe even if it means harming others to get there.”

That’s the real problem. Advancement opportunities are scarce.

Not everyone who wants to be a senior leader can become one.

Not everyone who wants it deserves to be one.

And when individual advancement incentives conflict with what’s right for the business, some people will actively undermine others to look good by comparison.

I don’t think most people are consciously thinking “I’ll harm others to get ahead.” They’re thinking things like “I need to make my work visible” or “I need to protect my reputation” or “I need to make sure I get credit for this.” But in a system where advancement is scarce, these defensive moves can undermine others.

I’ve seen this pattern play out across companies. Someone gets publicly supported but privately undermined. Resources get withheld. Credit gets redirected. And when the person can’t deliver without the support they were promised, they’re blamed for the failure - not the people who set them up to fail.

The company can’t tell the difference between someone who’s actually underperforming and someone who’s been made to look incompetent by others.

So what do you do when you’re in this situation?

I’ve thought about this a lot. Here’s what I’ve learned.

See It Clearly First

You can’t navigate what you can’t see. The first step is recognizing you’re in a broken incentive structure.

Here are the signs:

1. Visibility matters more than results. People spend more time promoting their work than doing good work. Whoever tells the best story wins, regardless of actual impact. Leadership rewards the person who “owns the narrative,” even when they did not deliver the outcome.

2. Credit gets stolen or diluted. Your ideas get repackaged as someone else’s. Others take credit for work you did, and when you point it out, you’re told you’re told you’re territorial or not a team player.

3. Alliances matter more than competence. Who you know matters more than what you deliver. Political savvy beats technical excellence. The person who manages up wins over the person who executes well.

4. Defensive behavior is normalized. People hoard information to stay valuable. Collaboration feels risky because someone might take credit. Helping others feels like weakening one’s own position.

5. Zero-sum thinking dominates. Someone else’s win feels like your loss. Promotions create resentment instead of inspiration. Success is seen as limited, not expandable. The pie is fixed, and everyone’s fighting for their slice.

   

Here’s the critical question: Is this just normal organizational friction, or is the incentive structure fundamentally broken?

Here’s how to tell:

In normal friction, you see occasional conflicts, but good work generally wins over time. People who deliver get recognized. Collaboration is rewarded. Mistakes are learning opportunities.

In a broken system, you consistently see incompetent people advance while competent people stall or leave. Politics matter more than performance. The people getting promoted are the ones you don’t respect. And the people you do respect are either leaving or becoming cynical.

Once you see it clearly, you can make strategic choices instead of just reacting.

If You’re A Leader, This Should Worry You

When your incentive structure rewards undermining behavior, you lose your best people. They leave. And you’re left with people who are good at politics, not necessarily good at their work.

Here’s what a broken evaluation looks like:

1. You’re rewarding visibility over impact. Promotions go to people who are good at self-promotion. Quiet, effective contributors get overlooked. The person who talks about the work gets credit over the person who does it.

2. You’re making credit zero-sum. Only one person can “own” a win. Team success gets attributed to whoever managed up best. Collaboration becomes risky because someone will take the credit.

3. You’re conflating confidence with competence. The person who sounds most certain wins. Thoughtful responses like “I don’t know, let me investigate” get punished. Admitting mistakes equals career damage.

So what does good evaluation actually require?

1. Make credit non-zero-sum. Multiple people can win from the same success. Explicitly credit collaboration, not just individual heroics. Ask yourself and your leadership team: “Who else contributed to this that I’m not seeing?” Make it a regular practice.

2. Evaluate based on outcomes, not narrative. Look at what actually happened, not who told the best story about it. Track results over time, not just who spoke up in the meeting. Get input from multiple peers and reports including skips. The person doing the work often sees things leadership doesn’t.

3. Reward collaboration over competition. Explicitly recognize people who make others better. Penalize credit-stealing and undermining when you see it. Make helping others a path to advancement, not a risk to your own career.

4. Expand the pie. Create more paths to advancement. Offer lateral moves with prestige and compensation. Don’t make every ambitious person compete for the same three VP slots. Recognize that not everyone wants to manage, but everyone wants to grow.

Here’s the hard truth for leaders: If your incentive structure is broken, your best people are already looking for the exit. And the people who stay? They’re optimizing for the game you’re actually rewarding, not the one you think you designed.

Your Actual Options If You’re Experiencing This

If you’re on the receiving end of this, here are your real options. I’m not going to sugarcoat it. None of them are great.

1. You can fight back politically. This means managing up aggressively. Making your work visible constantly. Building alliances with powerful people. Defending your territory. Maybe even undermining the people who are undermining you (if you’re willing to go there.) The cost? It’s exhausting. It’s soul-crushing if you hate politics. It requires skills you may not have or want to develop. You might become what you hate. And even if you win, you’re still in a broken system.

2. You can build stronger alliances earlier. Cultivate advocates who know your work. Have people in your corner before the attacks start. Build relationships with leadership. Create visibility proactively, not reactively. This might work because it’s harder to undermine someone with strong advocates. Leadership hears the truth from people they trust. You’re not defenseless.

   But it might not work because it still requires political savvy and time. If the system is truly broken, alliances may not matter. You’re still playing the game.

3. You can document everything. Track your contributions obsessively. Save receipts on who did what. Have evidence when credit gets stolen. Build a paper trail. This might work because it’s harder for people to gaslight you. You have proof if you need to escalate. It protects against blatant lies.

   But here’s why this often doesn’t work: In political environments, perception matters more than documentation. Leadership often doesn’t want to dig into “he said, she said” situations. And honestly? Documenting everything can make you look defensive even when you’re right.

4. You can leave before it gets worse. Find another role before the damage spreads. Exit while your reputation is still intact. Cut your losses. This might be the right move because some systems are too broken to fix from inside. Your mental health and career trajectory matter. You can’t win a rigged game.

   But it feels wrong because it feels like quitting. It feels like letting the bad actors win. You wanted to succeed here.

Here’s the reality: Sometimes there’s no good answer. You can try to fight, document, build alliances. But if the incentive structure fundamentally rewards undermining behavior, the person who refuses to play that game often loses.

The Hard Truth: Sometimes The Right Move Is To Leave

Most career advice says: “Don’t quit. Fight harder. Be more strategic. Build better relationships.”

But sometimes the system is so broken that staying is losing.

How do you know?

Ask yourself these questions:

1. Is good work rewarded here, or is political skill rewarded?

2. When you look at who’s been promoted in the last year, are they people you respect for their competence or their ability to play politics?

3. When you look at who’s advanced, do you want to become them? Not just in terms of title, but in terms of how they got there and who they had to become to do it?

4. Are you becoming someone you don’t want to be in order to survive here? Are you doing things that violate your values? Are you lying awake at night thinking about work politics instead of the actual work?

5. Is the cost of staying higher than the cost of leaving? Not just financially, but emotionally, mentally, professionally?

If good work can’t win, you’re not in a meritocracy. You’re in a political system. And if you’re not good at politics, or you’re not willing to play that game, you will lose.

That’s not a character flaw. It’s strategic clarity.

The question isn’t “Am I tough enough to stay?”

The question is: “Am I in a system where good work can win, or where good work gets punished?”

If it’s the latter, leaving isn’t failure. It’s refusing to optimize for a game you don’t want to win.

Some battles aren’t worth fighting. Some systems are too broken to fix from inside. And recognizing that isn’t weakness. It’s wisdom.

So ask yourself:

Have you seen good work rewarded here in the last year? Not just praised, but actually rewarded with advancement, compensation, opportunity?

Do you trust leadership to evaluate fairly? Do they have the information they need? Do they care about getting it right?

Are the people advancing the people you’d want to become? Would you be proud to follow their path?

If the answer is no, you already know what to do.

I don’t have a neat framework for this. I don’t have a three-step process. What I can offer is a clearer lens for evaluating whether you’re in a system that can be fixed, or one you need to leave.

Sometimes the most strategic thing you can do is walk away.

What are the signs that tell you a system is fixable vs. one you need to leave?

Clarity rarely shows up fully formed. Most of the work happens quietly, in the moments you barely notice but that shape every decision.

October reminded me of this in a small but instructive way. I set out to post again on Substack, not perfectly, not consistently, just again. Four posts later, uneven and imperfect, I realized the most important work had not happened in the posts themselves. It happened in the gaps between them: the doubts, the recalibrations, and the small mental pivots that left no visible trace but shaped every sentence.

Most people see understanding as an endpoint. The well-written post. The confident decision. The impactful launch. But rarely is it sudden or obvious. It is the byproduct of hidden effort: noticing friction, surfacing inconsistencies, wrestling with tension, and letting ideas wobble until they hold.

Each post illuminated the principle in a different way:

Decision-Making Speed

One post started by highlighting my speed in decision-making. Initially, I framed it as a strength I could rely on blindly. Mid-draft, I realized that speed alone was brittle, it had not been consciously analyzed and could not be scaled or taught. I documented recent decisions and noticed three distinct modes I relied on: experimentation, values-based insight, and pattern recognition.

Lesson: Recognizing these patterns showed me that hidden effort was necessary to turn intuition into strategic judgment.

Unexpected AI Lesson

Another post, “When AI Got a Generous 7/10”, grew out of a real classroom moment. AI gave a surprisingly generous 7/10 on an exercise, and the outcome struck me immediately. I had not planned to write a post, but noticing the significance of what happened — how AI’s output interacted with human judgment — was the quiet work that transformed the moment into insight.

Lesson: Some of the clearest insights emerge spontaneously, if you pay attention to subtle signals.

Two Weeks of Nothing

This post arose from observing a team frozen by over-analysis. I noticed how waiting for certainty itself became a decision. The unseen work was seeing the pattern in real time and translating it into a teaching story that highlighted reducible versus irreducible uncertainty, opportunity costs of delay, and the importance of decision-making processes.

Lesson: Invisible observation and reflection can turn stalled situations into actionable understanding.

Slowing Down

My first post, “The Slower Pace Led Me Here”, revealed another facet of hidden effort. It began by emphasizing rest and reflection. Midway, I noticed the tension between passivity and active observation. Slowing is not just stopping; it is a deliberate act of noticing and attending. Recognizing this subtle distinction guided the essay’s structure and framing.

Lesson: Even seemingly passive moments contain rich opportunities for insight, if you notice them.

These micro-moments, invisible checks, are what produced the real understanding. They illustrate a broader principle: the visible outputs of work, essays, products, and launches are only part of the story. The hidden effort that precedes them, often unnoticed and uncelebrated, shapes their resilience and depth.

In product and leadership, this work is rarely recognized. Experienced leaders know iteration matters, they know shipping is essential. What often goes unseen is the work between iterations: challenging assumptions, tracking subtle patterns, wrestling with the friction of uncertainty. The leaders who appear decisive are rarely those who simply act quickly. They are the ones who have tested their reasoning against alternatives, exposed their ideas to challenge, and made hundreds of small, quiet adjustments repeatedly before anything becomes visible.

Making Hidden Labor Systematic

Flag points of friction early. Mark any sentence, feature, or decision that feels awkward. These moments often hide the biggest opportunities for insight.

Articulate the strongest counterargument. Before publishing, launching, or deciding, ask: if someone wanted to disprove me, what would they say? Can I explain it better than they can?

Revisit flagged moments first. Iteration is most effective when you start with the areas of highest tension. The subtle, uncomfortable moments often reveal the largest opportunity for improvement.

Document patterns quietly. Track recurring doubts, surprising reactions, and subtle insights. Over time, these accumulate into a deep understanding that guides future decisions.

October was not about perfection. My posts were uneven, sometimes awkward, occasionally vulnerable. But they became a lens through which I practiced the hidden effort that produces clarity. The lesson was not that I shipped imperfectly; it was that the quiet work between posts taught me more about reasoning, judgment, and insight than any single “perfect” essay ever could.

The most visible work — essays, launches, decisions — gets the credit. But the hidden work matters far more. It is the effort that shapes opinions, refines judgment, and gives visible outputs their depth. Skipping it leaves ideas brittle. Doing it quietly and consistently builds resilience and nuance.

As November begins, the goal is not to perfect every post or project. It is to continue practicing the hidden work: noticing, questioning, revising, iterating quietly. Insights and good judgment will follow, not because they are aimed at directly, but because they are earned in the gaps where most people are not looking.

October reminded me that the most valuable work is often invisible. The path to clarity is paved in the quiet, uncelebrated effort that precedes every confident decision.

A friend told me recently they admire how quickly I make decisions. I thanked them. Then spent three days wondering if I actually know what I’m doing.

I make decisions fast. Always have. Restaurant choices, career moves, business strategy - I am rarely stuck in analysis paralysis, I bias towards action. While other people are still gathering data and weighing their options, I’ve already picked a direction, placed a stake in the ground, and started.

That quality served me well. I made it to VP. Projects moved forward. Things got done.

But here’s what I’m realizing now: I never actually thought about why I was doing it. I just did it. And looking back, that gap limited me in ways I didn’t see at the time.

I couldn’t effectively delegate high-stakes decisions because I couldn’t explain my process. I couldn’t scale my judgment through my team because I didn’t understand my own judgment well enough to teach it. I advanced anyway—maybe luck, maybe timing, maybe I had enough other things going for me.

But it could have been easier and I could have made my teams even stronger.

I’m finally doing the reflection work I should have done years ago. And what I’m discovering is both humbling and useful.

What Reflection Is Revealing

A friend’s compliment pushed me to do something I’d been neglecting for years: actually examine how I make decisions.

So I gave myself an assignment. For two weeks, I documented every significant decision:

• What did I decide?

• How long did it take?

• What made me feel ready to move?

• What was I weighing without realizing it?

At first, it felt ridiculous. Like trying to explain how to breathe.

But after ten days, something unexpected emerged.

The Three Modes I Didn’t Know I Was Using

I wasn’t just “making fast decisions.” I was switching between three completely different approaches depending on the situation:

Mode 1: Low-Stakes Experimentation

Low-cost decisions that are easy to reverse. I move fast because the cost of being wrong is minimal and the value of learning quickly is high.

Example: Choosing a restaurant—scan reviews, check location and dietary options, decide in 30 seconds. If it’s bad, we don’t go back. The goal is to try something and learn, not to make the perfect choice.

When I use it: Testing new approaches, trying options where failure is cheap and informative.

The risk: Using this mode when stakes are actually higher than I think.

Mode 2: Values-Based Clarity

High-stakes decisions where my priorities are non-negotiable. I move fast not because stakes are low, but because I have absolute clarity on what matters.

Example: I turned down well-paid work recently in under a minute because I knew I didn’t have time to deliver work that met my standards. Once I determined I couldn’t meet my bar for excellence, there was nothing left to decide. Reputational risk isn’t worth any fee.

When I use it: Decisions that touch core values and ethical lines.

The risk: Moving decisively based on your values when others may not share them and you haven’t built alignment first.

Mode 3: Pattern Recognition

Decisions based on situations that feel familiar from past experience. I move fast because I think I’ve seen this before.

Example: I gave advice to a colleague based on what seemed like a similar situation I’d handled. Except when I thought harder about it, the context of the organization I was in was a little different. It just felt familiar enough.

When I use it: Leveraging experience to move efficiently through known territory.

The risk: Moving fast on pattern-matching that’s actually just familiarity, not lived experience.

What I Wish I’d Known Earlier

Here’s what’s becoming clear: I was treating all three modes the same way. Fast decision, done, move on. I thought speed was the skill. Turns out, knowing when and how to be fast was the actual skill.

Which meant:

I couldn’t delegate effectively. When I sent someone to handle a high-stakes situation, they didn’t know which mode to use. Should they experiment and iterate? Hold firm on standards? Trust their pattern recognition? I never told them because I didn’t consciously know I was switching modes.

I couldn’t explain my thinking to executives. When I made recommendations, I couldn’t articulate the sophistication of what I was doing. It just looked like I moved fast—not that I was making calculated choices about when to move fast and why.

I couldn’t develop my team’s judgment. They could watch me work, but they couldn’t learn from me. Because I had nothing to teach except “trust your gut”—which is useless advice.

I advanced anyway. But I could have been so much more effective if I’d understood this earlier.

Why This Matters for You

Here’s the counterintuitive part: examining your strengths might be what unlocks your next level.

Not fixing your weaknesses. Not working harder. But understanding what you already do well enough to make it teachable, delegatable, scalable. Systematize your strength.

Because at a certain point in your career, success isn’t about being good at your job. It’s about building others who can be good at it too. Teaching others to build requires making the thought process visible. And you can’t make something visible that you haven’t examined yourself.

What You Can Do Starting This Week

For the next two weeks, document 10 significant decisions:

1. What did you decide?

2. How long did it take?

3. Which mode were you using? (Low-stakes experimentation, values-based clarity, or pattern recognition)

4. What would someone else need to know to make this same call?

Then look for patterns:

• When do you move fast for good reasons vs. out of habit?

• What are your actual non-negotiables vs. what you say they are?

• When is your pattern-matching solid vs. just familiar?

• What context are you assuming others have that they don’t?

Then start practicing articulation:

In your next important meeting, try naming your framework out loud:

“This is a low-stakes situation where we can test and iterate quickly.”

“This touches our brand standards, which aren’t negotiable—so there’s really no decision to make.”

“This feels like the X situation we saw last year, but I want to pressure-test whether it’s actually the same pattern.”

Watch what happens. You’ll sound more strategic. Not because you’re thinking differently, but because you’re making the sophistication of your thinking visible.

The Bottom Line

I made it to VP without doing this work. But I could have scaled my impact more and developed even stronger teams.

You probably already have strengths that could take you further. Natural abilities that have served you well.

But if you can’t articulate how you do what you do, you’ll hit a ceiling. You can’t delegate what you can’t explain. You can’t scale what you don’t understand. You can’t develop others to do what you can only perform intuitively.

The clarity you gain won’t just help you understand yourself better. It’ll change how you lead, how you delegate, and how far you can go.

Two weeks. Ten decisions. Document them.

The assignment seemed straightforward: You’re a shift supervisor at a local family restaurant. The health department just shut you down for 5 days. Your team is counting on you to show leadership. Write a message to your employees.

Oh, and use AI however you want.

They jumped in. Prompts flew. Within minutes, they had polished messages from ChatGPT that hit all the right notes—empathy, resources, open-door policies. When I asked them to rate AI’s performance, the numbers rolled in: 7s and 8s across the board.

“Pretty good, right?” I asked.

Then I threw them a curveball.

“Let me tell you about the people you’re actually writing to.”

There’s Jake, the high school kid who works evenings. Lives at home. Parents feed him. He doesn’t need a food bank—he needs to know if his job is safe and when he can pick up his hours again because he’s saving for a car.

There’s Maria, a single mom whose rent is due in 5 days. She lives paycheck to paycheck—actually, more like *shift to shift*. Those daily tips are how she feeds her kids. Right now, she’s not worried. She’s *panicking.*

And there’s Carlos, who’s been working in kitchens for 20 years. He sends money home every month. He has some savings, sure, but he comes from a culture where asking for help isn’t just uncomfortable—it’s shameful. Self-sufficiency isn’t a value; it’s an identity.

AI’s generic message mentioned food banks and invited people to “reach out if they need help.”

For Jake? Useless.

For Maria? Vague to the point of cruelty.

For Carlos? Culturally tone-deaf.

I asked them to revise the message—same AI tools, but now with actual human judgment layered on top.

This time:

- Jake got a message with concrete return-to-work dates and reassurance about his position.

- Maria got specific information: the food bank’s address, hours, and which bus routes get her there, plus a promise she’d get priority shifts when they reopened.

- Carlos got a Spanish translation that mentioned resources delicately—“should anyone need them”—respecting the cultural weight of self-reliance.

Then I asked them to rate AI’s original response again.

Not a single score broke 5.

You could see it click. That moment when the technology went from “wow, this is amazing” to “wait, this is just a starting point.”

Because here’s what AI couldn’t know:

- That Jake scrolls his bank account dreaming about a used Honda

- That Maria’s landlord doesn’t accept empathy as payment

- That Carlos would rather skip meals than admit he’s struggling

AI gave them competence. Human judgment gave them compassion.

And that’s the whole point, isn’t it?

We’re in a “humans who understand how to use AI versus humans who don’t” reality. The students who will thrive aren’t the ones who can write the best prompts—they’re the ones who know what questions AI can’t even think to ask.

My students walked in thinking AI was going to make communication easier.

They walked out understanding it makes lazy communication easier.

But good communication? That still requires knowing that behind every employee ID number is a person with a specific life, specific fears, and specific needs that no language model has ever met.

The assignment continues. But that lesson? Already complete.

-----

Teaching Business Communication at Diablo Valley College. Still learning alongside my students. Still convinced the most powerful technology is knowing when to override it.

They’d built an intelligent routing feature for their enterprise support software—the kind that matches incoming customer requests with the right agent (human or AI) based on request complexity. Incorrect matching meant a senior agent’s time might be wasted on password reset requests or, worse still, a customer with a complex API integration issue could be routed to a chatbot whose knowledge base didn’t include API troubleshooting.

The Testing Dilemma

The team had done their homework. They’d analyzed six months of support tickets and found that 92% were straightforward requests—password resets, account questions, basic troubleshooting. The remaining 8% were complex technical issues requiring senior engineers.

Their launch checklist included load testing, but here’s where it got complicated: the requests were interactive and highly variable. A customer might submit two sentences or a detailed technical breakdown with logs, screenshots, and code snippets. Processing time ranged from 200 milliseconds to 8 seconds.

The engineering team split into two camps:

Team “Test Everything”: “We need to test with maximum complexity requests. If the system crashes when an enterprise client submits 5,000 complex tickets during a service outage, we’re finished. We’ll have their VP of Engineering on the phone while their entire support operation is offline.”

Team “Ship It”: “That’s not realistic. Testing worst-case scenarios will cost two more weeks and $50,000 in cloud infrastructure. We’ve already delayed this feature twice this year—we committed it to three enterprise prospects in their contracts.”

They compromised. They tested with weighted averages matching actual ticket distribution—70% simple, 20% moderate, 10% complex. The tests passed. The system handled 10x their projected peak load with room to spare.

They were ready to launch. The final sign-off meeting should have been a formality.

The Question That Changed Everything

In the final sign-off meeting, the CEO asked: “What’s our rollback plan if we’re wrong about the average?”

This should have been a fifteen-minute conversation. They had feature flags. If response times spiked above 3 seconds for more than 5 minutes, the system would automatically fall back to their old random routing system. They planned to monitor closely for the first 48 hours.

Instead, that single question triggered two weeks of organizational paralysis.

Team “Test Everything” saw validation: “See? The CEO is worried. We need more testing. Let’s run the worst-case scenarios.”

Team “Ship It” pushed back: “No amount of testing will predict actual customer behavior. We need real-world data. The only way to get that is to launch.”

Management got nervous and proposed alternatives: “Maybe we should pilot with just one client first. Or add more monitoring. Or build a more sophisticated fallback system.”

Engineering calculated that each option would take anywhere from three days to two weeks.

So the team did nothing.

Not more testing. Not launching. Just meetings about meetings about risk.

The product manager created a risk assessment document. Engineers built probabilistic models predicting various failure scenarios. Daily stand-ups became philosophical debates about acceptable risk levels. The #launch Slack channel became a graveyard of competing proposals.

What I Told Them

When I walked into their offices, the team expected me to help them decide: do more testing or launch now?

I told them that wasn’t the question anymore.

The real problem wasn’t uncertainty about load testing. The problem was that they’d let a reasonable question become an excuse for organizational paralysis. They’d convinced themselves that making no decision was safer than making either decision.

But here’s what they missed: not deciding was itself a decision. Every day they delayed was a day they chose the status quo over progress. A day their competitor gained ground. A day their team’s morale eroded further.

They were waiting for certainty that would never come.

Two Types of Uncertainty

I walked them through a framework I teach my business students when they’re wrestling with decisions.

There are two types of uncertainty:

Reducible uncertainty: Things you can actually learn more about through research, testing, or analysis. If you don’t know your customer’s preferences, you can survey them. If you’re unsure about system performance under load, you can test it.

Irreducible uncertainty: Things that no amount of research will tell you. How will customers actually behave once the feature launches? Will the assumptions about ticket distribution hold true? What unexpected ways might users interact with the system?

The team had already addressed the reducible uncertainty. They’d done the analysis. They’d run the tests. They’d built the rollback mechanisms.

What remained was irreducible uncertainty—and no amount of additional testing or planning would eliminate it.

The Real Question

I suggested reframing their question. Instead of “Do we have enough information to guarantee success?” I asked them to consider: “Do we have a good decision-making process?”

Because here’s what most teams get wrong: they conflate good decisions with good outcomes.

A good decision can lead to a bad outcome. You can do everything right and still fail because of factors beyond your control—market timing, competitor moves, unforeseen technical issues, changes in customer expectations.

A bad decision can look brilliant in hindsight. You can skip essential testing, launch recklessly, and still get lucky when nothing breaks.

The goal isn’t to guarantee outcomes. The goal is to make good decisions with the information available.

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What Good Decision-Making Actually Looks Like

I had them work through a simple framework:

1. What can we actually know?

• Ticket distribution from historical data: Yes

• System performance under simulated load: Yes

• Exact customer behavior after launch: No

• Whether assumptions will hold in production: No

2. What’s the cost of waiting?

• Delayed roadmap execution, potential market share loss to competitors

• Team morale degradation

• Engineering resources tied up in analysis paralysis

• Delayed revenue from new feature

3. What’s our learning strategy?

• Feature flags for instant rollback

• Monitoring dashboards tracking response times

• Customer feedback channels

• Weekly review cadence for first month

4. Is this decision reversible?

• Yes. They could roll back in minutes if needed.

• Not a “bet the company” moment.

• Low cost of being wrong, high cost of not learning.

Once we mapped it out this way, the answer became obvious.

They launched three days later.

What Happened Next

The feature worked. Not perfectly—there were some edge cases they hadn’t anticipated, and they did roll back briefly on day four when an enterprise client’s outage created an unusual spike in complex requests. But they learned, adjusted, and had the feature stable within two weeks.

More importantly, they learned something about decision-making: waiting for perfect information is itself a decision, and often a poor one.

Why This Matters for You

If you’re in product leadership, you face this tension constantly:

• Do we launch with the MVP or build more features first?

• Do we address this customer complaint now or wait to see if it’s a pattern?

• Do we invest in this technical infrastructure upgrade or focus on new product development?

The instinct is to gather more information. Run more tests. Get more feedback. Build more consensus.

Sometimes that’s right. But often, it’s just fear dressed up as diligence.

The question isn’t “Am I certain this will work?” The question is “Do I have a good process for making this decision and learning from the outcome?”

Because here’s the truth: in complex systems—whether it’s software, organizations, or markets—you can’t eliminate uncertainty. You can only develop better processes for navigating it.

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The Framework

Next time you find yourself or your team paralyzed by uncertainty, try this:

1. Distinguish between reducible and irreducible uncertainty

• What can you actually learn more about?

• What will remain unknown no matter how much analysis you do?

2. Assess the cost of waiting

• What are you giving up by not deciding? What’s the opportunity cost?

• Is delay itself a decision?

3. Check if the decision is reversible

• Can you course-correct if you’re wrong?

• What’s the actual cost of being wrong vs. the cost of not learning?

4. Focus on process, not outcomes

• Do you have good information?

• Are you considering multiple perspectives?

• Do you have a learning strategy?

Perfect information is a mirage. Good process is real.

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The CEO’s question—”What’s our rollback plan?”—wasn’t actually asking for more testing. It was asking whether they’d thought through how to learn quickly if their assumptions were wrong.

The team that had spent two weeks paralyzed had confused the two.

What decisions are you delaying while waiting for certainty that won’t come? I’d love to hear about the uncertainty you’re navigating.

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He passed in January, soon after I posted my last post.

The months since have been a season of reflection—processing loss, rediscovering rhythms, and slowly finding my way back to the work that matters. Somewhere in that quiet space, something unexpected happened: I found myself thinking differently about the work I do, the leadership challenges I’ve navigated, and what it means to show up with clarity and purpose.

What That Season Taught Me About High-Stakes Decisions

There’s something about caregiving and loss that strips away all pretense. During those months, I learned to ask better questions. I learned to push back on assumptions. I learned to zero in on body language, discern unspoken subtext, and regularly placed myself in my father’s shoes, thinking about how I can best support him and how to advocate for him effectively. I learned that sometimes the most important thing you can do is simply be present with the uncomfortable truth.

These are the same skills we struggle with in boardrooms and team meetings. The ability to see what’s actually happening rather than what we wish was happening. The courage to speak up when everyone else is nodding along. The discipline to slow down when speed feels urgent.

The Questions I’m Carrying Forward

During that time, I kept returning to the same questions I explore with my business communication students at Diablo Valley College—questions about when and how to communicate clearly in high-stakes situations:

• How do we balance urgency with wisdom?

• When does staying quiet become complicity?

• What’s the difference between patience and avoidance?

• How do we lead through uncertainty without pretending we have all the answers?

These aren’t just academic questions. They’re the decisions we face every day in our work—whether to speak up in meetings, how to deliver difficult news, and when to push back on assumptions.

I’ve been away from this space for nine months. That’s enough time for perspective, enough distance to see what truly matters, enough healing to bring renewed energy to these conversations. My work teaching business communication has kept me grounded in the real challenges professionals face every day—and I’m ready to bring those insights here.

What’s Coming

You subscribed to Product Unblocked because you wanted practical leadership wisdom, not platitudes. That hasn’t changed.

In the weeks ahead, I’ll be exploring:

• When silence becomes a decision - How to recognize the moments where not speaking up is actually making a choice

• The questions that unlock stuck conversations - Communication patterns that move teams past gridlock

• Making better decisions with imperfect information - How to think clearly when you can’t know everything

• The role of AI as a communication tool in business, not a crutch - how and when to effectively use AI in professional settings

• Reading the room without mind-reading - How to gauge stakeholder reactions without falling into assumption traps

And yes, we’ll continue the work that resonated with so many of you—case studies from the trenches, frameworks you can use tomorrow, honest conversations about the leadership challenges that keep you up at night.

A Request

If you’ve been reading quietly, now’s the moment to lean in. Share these posts with colleagues who need them. Reply with the leadership challenges you’re navigating. Let’s build something more than a newsletter—let’s build a community of leaders who refuse to settle for superficial solutions.

The last few months taught me that time is precious and clarity is a gift. I won’t waste either.

Thank you for holding space while I stepped away. Thank you for being here when I return.

Let’s do meaningful work together.

These days unfold differently. Time moves both slower and faster, somehow. The hours by his side have become a meditation on presence, on the weight and worth of each passing moment.

There is something to be said for slowing down, for letting time breathe around us. Perhaps it takes these watershed moments to remind us that life's richest experiences often come not in the doing, but in the being – in the small acts of witnessing, of sitting with both joy and sorrow, of simply existing alongside those we love.

I will return to this space, eventually, when time and energy allow. For now, I'm learning to embrace this slower pace, to find meaning in the pause, and to honor these precious moments that, despite their difficulty, hold their own kind of beauty.

Thank you for your understanding as I navigate this season of life. May we all remember that sometimes, the most meaningful path is the one that asks us to slow our steps.

"I see the train wreck coming," she told me, "but raising the alarm could be career suicide." She wasn't exaggerating. The vendor selection committee included the CTO, CFO, and three senior VPs. The CTO had personally championed this vendor based on past success at his former company.

In our early coaching sessions, Kate shared how her team's daily standups had become increasingly tense: "They're telling me things like 'The API documentation is outdated by months' and 'Their technical leads keep rotating off our project.' But when I raise even mild concerns to leadership, I can feel the resistance."

Initial Response:The Passive Trap

When Kate first showed me her communications, I could see why she wasn't getting traction. Her updates were carefully crafted to avoid waves:

Weekly Status Mail: "Vendor integration progressing with expected learning curve challenges. Timeline impacts being assessed. Status: Yellow"

Monthly Steering Committee: "We're working through some technical clarifications with the vendor team. May need to revisit certain milestone dates as requirements are refined."

Together, we analyzed the mounting consequences:

• Q1 deliverables had silently slipped to Q3

• Her team was working weekends to compensate for vendor gaps

• Shadow solutions were emerging as business units lost faith

• Her credibility was eroding as her updates grew increasingly disconnected from reality

Kate’s initial instinct was to play it safe. In leadership meetings, she had used vague language to describe the delays. Her updates painted a falsely optimistic picture, flagging issues as “yellow” instead of “red” and burying critical concerns in details.

As a result:

• The leadership team believed things were manageable when they weren’t.

• The executive team delayed tough decisions, and Kate silently allowed issues to escalate.

• As reality unfolded, Kate’s early reassurances began to look like poor judgment.

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The Too-Aggressive Reaction: Burning Bridges

Frustration finally boiled over. Kate fired off a strongly worded email to all the executives, declaring the vendor a failure and recommending immediate termination of the contract. While her frustration was valid, the email created more problems than solutions.

The fallout was exactly what she'd feared:

• An angry call from the CTO demanding a retraction

• Vendor escalated to their CEO

• Legal responded raising contract concerns

• Team morale hit new lows

The senior leaders who selected the vendor felt attacked, making them less likely to collaborate. The email came across as impulsive and reactionary, rather than strategic, resulting in poor optics. It strained ties with both the vendor and internal stakeholders, making problem-solving even harder.

Finding the Assertive Middle Ground

This crisis point became our opportunity to develop a more effective approach. We worked together to craft a measured strategy that would honor both the technical reality and the political complexity of the situation.

Individual Outreach

First, we designed individual outreach approaches for each stakeholder. I helped Kate script and role-play these crucial conversations:

With the CTO: "James, I'd value your perspective on some implementation patterns I'm seeing. Your experience with [Vendor] in your previous role would be incredibly helpful in pressure-testing my analysis."

With Business Heads: "I want to ensure we're fully aligned on your Q3-Q4 dependencies before our broader vendor review. Could we spend 30 minutes walking through the critical path items?"

With Legal: "I'd appreciate your guidance on our vendor governance framework. Some delivery patterns have emerged that may affect our contractual obligations and risk profile. Could we review the relevant performance clauses and discuss proactive risk mitigation strategies?"

Data-Driven Executive Review

Kate then called a special steering committee session. Her opening set the tone:

"Thank you for making time for this important discussion. Over the past six months, we've gathered significant data on our transformation progress. Today, I want to share what we’ve learned and explore options for protecting our business objectives."

Her presentation focused on three areas, leading with the executive summary and her recommended solution. She covered:

• Delivery Gap Analysis

  • Sprint completion rates: actual vs. committed

  • Critical feature delays: measured in months

  • Resource turnover: percentage of original vendor team replaced

• Financial Impact

  • Budget variance: % increase in costs

  • Internal cost of delays: $ Million

  • Risk-adjusted project ROI: decline in ratio

• Strategic Implications

  • Customer experience targets missed

  • Roadblocks to meeting individual teams’ business objectives

  • Mounting technical debt

She then presented three options with structured analyses and risk mitigation strategies for each path:

1. Full Reset: Vendor replacement (~M month delay, +$M cost)

2. Controlled Recovery: Renegotiate terms, add penalties (~3 month delay, +$M)

3. Hybrid Approach: Split scope between current and new vendors (~4 month delay, +$M)

During the presentation, Kate emphasized shared responsibility to navigate the political minefield: “We made this decision based on the best information at the time. Now, we have new data that calls for a course correction.”

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The Results

Kate’s assertive approach shifted the narrative. Instead of blame games or denial, the leadership team engaged in constructive problem-solving.

The committee adopted the Hybrid Approach with modifications:

• Retained the vendor for stable components

• Accelerated internal capability building

• Instituted bi-weekly executive reviews

• Revised governance for vendor selections

Organizational Learning

The experience drove lasting changes. The organization:

• Developed a new vendor evaluation framework

• Implemented enhanced escalation protocols

• Improved risk monitoring

• Instituted regular "health checks" with key vendors

• Invested in internal technical capability

Most importantly, Kate’s credibility as a leader grew. By addressing the issues head-on without unnecessary drama, she demonstrated strategic thinking and resilience.

Why Assertiveness Works

This case highlights why assertiveness is often the most effective leadership style in complex situations. It’s not about being nice or harsh—it’s about being clear, focused, and solution-oriented. Assertive communication:

• Leads with Data: Decisions are grounded in facts, not emotion.

• Builds Bridges: It acknowledges different perspectives and avoids unnecessary blame.

• Balances Urgency and Deliberation: It creates a sense of purpose without panic.

• Focuses on Outcomes: It keeps the team aligned on business goals rather than interpersonal conflicts.

Final Thoughts

Leadership is rarely about making perfect decisions; it’s about navigating the imperfections with clarity and composure. Kate’s journey serves as a reminder that assertive communication isn’t just a leadership skill—it’s a lifeline when the stakes are high.

I was at a product review meeting where the room buzzed with tension. The engineering team presented data that seemed to contradict the marketing team's customer research. Just as the discussion threatened to derail into defensive posturing, someone asked: "Before we dig into the numbers, could you share what surprised you most in your findings?" The tone of the conversation that followed shifted.

The moment captured something crucial about questions: how they're asked shapes what people share. Here's what I've learned about making these critical shifts happen.

Lead With Context

A thoughtful setup transforms how information flows in conversations. Three types of preambles consistently make a difference:

1. Recognition Preambles

These acknowledge expertise and set expectations: "Jan, since you researched this market, there are two points I'd like your perspective on." This shows Jan you value his knowledge while helping others identify who holds specific expertise.

Think of recognition preambles as spotlights - they illuminate expertise in a way that invites rather than demands participation. More examples:

• "Alex, your experience launching in the South American market gives you unique insight. Could we explore..."

• "Pat, few people understand our supply chain challenges the way you do. Help us understand..."

• "Sam, you've been closest to the customer feedback on this. What patterns are you seeing in..."

2. Audience Preambles

These frame the conversation's purpose and stakeholders: "The leadership team asked for clarity on our market position, so I'd like to explore some specific metrics with you." This helps people calibrate their responses to the right level of detail and audience needs.

Audience preambles act like filters, helping those who respond refine their message for maximum impact:

• "Our investors are particularly interested in the sustainability metrics, so let's focus on..."

• "The customer’s engineering team needs this explained in technical detail, which is why I'm asking..."

• "Several customers have raised this specific concern, so I want to understand..."

3. Transition Preambles

These guide the group through complex discussions: "We've covered the customer feedback. Now, looking at our timeline constraints, let's focus on the final metric." This keeps everyone oriented and maintains momentum.

Think of transition preambles as bridges between ideas. They help people cross from one concept to another while maintaining the thread of conversation:

• "Building on what we just learned about user behavior, let's examine how that affects..."

• "Now that we understand the technical limitations, we should explore the budget implications..."

• "You've given us great context on the current state. Looking ahead, what should we consider..."

The key is to match the preamble to the moment. Each type serves a distinct purpose in making conversations more productive, turning what could be an interrogation into a collaborative discovery. When used thoughtfully, these preambles create psychological safety, maintain momentum, and ensure everyone understands not just what's being asked, but why it matters, and who needs to know.

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Watch The Tone

The words themselves might have been straightforward but the same question lands differently based on delivery. Where we place emphasis in our questions carries hidden messages about our intentions and assumptions. Consider these variations:

1. The Emotional Weight of Emphasis

"Do you think these numbers make sense?" (Opens space for personal insight, values individual perspective)

versus

"Do you think these numbers make sense?" (Suggests doubt, carries subtle criticism)

The first invites wisdom while the second invites defense. One builds bridges, the other builds walls.

2. The Collaborative Power of "We"

Language choices can either unite or divide teams:

"Are you assuming nothing has changed?" (Creates distance, suggests blame)

versus

"Are we working with outdated assumptions?" (Builds shared ownership, invites joint problem-solving)

3. Questions That Build or Break Trust

The most powerful questions often come wrapped in genuine curiosity:

"What's wrong with this approach?" (Puts people on guard, suggests failure)

versus

"What possibilities haven't we considered yet?" (Opens minds, invites innovation)

4. Reading the Emotional Room

Sometimes the most important tone shift isn't in the question itself but in the space around it. Notice when tension creeps in:

• "I'm hearing some concern in our voices. Could we pause and check our assumptions?"

• "This feels like a moment where we could all use a fresh perspective. What if we approached this differently?"

• "There seems to be some unspoken worry here. What would make this conversation feel safer?"

The art lies not just in crafting the right question, but in delivering it in a way that opens minds rather than closing them. It's about creating moments where people feel included rather than interrogated.

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Match Speed to Situation

Sometimes rapid brainstorming energizes the room. Other times, complex topics require slowing down so everyone can process and contribute. Reading the room and adjusting accordingly makes a difference.

1. The Fast Track: When Speed Sparks Innovation

Some moments call for rapid-fire thinking:

• Early brainstorming sessions where energy feeds creativity

• Crisis situations requiring quick pattern recognition

• Team alignment meetings where momentum matters

Try this: "Let's do a two-minute question storm. No filtering, just curiosity. What could we explore here?"

2. The Slow Lane: When Depth Demands Time

Other conversations need room to breathe:

• Complex strategic decisions with far-reaching implications

• Sensitive topics where emotions run deep

• Cultural or organizational changes affecting many stakeholders

Consider: "This feels like something we should sit with for a moment. What aspects need deeper exploration?"

3. Reading the Room's Rhythm

Watch for signals that tell you when to shift gears:

• Furrowed brows often signal a need to slow down

• Fidgeting might mean it's time to pick up the pace

• Side conversations could indicate lost momentum

• Engaged note-taking suggests you've found the right speed

4. Making It Work in Practice

Before your next significant conversation:

1. Assess the topic's complexity and emotional weight

2. Plan your initial pace but be ready to adjust

3. Watch body language and engagement levels

4. Build in natural pause points for reflection

5. Notice when energy shifts and be ready to respond

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Bringing it all together

Try these approaches in 2025:

• Before meetings, write down one clear setup line for key questions, including both recognition and audience preambles if necessary.

• During meetings, use the transition preambles to bring the room with you.

• Notice tone when asking tough questions - aim for curiosity rather than challenge.

• Read the emotions in the room. Consider pausing to reflect, reframe, or redirect as appropriate.

• Pay attention to when conversations need to speed up or slow down by reading the room’s rhythm.

The goal isn't perfection. It's about asking questions in ways that help people think better and share more openly.

In this moment, as one year folds into another, I'm reminded that our greatest leadership legacy often lies not in the answers we provide, but in the questions we ask. The conversations ahead hold the seeds of transformation - what questions will you plant to help them grow?

The key is learning to say no in a way that respects both your boundaries and the relationship.

Try this instead.

1. Understand the Value Trade-off.

   Pro tip: Before responding to any request, pause and identify exactly what you'd be giving up by saying yes.

   Why it works:

   1. Makes the hidden costs of "yes" visible

   2. Helps you make conscious rather than reactive decisions

   3. Strengthens your resolve when the trade-off isn't worth it

2. Communicate Your Boundaries.

   Pro tip: State your boundary as a positive commitment to something you value, rather than a rejection of their request. Example: "I've committed to protecting my Tuesday evenings for personal time."

   Why it works:

   1. Frames your “no” around what you're choosing to prioritize

   2. Shows you've thoughtfully considered your commitments

   3. Makes it clear this isn't personal - it's about your established boundaries

3. Maintain Control of Future Asks

   Pro tip: Close the conversation with clarity that you will initiate communication if anything changes.

   Why it works:

   1. Prevents the "I’ll check back with you later" cycle from kicking in

   2. Puts you in charge of revisiting if circumstances change

   3. Respects both parties' time and energy

An Example:

Them:

"We're forming a cross-functional innovation committee that meets every Tuesday. Given your experience, you'd be perfect for this. Can you join?"

You:

❌ "Sorry, I'm just so busy right now." (leaves door open for pressure). Instead, try:

✅ "I've looked at what this would mean for my schedule. Those hours are crucial for me to prepare for the Weekly Business Review. It's a trade-off I'm not willing to make right now."

✅ "I've made a promise to myself to block off time to wrap up the WBR report by noon on Tuesday. It's a boundary that helps me be ready for Wednesdays."

✅ "If my commitments change significantly in the future, I'll reach out to you about joining. Until then, I'll need to pass."

Notice how each response follows the three-step framework:

1. Acknowledges the specific trade-off (being prepared for weekly meeting vs. committee participation).

2. States the boundary as a proactive commitment to existing work.

3. Keeps you in control of any future involvement.

The approach remains professional while being clear and firm about your priorities and boundaries.

The end of the year brings a particular set of challenges for managers. These matter the most:

Plan Around Cultural Rhythms

Managing a team across Singapore, Stockholm, and San Francisco meant juggling multiple holiday calendars. My European team members typically took extended breaks from mid-December through early January - it's simply part of their work culture. U.S. colleagues expected the week between Christmas and New Year's to be light or were off work, while APAC team members planned around entirely different celebrations - from Lunar New Year to Diwali. Missing these patterns leads to frustrated team members.

Set Clear End-of-Year Boundaries

If you're still scheduling performance reviews for mid-December, stop. Just stop. Getting meaningful input becomes nearly impossible when half your stakeholders are mentally (or literally) already on vacation. Wrap up major evaluations by early December at the latest. My teams aimed to gather all feedback by November 15, with December 1st as the hard deadline for submitting all evaluations. This timing lets everyone start their holidays without lingering tasks.

Respect the Recovery Runway

January isn't just about new beginnings - it's about gentle re-entry. Expecting peak productivity in the first two weeks of the year is like asking someone to run a marathon right after waking up. We found that scheduling the first week of the year for team alignment and planning, with major deliverables starting in week three, worked best.

Organize Fair Coverage

This one's tricky, but crucial. My teams included members from cultures where month-long winter breaks are the norm working alongside others who get just 15 precious vacation days annually. Therefore, we created a fair rotation system and communicated expectations early so no one felt like they're always the one holding down the fort. Our solution was threefold:

1. A shared calendar marked three months out with all cultural holidays and breaks.

2. Clear handoff protocols between time zones with all teams adhering to a simple triage system:

   • Critical issues = response needed within 2 hours (clearly defined: Sev 0 and Sev 1s such as production down, data breach, customer-facing outages)

   • Important issues = response by next business day (Sev 2 issues such as system degradation, non-critical bugs)

   • Everything else waits until return.

3. Rotating coverage teams - Team members would self-select and rotate coverage of major holiday periods annually - those who covered Christmas one year (for example) would get priority for for their preferred holiday next time.

Navigate Celebrations Thoughtfully

Gift-giving across cultures requires extra thoughtfulness. Skip the professional development books - what feels like inspiration to the manager lands as obligation to recipients. Nothing dampens holiday joy quite like an implied reading assignment that turns into a January homework check-in.

Where a bottle of wine might be a thoughtful gift in France, it’s inappropriate for team members who don't drink alcohol. I once received a gift basket containing cured meat that I couldn’t eat as a vegetarian. Cash or cash equivalents, perfectly normal in some Asian cultures, could violate company policies.

Instead, we found success with:

• Team donations to food banks chosen by the team.

• Personal appreciation notes highlighting specific contributions.

• A holiday fund ($50 per person) that each team member directs to their chosen charity.

Some of our best moments came from organic sharing of traditions. During one virtual celebration, a colleague from Sweden shared pictures of her St. Lucia Day celebration, including the saffron buns she bakes every December 13th. Another team member shared his special Diwali dessert recipe, also featuring saffron, leading to an engaging discussion about festival foods across cultures. These natural conversations built stronger connections than any scheduled team-building exercise.

The most successful holiday seasons I've managed weren't about perfect planning, but about setting clear boundaries and honoring different approaches to celebration and rest. One of our highest-performing quarters followed a December where we deliberately slowed down, let people fully disconnect during their time off, and focused on starting January fresh.

Instead of getting defensive or backing down, try these approaches:

1. "That does not align with what I know. Could you share your perspective?"

   Pro tip: Keep your voice calm and genuinely curious.

   Why it works:

   1. Diffuses tension by showing openness to their view

   2. Moves from confrontation to conversation

   3. Creates space for both perspectives to be heard

2. "Let's look at this together."

   Pro tip: Use this phrase as an invitation to collaborative exploration rather than a challenge.

   Why it works:

   1. Shifts from opposition to partnership

   2. Creates a shared goal of understanding

   3. Reduces defensive reactions

3. "Help me understand the differences in our perspectives."

   Pro tip: Keep your tone measured and professional to demonstrate genuine interest in the discussion. Your body language matters as much as your words - maintain open, relaxed posture to show you're genuinely interested in discussion

   Why it works:

   1. Sets a professional tone for exploring disagreement

   2. Creates space for structured discussion

   3. Positions the conversation as a dialogue rather than a debate

4. "What information are you basing that on?"

   Pro tip: Ask this with genuine curiosity rather than skepticism. Your tone can make the difference between defensiveness and dialogue.

   Why it works:

   1. Creates opportunity for fact-based discussion

   2. Helps identify the root of disagreement

   3. Shows respect for their reasoning process

An Example:

Them:

"That's not true - we can definitely deliver this project by next quarter."

You:

❌ "No we can't!" (escalates conflict). Instead, try:

✅ "That does not align with what I know. Could you share your perspective? I'd like to understand how you envision us meeting that timeline."

✅ "Let's look at this together. What assumptions are we making about team capacity and dependencies?"

✅ "Help me understand the differences in our perspectives. Which deliverables do you see fitting into next quarter?"

✅ "What information are you basing that on? I have our past project timelines here - they might help us create a realistic schedule."

Try these responses in your next challenging conversation and notice how they change the dynamic.

Saying “I disagree” often triggers defensive and confrontational reactions in the other person and prompts people to dig deeper into their position rather than consider alternatives.

Instead try:

1. “I see things differently.”

   Pro tip: Keep your tone collaborative rather than confrontational. The same words land differently depending on whether you sound defensive or open to discussion.

   Why it works:

   1. Uses "I" statements that own your perspective without invalidating theirs.

   2. Opens a door rather than putting up a wall.

   3. Invites dialogue rather than debate.

2. "I'm curious what makes you say that…"

   Pro tip: The tone matters as much as the words. Say this with genuine curiosity rather than skepticism to keep the conversation productive.

   Why it works:

   1. Transforms disagreement into mutual exploration.

   2. Gives the other person a chance to elaborate and feel heard

   3. Often leads to the other person examining their own assumption.

3. "Yes, and..."

   Pro tip: Pay attention to your emphasis - if you rush through the "yes" to get to your point, it sounds insincere. Give equal weight to both parts of your response to show you genuinely acknowledge their perspective.

   Why it works:

   1. Acknowledges their concern rather than dismissing it

   2. Allows you to introduce new perspectives without creating defensiveness

   3. Keeps the discussion collaborative rather than combative

4. I used to think that too, until I discovered..."

   Pro tip: Share your genuine learning experience - people are more likely to consider new perspectives when they hear how and why someone else's view evolved.

   Why it works:

   1. Creates instant rapport by showing you once shared their view

   2. Makes your different perspective feel like a journey, not a judgment

   3. Models that it's okay to change your mind with new information

An Example:

Them:

"Working from home reduces productivity."

You:

❌ "I disagree." (creates tension). Instead, try:

✅ "I see it differently because in my team, our output actually increased by 20% when we went remote. What metrics are you looking at?"

✅ "I'm curious what makes you say that..."

✅ "Yes, remote work can create challenges, and I've found that with the right tools and processes, many teams are actually seeing increased output. For instance, eliminating commute time has given our team two extra focused hours each day."

✅ "I used to think that too, until I discovered our team's output increased by 30% after implementing clear virtual workflows. The key was finding the right balance of focused work time and team check-ins."

Try one of these and let me know how it went! Other tips? Please share below.