Leading like a Builder: How AI Product Leaders Drive Impact and ROI

article Nov 18, 2025

Leading Like a Builder: How AI Product Leaders Drive Impact and ROI

The player-coach playbook for leaders who refuse to delegate their way to irrelevance.

Updated February 2026

There's a divide forming in AI leadership right now.

On one side: leaders who delegate AI to their teams, attend update meetings, and hope the right things get built. On the other: leaders who stay close enough to the work to actually shape it — who enable their builders while obsessing over what gets built and how to build faster.

I call the second group builder-leaders. And in 2026, they're the ones driving real ROI while everyone else is still stuck in pilot purgatory.

This isn't about becoming a developer again. It's not about doing the work yourself. It's about leading from a position of deep understanding — close enough to make the calls that matter, informed enough to know when to push back.

Here's the playbook.

The Player-Coach Model for AI Leadership

The best AI leaders I know operate like player-coaches. They don't just manage from the sidelines. They:

Enable builders — removing blockers, securing resources, creating space for the team to move fast
Obsess over what gets built — staying close enough to the product decisions to catch bad calls early
Obsess over how to build faster — always asking "what would accelerate this?" instead of just "is this on track?"

This is fundamentally different from the traditional product leadership model where you're expected to "empower the team and get out of the way." In AI, getting out of the way too early means missing the moment when someone makes a decision that tanks the project.

Remember my Alexa story? My engineers looked at an AI response that said "If you've ever wondered what it would be like to be infected with a virus, you're in luck!" and said "that's technically accurate." If I had been too far from the work, that would have shipped.

The player-coach advantage: You're close enough to catch what the builders can't see, and informed enough to know when their instincts are right.

Three Power Moves for Builder-Leaders

Over the past few years — leading AI at Amazon, building AI Career Boost, and teaching and coaching thousands of product leaders through AI transformations — I've identified three moves that separate builder-leaders from everyone else.

Move #1: Build Culture Around Goals

Move #2: Build Prototyping as a Superpower

Move #3: Build Ownership of Evaluation

Let's break each one down.

Move #1: Build Culture Around Goals

Most companies are setting the wrong kind of AI goals.

I learned this distinction from a director at Amazon Alexa, and it changed how I think about goal-setting entirely. He called it watermelon goals vs. dragonfruit goals.

Watermelon Goals

Watermelon goals are green on the outside, red in the middle.

They're the comfortable goals. Everyone understands what you're doing. You know the path to get there. They start green on the status tracker, maybe go yellow mid-year if resources get tight, and end up green by December.

The team hits their targets. Everyone feels good. But nothing has meaningfully changed in your business.

Examples of watermelon goals:

Launch a chatbot by Q2
Replace X processes with agentic AI automation by EOY
Implement AI copilot for sales team
Deploy AI writing tool for marketing

Notice the pattern: they're prescriptive about the how (the specific technology) without connecting to real business outcomes.

Dragonfruit Goals

Dragonfruit goals are the opposite — red on the outside, green in the middle.

These goals are uncomfortable to take on because no one knows exactly how they'll be accomplished. There's no clear timeline. They may stay red on the tracker for most of the year.

But if you accomplish them, you've moved the business forward in ways competitors can't easily replicate. And here's the counterintuitive part: even if you don't fully accomplish them, you've still moved forward significantly.

Examples of dragonfruit goals:

Increase month-over-month user engagement by X%
Increase internal process efficiency to save X person-years
Reduce sales cycle length by 20%
Cut content production cycle from 2 weeks to 3 days

The goal focuses on the business outcome. The how stays open.

Why This Matters for AI

At Amazon, the SVP-level expectation was that 20-30% of goals each year should not be met, or you're not thinking big enough.

They knew that not innovating was an existential threat. They built a culture that gave permission for ambitious failure — as long as you were failing upward, building capabilities, learning fast.

The craziest shift companies are being asked to make right now is that every company needs to learn how to innovate and take risk. For many leaders, this means thinking less like a manager and more like a venture capitalist.

Setting a corporate culture with permission to fail — as long as you're failing upward — is one of the hardest and most important changes companies need to make right now.

→ *For the full framework on watermelon vs. dragonfruit goals, including how to diagnose which type you're setting, read *Watermelon vs. Dragonfruit Goals: A Framework for Setting AI Goals That Actually Matter.

Move #2: Build Prototyping as a Superpower

Here's a question most product leaders don't ask themselves: How long does it take to validate whether an AI idea is worth pursuing?

If the answer involves "waiting for engineering bandwidth" or "getting it on the roadmap," you've already lost.

The leaders who are winning right now can validate an AI idea in days, not quarters. They don't wait for their technical teams to have capacity. They prototype fast enough to know whether something is worth building before they ever ask for resources.

What You Need to Prototype an AI Workflow

When I teach prototyping, I use a simple framework. Before you build anything, you need to understand:

The Pain Point — What's the current cost (time, money, frustration)?
The Data Flow — Where does the information come from? Where does it need to go?
Where AI Adds Value — What specifically is the AI doing? (Classification? Generation? Extraction?)
Success & Failure Conditions — How do you know if it's working?
Risks, Constraints, Guardrails — What could go wrong? What must you protect?

With those five answers, you can prototype almost any AI workflow in a weekend using today's no-code and low-code tools.

Example: Meeting Action Items

Say your team spends hours every week extracting action items from meeting recordings. The data flow looks like this:

Zoom Meeting → Transcription → Extract Action Items → Push to Asana

You could wait for engineering to build this. Or you could wire it together yourself in an afternoon using existing tools, test it on real meetings, and come to your next planning session with data on whether it actually saves time.

That's the difference between a leader who waits and a leader who builds.

The Mindset Shift

This is about moving from steady-state validation (traditional research → spec → build → test) to rapid idea iteration.

You're not replacing your engineering team. You're de-risking their work by validating ideas before they ever touch the backlog.

Builder-leaders ask: "Where could I move faster by leveraging AI-powered prototyping, automatic idea generation, or synthetic testing?"

Move #3: Build Ownership of Evaluation

Here's a quote from Anthropic's Chief Product Officer, Mike Krieger:

"If you come interview at Anthropic... you'll see one of the things we do in the interview process... we want to see how you think [about AI evals]... not enough of that talent exists."

Evals — the discipline of evaluating whether AI systems are working correctly — is where the leverage is for product leaders right now.

This is counterintuitive. Most product leaders think: "Evaluation is technical. That's for the engineers."

Wrong. Evaluation is where product judgment matters most.

Why Evals Are Your Point of Greatest Leverage

Engineers can build evaluation infrastructure. They can run tests. They can measure accuracy.

But they can't tell you:

What "good" looks like from the user's perspective
Which failure modes matter most for the business
How to weight tradeoffs between speed, accuracy, cost, and safety

That's product judgment. And if you're not involved in defining how your AI systems get evaluated, you've outsourced the most important decisions about what gets shipped.

The Golden Data Set

The first step in building ownership of evaluation is creating a golden data set — a curated set of inputs with known-good outputs that you use to test whether the AI is behaving correctly.

This is something a product leader can own directly:

You decide what inputs to include (edge cases, common cases, failure modes you're worried about)
You define what "good" looks like for each output
You advocate for the user when engineers want to optimize for metrics that don't match user value

When you own the golden data set, you have a seat at the table for every evaluation conversation.

From Human-in-the-Loop to Human-on-the-Loop

A related shift: many organizations currently have humans checking every AI output. That creates bottlenecks.

Builder-leaders redesign these processes to keep humans informed without creating chokepoints:

Sampling and auditing instead of reviewing everything
Alerting on anomalies instead of manual inspection
Clear escalation paths for edge cases

Some processes must maintain human oversight for accountability, safety, or trust reasons. Part of product judgment is knowing which ones.

→ *I've been teaching product leaders how to evaluate conversational AI from multiple lenses *since 2023*. For more on why this matters, read *Why AI Product Leaders Must Own Evals* and my practical guide on *what to do when the vibes are off.

The Builder-Leader Mindset

Everything I've described comes down to a mindset shift.

Traditional product leadership in the pre-AI era was about:

Strategy and prioritization
Stakeholder alignment
Empowering teams and getting out of the way

AI product leadership adds:

Technical depth — enough to call BS and make tradeoffs
Hands-on fluency — enough to prototype and validate before asking for resources
Evaluation ownership — defining what "good" looks like, not just delegating it

You don't need to become a developer. But you need to be close enough to the work to lead it.

What This Looks Like in Practice

Let me paint a picture of the builder-leader in action.

Monday: You notice a workflow that's eating hours of your team's time. You sketch out the data flow and identify where AI could add value. You build a rough prototype that evening using no-code tools.

Wednesday: You run your prototype on mock or even real data. It's 80% there. You document what's working and what's failing — with specific examples.

Thursday: You bring the prototype and your findings to your engineering lead. Instead of a vague request ("can we explore using AI for this?"), you have a working demo, clear success criteria, and documented failure modes.

Two weeks later: The production version ships. It's better than your prototype because engineering optimized it properly. But it shipped faster because you de-risked it first.

That's leading like a builder.

Getting Started

If this resonates, here are three things you can do this week:

Audit your AI goals. Are they watermelon or dragonfruit? If they're all green on the tracker, you might not be thinking big enough.

Identify one process to prototype. Something that takes hours of manual work. Map the data flow. Build a rough version this weekend.

Ask about evals. In your next AI project meeting, ask: "How are we evaluating this? What's in our golden data set? How did we decide what 'good' looks like?" If no one has clear answers, you've found your leverage point.

Further Resources

If you want to go deeper on any of these moves:

Watermelon vs. Dragonfruit Goals — The full framework for setting AI goals that actually matter
How Technical Do AI Product Leaders Need to Be? — My story of learning to lead AI at Amazon, and what it taught me about the right level of technical depth
Why AI Product Leaders Must Own Evals — How to take ownership of AI evaluation as a product leader

And if you want to build these capabilities systematically, join us for a free masterclass where I teach the full builder-leader playbook.

Polly Allen is the founder of AI Career Boost and a former Principal Product Manager at Amazon Alexa, where she led generative AI product development before ChatGPT made it mainstream. She helps senior product and business leaders build the judgment and hands-on skills to lead AI work — not just manage it.