Leaders Who Won't Build Will Get Left Behind
The AI era rewards clarity and discernment — skills you can only develop by getting hands-on
Leaders Who Won’t Build Will Get Left Behind
Hannah Pritchett, Chief People Officer at Anthropic, spent six months trying to explain to her team what she wanted them to build. She described it in meetings. She wrote it down. They came back with what they thought they heard. None of it was right.
So over a weekend, she built a rough prototype herself using Claude Code. She showed it to her team. Their response: “Now I understand what you want.”
Six months of misalignment. Resolved the moment she stopped describing and started building.
TL;DR:
AI executes your ambiguity perfectly — if you can’t articulate what you want, AI won’t figure it out for you
The two leadership skills that matter most in the AI era: clarity (knowing what to ask for) and discernment (knowing whether you got it)
You can only develop these skills by building — not by delegating, reviewing decks, or attending demos
86% of C-suite leaders are increasing AI investment, but only 20% of employees feel like co-creators in how it changes their work
The leaders who close that gap are the ones who build alongside their teams — not above them
The Clarity Gap Is a Leadership Gap
Melissa Daimler wrote a piece in Forbes this week that landed for me because she put a name on the exact leader I’ve been teaching for the last two years — at UC Berkeley Executive Education, at Maven, and across every cohort I’ve run. The builder-leader. The leader who gets hands-on before asking others to build.
Her core argument: the biggest problem with AI in organizations isn’t the technology. It’s that leaders haven’t done the hard work of getting clear about what they actually want. And her sharpest line: “AI will execute your ambiguity perfectly.”
Over the last year, I’ve watched this identity emerge in real time across my Maven cohorts. CEOs, entrepreneurs, founders, SVPs, marketing leaders — people who didn’t come from technical backgrounds but who chose to get hands-on anyway. They embody this builder-leader persona: they learn by doing, they build before they delegate, and they develop sharper instincts because of it.
I’ve also met the other kind. Leaders who view hands-on work as beneath them. Who believe their role is to set direction and let technical people figure out the details. A year ago, that was a defensible position. In 2026, those leaders are becoming irrelevant — and fast.
The pattern is always the same. A leader assigns a deliverable. An employee uses AI to produce it. The leader asks one clarifying question. The employee can’t answer it. The leader blames the tools.
But the tools aren’t the problem. The prompt was vague going in. The output was accepted without scrutiny on the way out.
Here’s the harder question Daimler asks — one most leaders don’t want to hear: Could you have answered that clarifying question yourself? Had you ever done the work you just assigned?
If not, you don’t just have a clarity problem with your employee. You have one with yourself.
What Builder-Leaders Do Differently
The builder-leader isn’t a leader who codes for a living. It’s a leader who has done the work at least once, so they know what good looks like. That distinction changes everything about how you lead an AI-augmented team.
A leader who hasn’t built asks: “Why doesn’t this look right?”
A builder-leader asks: “What context did you give it? How specific were your prompts? What did you do when the first output wasn’t right?”
That second conversation is a coaching moment. The first is a dead end.
This is what I’ve been building my entire teaching practice around. When executives in my courses sit down with Claude Code and build something — a workflow, a prototype, a decision tool — the shift is immediate. They stop asking “what can AI do?” and start asking “what do I actually need?” Those are completely different questions, and only the second one leads anywhere useful.
Daimler’s Paris travel app story makes this tangible. She built an app to organize hundreds of restaurant and café recommendations. The first version was functional but flat. Then she got clearer: organize by arrondissement so her family could use it contextually. Then clearer still: design it so her family would actually want to open it. Each round of building forced a new round of clarity. The prototype wasn’t the product. The prototype was the clarity.
The Two Skills AI Demands But Can’t Give You
Daimler identifies two skills that separate builder-leaders from everyone else: clarity and discernment.
Clarity is the front end. What you ask for, how you scope it, what quality looks like before AI generates anything. This is harder than it sounds. So much of what makes a great leader lives in their head — instincts, pattern recognition, the “I just know.” AI forces you to make that implicit knowledge explicit. You cannot prompt what you cannot articulate.
Discernment is the back end. The judgment to evaluate whether AI’s output is actually the outcome you wanted. AI produces output confidently. It will write you a board memo that sounds perfect — clean structure, polished prose — while the market data is from 2023, the competitor analysis includes a company that was acquired last year, and the recommendation contradicts your actual strategy.
Discernment is catching that before it goes out. And you can only catch it if you’ve done the work yourself at least once.
This maps directly to what I see in my courses. I developed a framework called Business-First AI that walks leaders through three steps: Analyze the workflows that matter, Deconstruct them into the detail required to build, and Build the actual solution.
Step two — Deconstruct — is where the a-ha happens. Every time.
Leaders walk in thinking they understand their workflows. Then I give them 30 minutes to break one down: every decision point, every input, every handoff, every edge case.
When we reconvene, the reactions are always the same. They’re amazed by the edge cases they’d never considered, the details they’d forgotten, the steps they’d been carrying in their heads without ever making explicit. They leave with a new appreciation for the level of clarity truly required for a machine to carry out the workflow that lives in their head.
That’s the clarity gap Daimler is writing about, experienced in real time.
By the time they reach step three — Build — the hard work is already done. The prototype comes together faster because the thinking is sharper. The clarity came from the deconstruction, not from the tool.
You can’t develop taste by reading a menu. You develop it by cooking.
What’s at Stake
Here’s what leaders are risking by staying hands-off.
Your credibility is at stake. BCG found that when leaders demonstrate strong support for AI, the share of employees who feel positive about it rises from 15% to 55%. But “support” isn’t sending a company-wide email or mandating adoption. It’s leaders who are experimenting with the tools, working through the same challenges, and building alongside their teams. Your employees know the difference between a leader who has built something and one who’s read the executive summary.
Your strategic vision is at stake. If you haven’t used AI to build, you’re forming strategy based on vendor demos and consultant decks. You’re making the biggest workforce decisions in a generation — investing and cutting — without the firsthand understanding those decisions require. Accenture’s 2026 data is stark: 86% of C-suite leaders are increasing AI investment, but only 20% of employees feel like active co-creators. That gap doesn’t close with better tools. It closes when leaders develop the clarity to know what they want and the discernment to know when they’ve got it.
Your ability to lead is at stake. The leader who hasn’t built can’t coach their team through AI challenges. They can’t ask the right follow-up questions. They can’t distinguish between AI slop and genuine insight. And here’s the uncomfortable truth Daimler surfaces: “AI slop” isn’t the AI’s fault. Human slop is accepting it. If you can’t tell the difference, that’s not a technology problem. That’s a leadership problem.
Your competitive position is at stake. The organizations moving fastest with AI aren’t the ones with the biggest budgets. They’re the ones where leaders have built enough to articulate a clear vision, set specific quality standards, and recognize when output meets them. Every week you wait to get hands-on is a week your competitors are developing the clarity and discernment you’re still outsourcing.
If you think getting hands-on is beneath you, you’re wrong. It’s beneath you to lead a team through a transformation you haven’t experienced yourself.
Start Here
Pick one deliverable you’ve been about to assign. A report, a strategy doc, an analysis. Do it yourself first. Take 30 minutes. Open Claude, ChatGPT, or whatever tool your company uses, and start with a real problem you’ve been putting off.
Be specific going in. Don’t say “give me an analysis of our metrics.” Say “I want a metrics report that tells me the story behind the numbers, identifies the top two or three gaps we need to address, and ends with agreed-upon next actions we can bring to the team.” Upload relevant documents. Give examples of what good looks like. Set constraints.
Evaluate what comes back with real critical thinking. AI doesn’t signal uncertainty. It sounds confident whether it’s right or wrong. That’s your job to catch.
Then bring what you learned back to your team. Show them what you built. That conversation — where you share your process, your prompts, your mistakes — is the work. That’s how you become a builder-leader.
Want a structured way to start? I built a Business-First AI plugin for Claude Code that guides you through all three steps of the framework — Analyze, Deconstruct, and Build. Install the plugin and it walks you through identifying the right workflow, breaking it down to the level of clarity AI requires, and constructing the solution. It’s the same process my cohort students use, available as a set of skills you can run on your own.
You won’t get it right the first time. As Daimler puts it: “The question is whether you’ll get the Eiffel Tower or the Egyptian pyramids. And whether you’ll know the difference.”
Master AI. Master Yourself. Build What Matters.
This article reinforced something I believe deeply: the AI era doesn’t reward the leaders who adopt the fastest. It rewards the leaders who get clear the fastest. And clarity only comes from building.
The great leaders I work with aren’t just learning AI. They’re fusing AI with their own leadership identity — reimagining what it means to set vision, to coach teams, to make decisions. They see the opportunity and they’re embracing it, reshaping who they are as leaders at the intersection of technology and judgment.
That’s the real work. Not mastering AI as a tool. Mastering yourself as a leader — and building what matters from that foundation. The two are inseparable now.
Stay curious. Stay hands-on.
-James
Going deeper. If this resonates and you want to go from reading about builder-leadership to practicing it, my Maven courses are built for exactly that. Claude for Builders teaches you to build real workflows with Claude Code and Cowork. Hands-on Agentic AI for Leaders takes you from building blocks to full AI-powered business workflows. Both have April cohorts open for enrollment. No slides. No theory. You build.