AI Fluency Is the New Executive Competence

By Camiel Notermans, Founder & CEO, ZeroForce

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The Uncomfortable Truth

Your executive team is functionally illiterate in AI.

Not in the sense of understanding transformer architectures or prompt engineering. In the sense that they cannot evaluate whether an AI system works, cannot predict its failure modes, and cannot make strategic decisions that account for what AI can and cannot do. They read about AI in the news. They don't understand AI through experience.

This is no longer a gap. It's a liability with an expiration date.

In the next 18 months, boards will begin replacing executives who cannot demonstrate hands-on AI competence. Not because they're anti-AI. Because they're making bad decisions based on incomplete mental models. And those decisions cost money.

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The Skill Gap Nobody's Talking About

There's a critical difference between AI awareness and AI competence.

AI Awareness is what most executives have:

• "We're using ChatGPT for customer service"
• "We need an AI strategy"
• "Our competitors are building AI"
• "We should hire a Chief AI Officer"

AI Competence is what they don't have:

• Can you evaluate whether a given AI output is accurate or hallucinated?
• Can you spot the edge cases where an autonomous system fails catastrophically?
• Can you architect a workflow where humans and AI coordinate instead of the AI breaking everything?
• Can you calculate the actual ROI of an AI system, not just the hype?
• Can you build a system and watch it fail, iterate, and rebuild?

The second list requires building. Actually deploying AI systems. Watching them break. Fixing them. Rebuilding them.

You cannot develop this competence by reading case studies. You develop it by doing.

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The Builder's Advantage

Here's what separates leaders with real AI fluency from those without it:

They've felt the pain of failure. They've built a system they thought would work, deployed it, and watched it spectacular fail in production. Then they had to fix it. They understand, at a visceral level, that "AI" isn't magic—it's a tool with hard constraints.

They understand the gap between theory and practice. They've read the research. They've built the system. They know where the gap is. This makes them dramatically better at evaluating proposals, spotting where another vendor is overselling, and recognizing when an AI approach actually makes sense vs. when it doesn't.

They know what can be automated and what can't. Through hands-on experimentation, they've developed genuine intuition about where autonomous systems add value and where they create liability. This changes their strategic decisions.

They evaluate AI proposals differently. When a Chief AI Officer pitches an AI initiative, an executive with building experience asks different questions:

• What's the actual failure mode here?
• Who's responsible when it breaks?
• How do we monitor this in production?
• What's the human-in-the-loop handoff?

Executives without building experience nod along and hope for the best. They greenlight initiatives that were never going to work.

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The Career Risk: 3-5 Years

Boards are waking up.

The first signal is already here: boards are beginning to evaluate executives on whether they have hands-on AI building experience. Not "AI strategy" experience. Building experience. Real exposure to AI systems in production.

In 3-5 years, this isn't a nice-to-have. It's a requirement.

The executives getting replaced won't be the ones who are "anti-AI." They'll be the ones who are AI-aware but AI-incompetent—the ones who can talk the talk but make terrible decisions because they've never walked the walk.

The window to build that competence is now.

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Why Sandbox Experimentation is the Fastest Path

The paradox is this: the fastest way to become an AI-competent executive is not to hire an AI expert. It's to build something yourself.

Sandbox ventures are learning environments. Your job isn't to build a billion-dollar business. Your job is to build AI competence by deploying real AI systems with real constraints.

When you build a Zero Human Company (or even a small autonomous system), you:

1. Make concrete decisions about AI architecture. You're not talking about "machine learning"—you're deciding whether a specific system should use agentic AI or deterministic automation. You learn the differences by building both.

2. Experience failure and recovery. You deploy something, it breaks, you fix it, you redeploy. You understand, at every level, what autonomous systems can and cannot do.

3. Learn to evaluate trade-offs. You discover that "more AI" isn't always better. Speed vs. accuracy. Autonomy vs. control. Cost vs. reliability. These aren't theoretical questions anymore.

4. Build intuition about what "works." After your third failed approach and your first successful one, you stop needing case studies to understand AI systems. You get it.

5. Develop the language to lead others. Once you've built AI systems, you can evaluate whether other people's AI proposals are naive, sound, or delusional. You can ask the right questions. You can spot the BS.

This isn't something you can teach in a workshop. This is what happens when you roll up your sleeves and build.

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The ZeroForce Approach: Venturing as Leadership Development

This is why ZeroForce builds Zero Human Companies with executives, rather than just advising them.

When a CEO or executive team works with us to build an autonomous venture, they're not outsourcing—they're learning by doing. They're making strategic decisions about AI, experiencing the consequences, iterating, and building real fluency.

By the time the venture is launched, they have:

• Hands-on experience building autonomous systems
• Real intuition about AI's capabilities and limitations
• The ability to evaluate other AI initiatives in their organization with authority
• A competitive advantage in any board conversation about AI strategy

They've also proven to their board that they understand AI at a deeper level than their peers.

This is what competitive advantage looks like in 2026: leadership teams that have actually built with AI, not just read about it.

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The Board Question Coming Your Way

In the next 12-18 months, you're going to hear this in a board meeting:

"Can our executives demonstrate hands-on AI building experience? Or are they relying on consultants and subordinates to do the actual AI work?"

If the answer is the latter, expect push-back.

The executives who have built something—even a small autonomous venture—will have credibility their peers don't. They'll make better strategic decisions. Their risk assessments will be more accurate. Their timelines will be more realistic.

They'll know what they're talking about.

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The Skills Gap Isn't Optional Anymore

The difference between executives with real AI fluency and those without is about to become very visible.

To executives without building experience: You have a window. Close the AI competence gap now, before it costs you your seat at the table.

To boards: Stop accepting "we have an AI strategy" as an answer. Ask whether your executives can build with AI, not just talk about it.

To organizations: The leadership development curriculum of the next 3-5 years isn't about MBA programs. It's about sandbox ventures where executives learn AI fluency by doing.

The future executive isn't just someone who understands AI. It's someone who has built autonomous systems, learned from failure, and can make strategic decisions based on real experience instead of hype.

That's no longer optional.

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30-Day Action Plan for Boards

1. Audit AI fluency across your C-suite. Not "do they understand AI?" but "have they actually built and deployed AI systems?" Document the gap.

2. Fund a sandbox experiment. Work with an AI venturing partner to explore a real autonomous system. Involve your executive team directly—this is a learning investment, not just a venture project.

3. Evaluate strategic AI decisions through a new lens. When an executive proposes an AI initiative, ask: "What failure modes have you experienced with similar systems?" and "How do we handle the human handoff when the AI breaks?"

4. Reframe AI training as leadership development. Not a one-day workshop. An active building experience where learning happens through real deployment.

5. Track hands-on AI experience as a leadership competency. Make it clear that future executive advancement requires demonstrated AI building experience, not just strategic thinking.

The executives who do this now will have a 3-year head start on their peers. That's a competitive advantage boards notice.

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Camiel Notermans is the founder of ZeroForce, which helps organizations build Zero Human Companies—autonomous ventures that operate without human intervention. He's spent the last 5+ years working with executive teams to develop hands-on AI fluency through building and deploying autonomous systems in production. He's observed firsthand how executives who have built AI systems make fundamentally different (and better) strategic decisions than those who haven't.