The AI Leadership Triangle: Vision, Data, and Velocity

By Mike Caprio

There’s no shortage of noise about AI. Everyone’s got a pilot program. Every strategy deck has a slide titled “AI.” But beneath the buzz, very few organizations are actually building AI capability into the bones of their business.

The difference almost always comes down to leadership — not just C-suite buy-in, but operational clarity.

That’s where the AI Leadership Triangle comes in: a practical framework to guide AI adoption and maturity. It’s not theoretical. It’s diagnostic.

The three sides?

• Vision

• Data

• Velocity

Let’s break it down.

Vision: Do You Know Why You’re Doing This?

It sounds obvious, but many teams jump into AI because they feel they have to — not because they’re clear on what problem it’s solving. This leads to fragmented pilots with no through-line to impact.

Strong leadership starts with a real thesis:

“We believe AI can help us [achieve X goal] by [solving Y problem] in [Z workflow].”

If that sentence isn’t clear at the exec level, the downstream chaos is inevitable. Vision doesn’t mean a 5-year plan — it means a 6-month reason.

Data: Are You Feeding the Engine?

Even the most elegant LLM or predictive model is useless without the right fuel. Most organizations sit on mountains of messy or siloed data, and assume a vendor will “figure it out.”

Spoiler: they won’t.

Leadership doesn’t need to become data architects — but they do need to own decisions around:

• Which data matters

• Who controls it

• How it moves across teams

• Whether it’s good enough to trust

If your org can’t describe your core data flows in plain English, you’re not ready to build.

Velocity: Can You Move Without Breaking?

This is the corner most leadership teams ignore.

AI progress compounds. The companies that win aren’t just technically capable — they’re structurally nimble. They’ve reduced friction in:

• Legal reviews

• Procurement timelines

• IT approvals

• Pilot-to-prod transitions

Velocity doesn’t mean recklessness. It means that once you identify a useful use case, you’re not stuck in a 6-month hamster wheel of internal process.

If a prototype works, can it ship? If it fails, can it be replaced?

Why the Triangle Matters

Most orgs over-index on one side. They might have a clear vision and good data, but can’t get anything live. Or they’re shipping fast but with no strategic reason for doing so.

Weakness in one corner breaks the whole shape.

This isn’t a one-time exercise. You can revisit the triangle every quarter — and you should. It will evolve as your AI literacy grows.

Where to Start

• Pick one high-friction workflow

• Clarify what success would look like if AI helped

• Map what data it would require

• Ask what’s stopping you from trying it

That’s it. That’s leadership in action.

You don’t need to become an AI expert overnight. But you do need a framework for progress. And the triangle is a damn good place to start.