"You cannot overtake fifteen cars in sunny weather. But you can when it's raining."

Ayrton Senna, F1 driver, said that. And it captures where we are right now.

In 2010, Tesla became the first American automaker to go public since Ford in 1956. Not because electric vehicles were new. They weren't. But a window had opened. Battery technology crossed a viability threshold. Incumbents were slow to respond.

Elon Musk didn't invent the electric car. He recognised the window and overtook before it closed.

This is how disruption works. Not as a gradual tide, but as a narrow opening. A moment when technology matures, incumbents hesitate, and the prepared surge ahead all at once.

That window is open now. In sport. In AI.

The arbitrage isn't technology. Everyone can access AI. The arbitrage is in understanding.

Most organisations see a chatbot.

They don't see what's actually coming.

Questions answered that seemed impossible. Practitioners freed from the mundane to connect, interpret, coach. Prediction improving exponentially, but only for those who've built the foundation.

The organisations capturing this moment won't just do it with better software.

They'll do it with better people.

Bridge-builders.

Practitioners fluent in both high-performance sport and AI's frontier. With the strategic mind to translate between them. They are rare. Unevenly distributed across sports and geographies.

Here's what makes them invaluable: the same skill that unlocks value from coaches unlocks value from AI.

Consider what the best practitioners actually do. They sit with a physio who knows something's wrong but can't articulate it. A coach who senses a pattern but can't name it. They extract the real question beneath the stated question. Deliver what solves the actual problem, even when the person couldn't envision it themselves.

AI works the same way. Models don't know what you actually need. They respond to what you give them.

Getting the best from AI requires the same empathic, interpretive skill. Understanding how it thinks. Knowing what context it needs. Engineering inputs so outputs carry the nuance end users require.

The people who bridge gaps between humans can bridge the gap between humans and AI.

But having these people isn't enough.

They need proximity to problems. Not briefs to execute.

There's a difference between receiving a task and being in the room when the question is first framed. One is delivery. The other is strategy.

Maybe the issue isn’t new roles, but old assumptions about how work is structured. Ensuring data-strategic minds have a finger on the organisation's real concerns. So they are proactive, not reactive.

You don't need more soldiers. You need more leaders. Not necessarily in positions of leadership. People with the conviction and capacity to shape direction.

The cost of standing still is concrete.

Some teams will answer complex questions in minutes. Others will still be staring at two-variable charts. For some, models reveal patterns humans can’t see. Elsewhere, people will be stuck doing work AI should already handle.

The competition will seem to know what you're about to do. Not magic. Just better preparation meeting better tools.

The rest will wait for a startup to productise generic tooling. Meanwhile, rivals make decisions using their own data, their own models, and their own hard-won knowledge.

This gap won't close. It compounds.

Three moves emerging across leading organisations:

The talent market for AI-fluent sports practitioners is small. Shrinking fast as early movers secure them.

Those who've been preparing are coming up on the inside. Building foundations for innovation they know is coming.

Sports teams spend years chasing a one percent edge. AI is not a one percent edge. It's a step-change.

The kind that redraws leaderboards. The kind that can change the weather.

It is about to rain, and there will be a lot of passing.

Where you finish is up to you.