Talk to enough business owners about AI and a pattern starts to show. Somewhere in the conversation, someone admits to feeling behind. Not behind a specific competitor necessarily, more behind an idea of what a proper AI strategy is supposed to look like: bigger tools, more data, a platform built specifically for the job.
It’s an understandable worry. It might also be pointed at the wrong problem.
Most of the public conversation about AI right now is a conversation about scale. Which company has built the biggest model. Which country is pouring the most money into data centres. Whose infrastructure ends up dominating the next decade. That conversation is relevant if you’re one of the handful of organisations actually building the models. It matters a great deal less if you run a fifty-person business in Oxfordshire.
Because whether AI genuinely helps your business is never really about how big the model is. It’s about how well you already understand the problem you’re pointing it at.
I was reminded of this recently at a B4 Corum Event, where Robert Pilkington, joint CEO of Ebbon Group, talked about his business, which has spent twenty-five years steadily building automotive technology. It started out solving problems for the group that owned it, then took that capability into the wider market. Along the way, it made some deliberate choices about what not to chase. At one point it sold off part of the business so it could concentrate on the part it actually understood best.
When AI eventually arrived, it wasn’t the beginning of the story. It arrived after two decades of accumulated knowledge about a specific industry, its customers and its recurring frustrations. The AI didn’t create that advantage. It just enhanced it.
I wrote about The Ebbon Group in more detail in a longer piece on the Thinking in Fields website, but the practical point travels well beyond automotive.
If you run a business that has spent years, or decades, becoming genuinely good at something specific, that expertise isn’t a consolation prize while you wait to afford better AI. It might be the actual advantage. AI can widen the number of options you consider and speed up how quickly you can test them. What it can’t do is tell you which problem in your business is actually worth solving. That still depends on knowing your field better than the tool does.
For most businesses, the useful question probably isn’t how do we get more AI capability. It’s closer to: what do we already know that almost nobody else knows, and where might a bit more intelligence make that knowledge go further?
The businesses that get the most out of AI over the next few years may not be the ones with the biggest budgets for it. They may simply be the ones who already knew, long before AI arrived, exactly which problem was worth solving.
ABOUT DAVID FINCH
David Finch is the Founder of Thinking in Fields. He works with founders, CEOs and leadership teams to help them convert intelligence into coherent action, identifying where decisions are stalling, ownership is unclear, and organisational friction is preventing progress. Thinking in Fields helps organisations turn better intelligence into better outcomes. The result is greater organisational coherence, enabling businesses to grow, adapt and create long-term value in an age of abundant intelligence.