AI FOTO™ 02: From Tool Collecting to Strategic AI Adoption

When I talk to organisations about AI, one of the first questions I usually ask is a deceptively simple one.

“So, what are you using?”

The answers are nearly always enthusiastic. ChatGPT, Copilot, Claude, Gemini… perhaps a specialist tool for marketing, another for note-taking, and something someone discovered that promises to transform project management. Before long, a list begins to emerge, and it’s often an impressive one.

On the face of it, that’s encouraging. It tells me people are curious. They’re experimenting, exploring and trying to understand where AI might make a difference. I would much rather see that than an organisation standing still, hoping AI will somehow pass them by.

But then I ask a second question.

“Which of those tools has made the biggest difference to the way your organisation performs?”

The conversation almost always changes.

People pause. They look at one another. The discussion becomes less certain.

This isn't because AI isn’t delivering value, but because many organisations have never stopped to define what value actually looks like.

Tool Tactics shouldn't be before Strategic AI Adoption

It’s a pattern I’ve seen repeatedly over the past year. The excitement of AI has created a rush to experiment, and rightly so. The technology is evolving at a remarkable pace, and nobody wants to be left behind. The temptation is to keep trying the next tool, then the next, always wondering whether something better is just around the corner. That isn't strategic AI adoption; it's a scatter gun approach.

There’s nothing inherently wrong with experimentation. In fact, I think it’s essential. The organisations that learn fastest are usually the ones prepared to explore and occasionally get things wrong.

When AI Experimentation Becomes the Strategy

The problem comes when experimentation becomes the strategy.

Without really noticing, organisations start collecting tools rather than solving problems. Different teams discover different applications. Similar challenges are tackled in different ways. Knowledge becomes fragmented, licences begin to multiply, and nobody is entirely sure which platform is best suited to which task.

The irony is that more technology often creates more complexity.

It reminds me of conversations I used to have during large-scale digital transformation programmes. Organisations would spend months selecting the perfect technology platform, convinced that once the software was in place, everything else would naturally follow.

It rarely did.

Success depended far less on the technology than on the clarity of purpose behind it.

AI feels remarkably similar.

Focusing on Business Outcomes Instead of AI Tools

The organisations making the greatest progress aren’t necessarily the ones with the longest list of AI subscriptions. More often, they’re the ones asking different questions.

Instead of beginning with, “Which AI tool should we buy?”, they begin with, “Where are we creating unnecessary effort?” They look for the repetitive tasks that frustrate people, the manual processes that consume valuable time, the information that’s difficult to find or the decisions that consistently take longer than they should.

Only then do they ask whether AI can help.

It’s a subtle shift in thinking, but it changes the entire conversation. AI stops being the destination and becomes one of several ways of reaching it.

What Strategic AI Adoption Really Looks Like

I remember working with one leadership team that initially believed they needed a comprehensive AI strategy before they could make any meaningful progress. As we explored the way their organisation actually worked, a different picture emerged. Their biggest challenge wasn’t a lack of technology; it was the time people spent searching for information that already existed in multiple places.

Once that became clear, the discussion about tools almost disappeared. The focus shifted to solving a real business problem, and suddenly the technology had a purpose. That’s what strategic adoption looks like.

It doesn’t mean using less AI. Quite the opposite. It often means using AI more effectively because every implementation has a clear reason for existing. People understand why they’re using it, what success looks like and how it contributes to a wider objective.

In my experience, that’s the point at which AI begins to feel less like an interesting innovation and more like a genuine business capability.

As AI continues to evolve, organisations will no doubt continue discovering new tools. That’s inevitable, and it’s healthy. Curiosity should be encouraged.

But curiosity works best when it’s guided by purpose.

The businesses that create lasting advantage won’t be those that own the most AI tools. They’ll be the ones who develop the discipline to focus on outcomes first and technology second.

The FOTO Reflection

One of the greatest misconceptions about AI is that progress comes from having access to more technology.

In reality, progress comes from understanding the obstacles that prevent people doing their best work and applying technology thoughtfully to remove them.

That’s the difference between collecting tools and creating capability, and that’s how we move from obstacles to outcomes.

If your organisation has begun experimenting with AI but is unsure how to turn isolated successes into a coherent strategy, our AI Wayfinder Readiness services and Wayfinder Fit for AI diagnostics are designed to help you understand where you are today and identify the practical steps that create lasting capability.