Here is a quick test of your AI strategy. If the honest summary of it is "we are rolling out ChatGPT", or Copilot, or Gemini, or some chatbot built on one of them, then you do not have an AI strategy. You have a subscription.
That is not a criticism of the tools. They are genuinely useful. It is a criticism of mistaking them for the whole of AI, and mistaking access to them for an advantage.
Blindsided by LLMs
Something strange has happened over the last couple of years. Awareness of AI has gone through the roof, and understanding of it has barely moved. Most leaders now quietly equate "AI" with large language models, the things that write text and hold a conversation. I call it being blindsided by LLMs: the one form of AI that is easy to see has crowded out all the others.
It matters because of a simple competitive fact. The frontier language models are available to everyone, through the same handful of APIs, at the same price. Your biggest competitor can buy exactly what you buy, on the same afternoon. So whatever edge you think you are getting by adopting an LLM, they are getting too. Adopting the thing everyone can adopt is not a strategy. It is keeping up.
LLMs are one tool in a large box
The deeper problem is that an LLM is often not even the right tool. Language models are one corner of a much larger field. Depending on the problem, the technique that actually moves your numbers might be demand forecasting, optimisation, anomaly detection, recommendation, computer vision, simulation, or a plain well-tuned predictive model that is cheaper, faster, more accurate, and more explainable than any chatbot. These are not exotic. They are mature, proven, and frequently a far better fit than asking a language model to do a job it was never built for.
When the only tool you reach for is the one in the headlines, you end up forcing every problem into the shape of a chat interface. Some problems fit. Many don't.
Where the advantage actually lives
If the model is commoditised, where is the edge? In the things that are yours and cannot be bought off a shelf. Your proprietary data. The specific problems in your business that are worth solving. And, above all, the willingness to redesign the work around the technology rather than bolting it on top. The model is the commodity. The advantage is everything you wrap around it.
So what do you actually do?
Not the obvious thing, which is to pick a tool and roll it out. The wiser order is the reverse: start from the parts of your business where AI could genuinely change the economics, and only then match each one to the right technique, which may or may not be an LLM. That is slower and less exciting than announcing a chatbot. It is also the difference between AI spend that shows up in the numbers and AI spend that doesn't.
The companies that win the next few years will not be the ones that adopted AI first. Almost everyone will have adopted it. They will be the ones who understood that AI was never one thing, refused to be blindsided by the single form of it everyone was talking about, and did the unglamorous work of matching the right tool to the right problem.
If you want help working out where AI could actually move the needle in your business, and which kind of AI that even is, I am happy to talk it through. Let's talk.
Related: The people making the biggest AI decisions understand it the least · Why your AI spend isn't showing up in the numbers · Stop counting AI use cases. Redesign three value streams instead
