For the last several years, much of the AI conversation has centred on one question: which model is smartest?

That made sense when raw model capability was the biggest constraint. Every new frontier model expanded what was possible.

But as AI becomes more widely available in different sizes, capabilities and forms, I think the more important question is becoming: how can I leverage all of the intelligence available to me in the best way?

Answering that requires systems flexible enough to take advantage of the leading frontiers of general intelligence while also exceeding those frontiers in areas of deep specialisation and knowledge.

Today, we are launching Thomson, our own AI model built specifically for professional work. It is one of the clearest expressions yet of how Thomson Reuters is evolving as an AI technology company, and of a broader change I believe is coming to enterprise AI.

The future will not be every company sending every problem to the largest model available or the model with the best scores across the widest range of benchmarks. It will be organisations developing the ability to apply the right intelligence to the right work.

And as AI becomes core infrastructure for the enterprise, I don’t think companies will want to outsource every layer of intelligence that determines how their most important work gets done.

Intelligence should fit the task

Ask a rocket scientist to fix an electrical fault in your house.

They could probably work it out, but they’d likely bring more complexity to the job than it needs. And they may miss things an experienced electrician would catch instinctively. That isn’t because the electrician’s job is smaller. It’s a different job, mastered just as deeply. AI can work the same way.

Frontier models are extraordinary systems, and they will remain a critical part of the AI stack. There are problems where frontier intelligence is absolutely necessary, and in some cases clearly the best choice. When the path to the right answer is unclear, frontier models excel at figuring it out, drawing on a wide array of tools and information along the way.

But professional work does not always fit that shape. A legal brief, a contract, a tax return: these all have outcomes that require a high degree of accuracy, and the people relying on them need to defend and be accountable for them. The defining challenge isn’t creativity; it’s precision, and it repeats across thousands of narrow tasks rather than one open-ended one.

All of these tasks do not necessarily need the same model.

That is why model routing and orchestration matter. The system should be able to understand the work being done and determine what kind of intelligence is best suited to it.

For the user, that complexity should largely disappear. They should simply get the best possible outcome, applying their own judgement, experience and oversight where needed.

Thomson is proof that specialised intelligence works. It’s built to excel at what professional work actually demands: precision, domain fluency and verifiability, optimised specifically for the professional environments we understand best.

Sovereignty is about owning what makes you different

There is another important shift happening alongside this.

For enterprises, AI sovereignty should not mean cutting yourself off from frontier labs or trying to build everything yourself.

AI sovereignty is about owning the layers of the stack that matter to you, but ownership does not mean exclusivity. You can control your own capabilities while still leveraging the frontier of general intelligence for the things it does best. The goal is to own the capabilities that are strategically important differentiators to your business, the things only you can do or that you do better than anyone else.

As access to frontier models becomes broadly available, access itself becomes less differentiating. What matters is what you can build on top of that intelligence and what you can develop that your competitors can’t simply buy from the same provider.

Companies spend decades building proprietary knowledge, data, expertise and workflows. As AI becomes a more fundamental part of how work gets done, it makes sense that some of that differentiation should exist at the model layer too.

That is part of what Thomson represents for us.

Thomson Reuters has deep expertise in professional workflows and authoritative content built over generations. Thomson gives us the ability to encode more of those advantages directly into the intelligence layer itself.

At the same time, we will continue to work with leading frontier model providers. These are complementary capabilities, not competing philosophies. The opportunity is to know when frontier intelligence is best, when specialised intelligence is best, and how to bring the two together.

A different kind of AI advantage

I think that will become an increasingly important source of competitive advantage.

The advantage won’t come simply from having access to the most powerful model or from owning your own. It will come from building an AI architecture that can deliberately use different kinds of intelligence based on the work being done.

The first phase of generative AI was largely about proving how capable general-purpose models could become. The next phase will be about engineering those capabilities into systems designed for specific environments, standards and outcomes.

For professional work, the winners will be the organisations that know which intelligence to use, when to use it, and which parts they need to control themselves.

The next competitive advantage in AI will not come from access to intelligence alone. It will come from knowing how to orchestrate it, and knowing which intelligence is important enough to own.