AI ‘is not a tool, it is a transformation’

Tim Dickson explores how businesses can turn AI from just another tool to a truly transformative part of their operations.

According to research by McKinsey¹, almost 90% of businesses now have AI running somewhere within the organisation, whether that’s a chatbot on the website, a Microsoft Copilot license in the hands of knowledge workers, or simply an agent summarising meeting notes. The technology has stopped being an exotic novelty and started becoming part of the background fabric of business. But two years into the mainstream generative AI era, the companies pulling ahead are not the ones with the most tools deployed, but rather the ones that have stopped thinking about AI as a tool at all.

The reason for this is that there is a real difference between simply using AI and being transformed by it. Using AI might mean introducing a productivity feature here, a chatbot there – each one a useful improvement to something a person or customer was already doing. Being transformed by AI, however, is a bigger idea.

It means rethinking how the business actually runs, which work genuinely needs a human, where can people be better deployed, which processes can be rebuilt around what AI can now do, and what becomes possible when the two are properly joined up.

The first approach gives you incremental gains. The second is where the true, transformative value lives. They are not the same journey at different speeds – they are entirely different paths, and most industrial businesses don’t even realise they’re making the decision of which one to follow.

The easy part is nearly over

For the past two years, the AI conversation in industry has been dominated by tool thinking. Pick a use case, find a vendor, run a pilot, measure the result. Almost any process can be improved this way: searches get faster, summaries are delivered automatically, a first draft appears in a fraction of the time. We have done plenty of this and the results are impressive. Our internal GPT handles around 2,000 associate queries a month, the website chatbot helps more than a thousand customers a week find what they need, and the thousands of people using Copilot tell us they save two to three hours a month – with most of them putting that time straight back into higher-value work. Those outcomes represent genuine improvements.

However, it’s clear that they are also the easy part. Vendor partners are everywhere, pilots are currently cheap, and the technology has matured to the point where almost any organisation can prove that AI does something useful. The hard part – the one that separates the companies that will matter from the ones that will not – is what comes next.

The question is whether AI can be woven into the end-to-end processes that actually run a business – forecasting, demand planning, sales and operations planning, materials requirement planning, the customer service workflows that stretch across half a dozen systems and just as many teams. These are not chatbot use cases. They are the load-bearing operations a manufacturing company leans on every day, and running AI inside them is a different challenge entirely. It calls for multiple agents working together rather than in isolation, for AI connected to your core systems rather than bolted on beside them, and for those agents to be governed properly – monitored, observable, and with a human expert accountable and responsible for what they do.

None of that is trivial. But it is precisely where the transformation happens, because the moment you stop using AI to help people do their existing work faster and start using it to rethink what the work is, you have crossed from using a tool to developing a new capability.

Why most organisations stall

There is a pattern we see repeat itself in industrial AI. A company runs a few successful pilots, generates real internal excitement, and then watches that momentum evaporate when it tries to scale. The technology works and the pilots prove out, yet the leap from a working pilot to AI genuinely embedded in core operations takes far longer than anyone promised at the start. It is worth asking why this happens, because the answer is rarely the technology itself.

The honest answer is that the unglamorous foundations have not been built. Three in particular tend to be missing. The first is data readiness. AI is only as good as the data that fuels it, and most industrial businesses still have data spread across systems that were never designed to talk to each other, with inconsistent definitions, unclear ownership, and gaps that nobody has been formally responsible for closing. Treating data as a product – with governance, ownership, and clear definitions – sounds bureaucratic, but it is what makes enterprise-scale AI feasible.

The second is governance. With AI capable of touching important processes, the question of what an AI model is allowed to do, on whose authority, and with what oversight becomes a real one. Organisations that have not thought this through in advance tend to discover the gap at the worst possible moment, sometimes with disastrous consequences.

The third is change management – and this is the one that gets the least attention. AI transformation is fundamentally about people, and about whether the people doing the work feel that the change is being done with them or to them. The best technology stack in the world will not deliver value if the people expected to use it feel sidelined by it. The most effective way to deploy AI is as a tool to enhance experts’ skills – not as a replacement for them. Bringing everyone along is often the difference between a pilot that scales and one that stalls.

Transformation is not a project

The key mistake businesses need to avoid is thinking of AI transformation as a finite program with a start and an end. It is not. The technology will continue to evolve, the use cases will continue to expand, and the boundary between what humans and AI agents do best will continue to shift. Treating it as a one-time project leads to the kind of bolt-on, isolated tool deployments that deliver disappointing returns.

Treating it as an ongoing transformation looks different. It means building organisational capabilities – in data, in governance, in upskilling, in change management – that can absorb successive waves of AI technology as they arrive, rather than scrambling to react to each one. It means investing seriously in the people who will be using these tools, not as an afterthought but as a deliberate part of the operating model. And it means accepting that the goal is not to deploy AI, but to become an organisation that knows how to deploy AI continuously, well, and at scale.

The next three years

Ultimately, the companies that will pull ahead over the next three years are not the ones with the most agents in production. They are the ones that have built the foundations to take those agents and embed them in genuinely transformed business processes. They have done the unglamorous work of data, governance, and culture, and who can now move at pace because of it.

That work is not glamorous, but neither is it optional. The AI market has reached the point where pilots no longer differentiate anyone. The next differentiator – the one that will matter for the rest of this decade – is whether an organisation treated AI as a shiny new tool, or as the transformation it actually is.

Tim Dickson is Chief Digital and Information Officer, Regal Rexnord.