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55ThaNNIvErSary


IIOT & SMART MANUFACTURING FEATURE the ai transformation


Tim Dickson, chief digital and information officer, regal rexnord, explores how AI can become a truly transformative business tool


T


here 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, but being


‘transformed’ by 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. These are entirely different paths, and most industrial businesses don’t even realise they’re making the decision of which one to follow.


ai in load-bearing operations Over the last 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 – in fact we have done plenty of this at Regal Rexnord 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. All 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 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 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. This is 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.


ai as a tool, not a skills replacement 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, and 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.


an evolving transformation Businesses need to avoid thinking of AI transformation as a finite program with a start and an end. 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.


in the future 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 can now move at pace because of it. 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.


regal rexnord regalrexnord.com


www.designsolutionsmag.co.uk


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