While the tech giants are engaged in a frantic race for general AI models, another, quieter revolution is taking place in the heart of factories. It has the same very concrete face everywhere: systems capable of drawing on tens of thousands of technical documents to offer teams a reliable response in a few seconds. This revolution does not relate to the raw power of algorithms, but to an intangible asset that is difficult to replicate: the wealth of business data and knowledge, patiently accumulated over decades. For industrial leaders, the challenge is now to step back from the media mirage born with the general public arrival of generative AI at the end of 2022. Behind the generic acronym “AI” hide technologies with radically different maturities, which it would be risky to confuse when investing. Because in the industry, artificial intelligence is nothing new: the current disruption is not a birth, but the acceleration of a digital continuum in which the sector has been navigating for more than twenty years. To make the right decision, you need a compass.
The first two cardinal points of this compass are already firmly anchored at the heart of products and production lines. These families are not watertight, they are often combined in the same system, but distinguishing them helps you decide. First there are deterministic systems, based on physical laws and explicit rules. Strictly speaking, they are more automatic than AI, but they form the foundation on which everything else is based. They are the ones who control the regulation of processes in real time, welding, machining, injection, process chemistry, by instantly adjusting the parameters to guarantee product quality, regardless of the operator. Then comes supervised learning, a performance lever as powerful as it is discreet, which drives predictive maintenance and machine vision. Its strength lies in a statistically measured and validated margin of error. It is used to detect anomalies in production and use feedback from after-sales service in order to anticipate recurring defects.
The third family of this compass, generative AI, is the most visible but also the youngest. Its very nature requires caution: these models do not calculate a truth, they explore a plausibility. Although they prove to be excellent synthesizers, they tend to invent as soon as information is lacking. Today, generative AI remains, in most cases, too unstable to be placed at the heart of critical real-time decision loops. Its rightful place is still on the periphery, in the interface and orchestration. This is where the fourth approach comes into play, arguably the most promising for the industry: hybrid and agentic systems. More than a distinct technological family, it is an architecture that puts the generative at the service of the rest: generative AI does not play the role of brain, but of conductor. It guides the user and connects deterministic tools, calculators or databases, which carry out the background work. This is precisely the principle of the RAG systems which are deployed in factories today.
This project reveals where the true value lies. It does not lie in the choice of this or that fashionable model, but in the work behind the scenes: increasing its products, documenting its processes, adapting its information flows, converting historical data into a corpus structured and intelligible by the AIs of today and tomorrow. This is the real challenge. For an industrial manager, the decision now comes down to three questions: which AI to mobilize: deterministic, supervised, generative or hybrid? For what use, critical or peripheral? And where in the value chain?
No one knows which approach will dominate in ten years, and this is precisely why it would be unwise to bet everything on the current state of the art. The only certainty is that all, without exception, will need to be fed by documented and structured business data. It is this base, and not this or that model, which will decide the winners. The industrialist who invests today in his heritage of knowledge is not making a bet on a technology: he is preparing a ground that all generations of AI, present and future, will come to cultivate.
