In Industry, the Real AI Battle Isn’t the One You Think By Bruno Bouygues
While the tech giants wage an all-out race to build general-purpose AI models, another revolution, quieter but just as profound, is unfolding on the factory floor. Everywhere it takes the same very concrete form: systems able to draw on tens of thousands of technical documents to give teams a reliable answer within seconds. This revolution is not about the raw power of algorithms, but about an intangible asset that is hard to replicate: the body of data and domain knowledge patiently accumulated over decades. For industrial leaders, the challenge now is to step back from the media mirage that emerged when generative AI reached the general public in late 2022. Behind the catch-all label “AI” lie technologies at radically different stages of maturity, and confusing them at the moment of investment would be risky. In our sector, artificial intelligence is nothing new: the current disruption is not a birth, but the acceleration of a digital continuum we have been navigating for more than twenty years. To make the right decisions, you need a compass.
The first two cardinal points of that compass are already firmly embedded in our products and production lines. These families are not watertight; they often combine within a single system, but distinguishing those to help in deciding. First came deterministic systems, based on physical laws and explicit rules. Strictly speaking, they belong more to control engineering than to AI, but they form the foundation on which everything else rests. They are what govern real-time process control (welding, machining, injection molding, process chemistry), instantly adjusting parameters to guarantee product quality, whatever the operator. Then comes supervised learning, a lever of performance as powerful as it is discreet, which feeds predictive maintenance and machine vision. Its strength lies in an error margin that is statistically measured and validated. It is used to detect anomalies in production and to mine after-sales feedback in order to anticipate recurring defects.
The third family on this compass, generative AI, is the most visible, but also the youngest. Its very nature demands caution: these models do not calculate a truth, they explore plausibility. While they are excellent synthesizers, they tend to invent the moment information is missing. Today, generative AI remains, in most cases, too unstable to sit at the heart of critical, real-time decision loops. Its rightful place, for now, is at the periphery: in the interface and in orchestration. This is where the fourth approach enters the stage, arguably the most promising for our sector: hybrid and agentic systems. More than a distinct technological family, this is an architecture that puts generative AI in service of the rest. Here it plays not the role of the brain, but of the conductor. It guides the user and connects deterministic tools, calculators or databases that do the substantive work themselves.
This is where the true value hides. It does not lie in choosing this or that fashionable model, but in the unglamorous groundwork: enriching one’s products, documenting one’s processes, adapting one’s information flows, converting historical data into a structured corpus that is intelligible to the AI of today and of tomorrow alike. That is the real challenge. For an industrial leader, the decision now comes down to three questions: which AI should be deployed, deterministic, supervised, and generative or hybrid? For what use, critical or peripheral? And at what point in the value chain, and at what cost?
No one knows which approach will dominate ten years from now, and that is precisely why it would be imprudent to bet everything on the current state of the art. The only certainty is that all of them, without exception, will need to be fed with documented, structured domain data. It is this foundation, not any particular model that will decide the winners. The industrial company that invests today in its body of knowledge is not placing a bet on a technology: it is preparing ground that every generation of AI, present and future will come to cultivate.
Connect with Bruno Bouygues on LinkedIn
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