“Hold on, I see, oh yeah that’s it, I see that the 3D printer located in the West Wing of the hangar in Vaulx-en-Velin is going to conk out on Wednesday, August 12th, 2026.” That’s also what AI is all about. Matthieu Beghin, Pre-sales Manager at Prodware, dives into the subject and shares his expert insights on the latest technological advancements.

Over the last couple of years, we have seen and continue to see how predictive maintenance is evolving as a concept, and how Artificial Intelligence enabled applications will eventually become widespread and available to all. Bringing these two technologies together will trigger a major paradigm shift in the industrial sector. In other words, the advancement of these technologies is a serious game-changer for the industry of today and tomorrow. Is Predictive Maintenance in an AI-enabled world the beginning of a new industrial revolution? Yes, but not only…

Trying to guess what the future holds without embracing the present just doesn’t fly.

Because today, when it comes to understanding and dealing with maintenance, machines, artificial intelligence…it’s still kind of blurry. I mean if we’re aiming for clear-cut, well established schemes and very precise trouble shooting techniques that can pinpoint the cause of a problem flawlessly, we’re simply not there yet. While we are enjoying some of what AI has to offer and experiencing the far-reaching business steering capability that comes with Big Data and Data Mining, we are still basking in some kind of childlike enthusiasm, dumbfounded by technology with fantasy-like expectations. A revolution, if we are on the brink of a revolution that is, will surely not happen overnight…and not even tomorrow

Reaching for the Future via Inertia

One thing for sure though: so far, we’ve only tapped into a tiny fraction of the potential for using predictive maintenance solutions in industry. This is because AI itself is still in its early stages. But the advancement and development of these two technologies are closely intertwined.

These slow surfacing developments beget the following consequences. First off, it is understanding that in a not so mature market it is much easier to identify the most relevant players. Industries and large corporations stand out as the primary recipients of this type of solution. This is not only due to resources but also technological maturity. Moreover, it gives us an opportunity to examine the potential connections between predictive maintenance and existing technologies. Data collection is poised for broader implementation within production systems. Data is ubiquitous and ready for more effective utilization. Moreover, while data extends beyond industry, predictive maintenance could broaden its impact beyond its current confines.

Once we establish the connection between available data, computing power and industrial infrastructure, the opportunities are almost limitless. This could mean significant cost savings, international expansion along with a new 4.0 globalization model…The future advancements in predictive maintenance could lead to industry growth on a massive scale.

Challenging an Established Ecosystem

This inertia has various consequences that will fundamentally change the roles of all the stakeholders involved in the running of machines. Before everything was very clear cut: there was one supplier, a machine operator, another operator in charge of maintenance that services the machine on a regular basis and a technician who restarts the machine once it is repaired. Predictive maintenance changes all of that in one fell swoop: machines are fitted with a whole set of sensors that gather data that AI will then analyze to estimate the lifespan of each machine or device (RUL: Remaining useful life).

Machines then transfer data that can be used to run a technical assessment. Just like in Minority Report, the novel written by Philip K. Dick that was turned into a movie of the same name directed by Steven Spielberg. It is the story where criminals are arrested before they commit their crimes by a special crime unit called Precrime. The analogy here is that machines are maintained, spare parts changed before the machines break down and remain out of order for a couple of days. It’s all about doing what it takes before and avoiding the occurrence of incidents. This is what we mean about predictability. It’s with the IoT (the Internet of Things) that we’ll really appreciate how Artificial Intelligence will unleash its full potential. And of course, data analysis provides more and more in-depth statistical analyses that not only make managing maintenance that much easier but also impact retroactively the manufacturing and design of spare parts (shape and material) for improved effectiveness and better resistance to wear and tear. The bigger the fleet of machines, the more data there is, and the more manufacturers should factor in handling of the maintenance of their fleet of machines. Collecting operational data from 10 machines doesn’t provide the same insights as collecting data from 10,000 or 1 million machines.

AI-enabled data processing must now be shared or pooled to allow the entire production system to benefit from the analysis of a significant volume of data.

If data now becomes the main source of information and used to prevent machine breakdowns, then collecting this data also becomes incredibly strategic for today’s industry. Skill building, training programs, staff downsizing, the overall human dynamic is being disrupted. The Data Manager now becomes pivotal for a supplier of industrial machines. For an industrial player, the pooling of the analyses of data of a production unit can prove to be a significant asset in an increasingly competitive market. Yet, for a maintenance technician, the future may seem uncertain, and they may have to adjust and adapt to the new production ecosystem.

And, data collection, is an area of expertise that reaches way beyond the industrial sector per se. IoT is one of the trending technologies that is growing at an incredible pace. In fact, a smart refrigerator may turn out to be as useful as a machine that is part of a production unit, provided we properly leverage the data to generate value. In other words, the pivotal role that data now takes on will break down the silo mode of industry, forcing it to redeploy its set-up, oblivious to any consideration of physical barriers.

A Different Culture

But beyond the organizational changes brought on through time by preventive maintenance and predictive maintenance in the past, now and tomorrow, they will undoubtedly have an impact on the entire social structure of society. The industrial sector will be able to improve production processes (by reducing number of errors), make them more cost-effective (economies of scale on maintenance costs, less staff…), and quicker (by limiting as much as possible the downtime of machines). Better, cheaper and faster: what any business would dream of. But maintenance is just a part of the big picture. It’s Artificial Intelligence with a capital A & I that will completely disrupt production as we know it and therefore society as a whole. Because what comes with optimized predictive maintenance is a whole host of factors: interconnected means of production (with as many operational status sensors), a constant flow of market-related data provided to a business, optimized performance, and constant automation of production volume. A kind of 4.0 lean management approach. In a nutshell, this may be the advent of the factory of the future. But where does that leave humans and their added value? That still needs to be cleared up.

We also hear that some people actually “speak to machines.” They also listen to them. They know how to pick up a sound that doesn’t sound right or an apparent glitch in the motions of the machine. That is the result of an expertise garnered over so many years of experience. But without seeking to downplay their merit or expertise, it must be said that the computing power of Artificial Intelligence applied to predictive maintenance could soon challenge these highly regarded skills.

The cultural shift brought about by the introduction of AI-driven solutions also prompts consideration of responsibility. As AI’s scope widens, so do the responsibilities that go with it. However, responsibility is fundamentally a human trait. How do we apply it to AI? Consider the example of connected cars: statistics show they are safer than human drivers, with fewer accidents and tragedies. Yet, it remains unacceptable to accept and tolerate any harm caused by AI-driven vehicles. This is a true ethical dilemma.

The “optimization” aspect of a technological solution, no matter how effective it may be, is far from being the most decisive factor in its acceptance and deployment. Cultural change is not merely a technological issue but involves a distinctly human dimension that progress can never overlook. At least, let’s hope so.

Matthieu BEGHIN is a graduate of the University of Technology of Compiègne (UTC) with a degree in Mechanical Systems Engineering. He has worked in France, China, and Morocco, initially in industrial positions before transitioning to digital transformation projects. As a project manager for ERP and then CIO in various companies, focusing on machinery manufacturing and steel transformation, he has witnessed the importance of production equipment and their operational maintenance. He has also worked twice in IT service companies, providing him with a comprehensive perspective on industrial and digital issues. Mindful of advancements in technology as well as shifts in society, he shares his insights on ongoing changes in the maintenance field, particularly concerning the emergence of Artificial Intelligence.