Did you think electronics was the ultimate revolution in the automotive industry? Well, you’re not at the end of your surprises because the sequel is just as promising.
Did you think electronics was the ultimate revolution in the automotive industry? Well, you’re not at the end of your surprises because the sequel is just as promising. Thanks to Artificial Intelligence. Thanks to it, it is now possible to detect the invisible signals that precede a failure. This radical revolution is inspired by industry 4.0. In this specific area, predictive maintenance has revolutionized the way machines are maintained and productivity optimized (1).
Reactive failure: a model that collapses
Since the beginning, automobile mechanics have operated according to a simple but slightly fatalistic maxim: enjoy your car and go see a mechanic if you break down! And, rest assured, neither the protective ritual of the 15,000 kilometer service, nor the obligatory technical inspection every two years will be enough to eliminate the risks. In fact, an automobile part can last 50,000 kilometers in one case and fail at 20,000 kilometers in another. As for the battery, it can last five years in a temperate region and break down after two harsh winters. The result: unnecessary interventions on one side and costly downtime on the other. And the more electronics cars have, the more serious the consequences of these malfunctions become. Security, budget, mobility: all these parameters are impacted. The moral of the story? This model based on reactive diagnosis has reached its limits. It was time to invent something else.
Industry has shown the way
Surprisingly, the breakup came from elsewhere. In factories, logistics, energy, chemicals… industrial players understood very early on that repairing after damage was no longer viable. They reacted: thanks to connected objects, maintenance teams were able to install sensors on each machine, collect data continuously and analyze the slightest signals. Artificial intelligence has come to complete this system, by learning to recognize the invisible patterns which precede a breakdown. The results are clear. The use of predictive maintenance has reduced costs by 25 to 30%, while improving machine availability (1). As a result, the global predictive maintenance market has grown from $7.85 billion in 2022 to an estimated $60 billion by 2030 (1). We went from an experiment to a standard. And the automobile industry, which has lagged behind in this area, is catching up.
When AI comes under the hood
With electronics, every recent car has become a rolling sensor. Brakes, battery, engine, tire pressure, temperature: everything is recorded. For a long time, this data lay dormant in embedded systems. AI has changed the game. From now on, algorithms cross millions of histories, compare behaviors, identify imperceptible variations and predict the remaining life of components (2). The impact is concrete: a critical breakdown can be avoided and an intervention can be scheduled before the breakage. Like machines in industry, a vehicle can remain available for longer and prevent drivers from having to invest in a new car, resulting in a significant reduction in carbon impact. The market follows this movement. According to Stellar Market Research, automotive predictive maintenance was worth $41.66 billion in 2024 and could reach $191 billion by 2032, with an average growth of 21% (2). The shift is global, rapid and irreversible.
Automobile and industry: a permanent dialogue
The use of predictive maintenance in industry has yielded many benefits. Unplanned machine downtime has been reduced by up to 40%. The latter have also seen their lifespan extended (1). In the automobile industry, the gains follow the same logic with rarer breakdowns and better controlled costs for drivers. Crucially, targeted interventions increase safety (3). Deep learning, digital twins and vibration analysis, these advanced technologies from Manufacturing 4.0, are now being used in the automobile industry (4). And in return, the data collected by millions of rolling vehicles enriches industrial models. They significantly improve the accuracy of predictions and accelerate innovation. Industry and automobiles move forward hand in hand.
The challenges: a shift to secure
This revolution will not happen without conditions. The accuracy of predictions depends on the quality of the data, but vehicles do not all have the same sensors or the same usage environments, particularly company fleets (4). Added to this is the question of cybersecurity. A connected car transmits sensitive information: location, system status, driving habits. Protecting them becomes essential (5). Finally, users will have to be convinced. An alert only has value if it is understood and accepted. Explaining the predictions, reassuring them about their reliability, limiting false positives: confidence will be the key. Without it, the technology will remain underutilized.
This evolution possible thanks to AI is not just a technical evolution. It’s a paradigm shift. Industrial experience has shown that this model works. The automobile is taking over, with its specificities, its constraints and its promises. Tomorrow, breakdowns will no longer be inevitable, they will become the exception. And our mobility promises to become safer, more sustainable and better controlled.
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(1) Grand View Research – Predictive Maintenance Market
(2) Stellar Market Research – Predictive Maintenance in Automotive Market
(3) ConnectPoint – Predictive Maintenance Analysis
(4) ScienceDirect – Predictive Maintenance in Industry 4.0
(5) IoT Analytics – Predictive Maintenance Cybersecurity Challenges
