Artificial intelligence in the auto industry

Cars have long been more than just mechanical machines. Often, when they break down, we cannot simply open the hood and solve the problem. This is because a major transformation is taking place on the unseen side of the vehicle. The automobile is becoming digitized not only while driving on the road, but also when it breaks down.

According to a new analysis by S&P Global Mobility, artificial intelligence is becoming increasingly decisive in the field of vehicle repair and fault diagnosis. We usually discuss artificial intelligence in the context of self-driving cars, chatbots, or factory automation. However, one of the most practical and everyday impacts of AI in the automotive sector seems to be emerging the moment a vehicle enters the service center.

Today, many vehicles already generate their own data. The engine, brakes, battery, tires, transmission, exhaust, air conditioning, and safety systems are all monitored in some way. The problem is: the abundance of this data does not always mean convenience. On the contrary, in modern vehicles, fault detection can sometimes turn into looking for a needle in a haystack. A warning code can be the result of another issue; a small sensor error can mimic a larger mechanical problem. For the mechanic, the issue is no longer just listening to the sound, checking the oil, or replacing a part. Reading, filtering, and making sense of the data is now part of the job.

Artificial intelligence comes into play exactly here. By analyzing tens of thousands of fault records, maintenance history, part replacement data, and sensor information, it can offer the technician a faster roadmap. It can filter out the few critical points that truly require intervention from among the hundreds of error codes a vehicle might produce. This means both time savings for the service center and less uncertainty for the vehicle owner.

In fact, AI-powered diagnostic systems promise to reduce this uncertainty. When data from the vehicle is compared with past examples, it is expected that it will become more possible to say, “the fault is most likely caused by this part,” and to determine when the vehicle will break down. Consider a fleet company, for example. For a business managing hundreds of trucks, minibuses, or service vehicles, one of the biggest costs is a vehicle being stuck on the road. When a vehicle is not working, it is not just a repair cost; deliveries are delayed, work schedules are disrupted, and customer satisfaction drops. By calculating which vehicle is more likely to have problems and when, based on maintenance records and sensor data, artificial intelligence can give companies a chance to intervene earlier.

For this reason, technology companies and major suppliers are entering this field rapidly. It is no coincidence that Bosch is interested in startups working in the field of AI-based predictive maintenance, that parts distributors are developing AI-supported diagnostic systems for service centers, or that platforms are emerging that allow workshops to prepare more accurate price quotes. Because the automotive aftermarket is growing. As vehicles become more expensive, people use their cars for longer periods. As the number of vehicles out of warranty increases, the maintenance, repair, and spare parts market becomes a more strategic area.

The critical question here is: Will artificial intelligence replace the mechanic?

I don't think we can give a straight “yes” to this question. Because car repair is still a job that requires physical experience, manual dexterity, and practical intuition. Software can predict a fault, but removing a rusted bolt, replacing a damaged part, and testing the vehicle's behavior still require human labor. However, it must be admitted: Craftsmanship will change. The good mechanic of the past was the person who could hear the sound with their ears and understand the problem from the smell of the engine. The good technician of the new era will be someone who understands both mechanics and can read digital diagnostic systems. In other words, craftsmanship will not disappear; it will merge with data literacy.

The training side of this change is very important. In Turkey, the real issue for automotive service centers will not just be buying equipment. It will be necessary to train technicians who can use that equipment correctly, question the suggestions of artificial intelligence, and notice incorrect guidance. Because artificial intelligence is not a “decision-making god”; it is a tool that is strengthened by good data and can mislead with bad data. If service records are incomplete, part replacements are not processed properly, or maintenance history is disorganized, the results produced by the system will not be reliable. In other words, artificial intelligence can bring quality to the service center; but first, the service center's own data must be of high quality.

Another important issue is customer relations. In the near future, vehicle owners may learn about faults not through technical codes, but in more understandable language. Instead of saying “the check engine light is on,” the system might tell you “this part related to the fuel system needs to be checked, the risk level is medium, it would be good for you to go to the service center within this timeframe.” Solutions that explain the vehicle's problem to the user in simple language through sound, image, or driving data may even become widespread. This reduces the information asymmetry between the car owner and the service center. If the customer understands better what is being changed and why, trust also increases.

But one must also see the other side of the coin. In whose hands will the repair and maintenance data be? The vehicle manufacturer, the service center, the insurance company, or the technology platform? Will independent service centers be able to access these systems, or will data turn into a new area of monopoly in the hands of big players? While artificial intelligence can bring transparency to the customer, it can also create new dependencies within closed systems. Especially in the age of connected vehicles, the right to repair and data access will be one of the most important debates of the coming years.

Ultimately, car repair is no longer just a matter of jacks, wrenches, and spare parts. Software, data, algorithms, and service quality are all sitting at the same table. If used correctly, artificial intelligence can find faults faster, reduce unnecessary part replacements, make maintenance costs predictable, and increase the driver's trust in the service center. If used incorrectly, it can weaken craftsmanship, exclude independent service centers, and leave the customer at the mercy of digital decision-making processes they do not understand.

Therefore, the issue is not whether artificial intelligence enters the auto industry or not. It already has. The real issue is whose benefit it will work for. Will it be a tool that strengthens the mechanic's hand, provides transparency to the customer, and increases service quality; or will it be a new black box that keeps the car's hood a little more closed and its data a little more inaccessible?