5 Ways Artificial Intelligence is Transforming the Automotive Industry


For an industry that’s as highly technological as the automotive industry, the adoption of artificial intelligence (AI) and its subsets is surprisingly slow. A recent report found that roughly 10% of the companies surveyed are using AI-based initiatives at scale. The surprise doesn’t just come from the fact that automakers are accustomed to embracing new tech to improve productivity – it stems from the huge potential AI has for auto companies.

It’s true that the same report says that more than 80% of auto businesses are considering AI as the technology that will be at the core of their processes. But why that doesn’t translate into a deeper integration of AI-powered solutions in the industry? Why aren’t auto companies hiring developers, outsourcing to Latin American partners, and implementing this technology?

Maybe they perceive artificial intelligence (and machine learning, one of its most promising subsets) as too new and too risky a technology. However, AI is already making splashes across the auto industry. As illustrated by the following 6 uses, it seems that the technology is pretty ready for its widespread adoption.

Autonomous Driving

Driverless cars are on their way to become a thing, as they are expected to hit the mass markets in the coming years. It’s only natural, then, that anyone thinking of AI in the automotive industry, thinks of self-driving vehicles first. And while we’ll see the ultimate use of artificial intelligence in this sense when fully-automated cars finally conquer the roads, reality shows that there are companies already using intelligent driver assistance systems in their vehicles.

These systems help in several ways, from emergency braking and assisted parking to cross-traffic detectors and blind-spot monitoring. In a way, these smart features will help people grow accustomed to the AI presence in their vehicles. Easing in on the technology seems like a calculated decision, but in reality, driverless cars still need to be adjusted. Although machine learning algorithms are already furthering the development of these vehicles (with Tesla leading the way), the truth is that we aren’t quite there yet.

Predictive Maintenance

Using machine learning systems to anticipate when a piece of equipment might need maintenance isn’t a feat reserved for the automotive industry. However, the use in the sector goes beyond the manufacturing plant and into the vehicles themselves. The idea is to use sensors installed in the cars for a predictive analytics model to gather data and estimate when will the vehicle need to visit the garage.

The best thing about this is that predictive algorithms can provide customized estimations that are tailor-made for each individual driver. What’s more – the sensors will also be used to send the information back to the companies, which will use said data to improve the cars’ design and detect new business opportunities.

Smart Manufacturing

Everything that’s backstage in auto manufacturing can also get a boost with AI-based solutions. With them, automakers can be more productive, efficient, and ensure a higher quality for the final product. That’s why the assembly robots that the industry has been using for more than half a century are getting smarter. Today, there’s a whole set of them that can help with assembly, distribution, and quality control.

Predictive analysis is also used on a more strategic level. Tools based on AI are used to ensure the inventory is ideal according to seasonal demands across different facilities. This can help to enhance the relationship between automakers, junk cars masters and their partners, both the ones responsible for the parts needed to make the vehicles and the logistic partners across the chain.

Smart Prototyping

Developing new products has always been a challenging endeavor, especially for something as unique, valuable, and sensitive as a car. Vehicles don’t just have to look good, be comfortable, and function properly – there are a lot of security standards and requirements that need to be met before a new model hits the roads.

Fortunately, AI tools can also be used in this stage, mainly to reduce the costs of evaluation and performance tests. Artificial intelligence can use the data gathered during the tests to identify potential security and performance issues as well as opportunities for new features. With the help of AI, automakers can analyze everything from the design itself to how the mechanical and digital systems perform while in use.

Mobility as a Service

Perhaps the most unexpected change of artificial intelligence in the automotive industry is one that still isn’t here but that’s brewing right now. It involves the concept of car ownership, which will be threatened by the introduction of driverless cars in the market and the rising presence of ride-sharing services. Given that it’ll be easier to just go out and call a self-driving vehicle through an app, the idea of owning a car will feel unappealing.

That’s why automakers are already considering reinventing themselves as mobility companies. That will imply that they won’t just create the vehicles – they will put them on the streets to work for them. Naturally, this shift in their business model will have auto companies redefining their own strategies to better understand customer demands and capitalize on them.

Some final words

The introduction of AI-based tools in the automotive industry is already underway and making interesting changes. Some of them are pretty visible for consumers (or will be in the short term) like self-driving vehicles and smart driving assistants. Others are transforming the way automakers do their work, like the increasing presence of intelligent cobots and new prototyping platforms. Finally, AI will surely disrupt the industry in the future to the point where auto companies might need to redefine their business models.

All in all, artificial intelligence promises to be as transformative for the auto industry as it will surely be for other sectors. The slow adoption of auto companies shouldn’t be indicative of the general feelings of the field towards AI. In any case, the successful uses that are starting to appear throughout the industry will surely encourage other brands to adopt AI tools to stay in the game.