Beyond Driverless Cars: How AI is Used in the Automotive Industry

Beyond Driverless Cars: How AI is Used in the Automotive Industry

  • Artificial intelligence goes way beyond autonomous vehicles
  • It could save the automotive industry $44 billion by 2025
  • AI could help manage supply chain disruption
  • The benefits of AI touch every part of the process from manufacturing to aftersales

Many people might think of artificial intelligence (AI) as the backbone of autonomous vehicle technology — and they’d be right. But AI for automotive has so many more applications beyond powering driverless cars. So many, in fact, that it’s transforming the industry in a variety of ways from improving the efficiency of manufacturing to supporting the sales process. 


AI can improve efficiency and quality control in the manufacturing process. For example, AI-powered robots can identify and repair defects in parts, and computer vision systems can be used to monitor the production line for potential problems.

Many manufacturers recognize the value of this technology as it can identify defects up to 90% more accurately than a human (at least, according to Porsche), significantly reducing issues and recalls down the line.

AI is also a powerful planning tool and many manufacturers use digital twins to simulate a part, factory, or vehicle before working on the real thing. For example, BMW uses a digital factory twin to help predict worker movements. This helps to avoid any ergonomic issues as the cars are built. In doing this, BMW has been able to reduce the time spent planning factory operations by 25%.


Sales and Marketing

AI is being used to improve customer targeting and personalization. In fact, the use of AI in this area has such a knock-on effect on the industry that those benefits are estimated to reach $44 billion by 2025, according to McKinsey — that’s around 2% of the total operating margin.

Perhaps more importantly, better market intelligence from AI can help predict inventory needs, reducing the time a vehicle sits unsold at a dealership by as much as 30%, according to a report from BCG. This helps businesses improve their margins while still serving customers to the level expected of them.

AI is also used to better personalize marketing messages to customers. According to McKinsey, brands that choose to personalize the user experience generate 40% more revenue than the ones that don’t. This makes sense when you consider a car advert aimed at a 40-year-old city worker is unlikely to resonate with a 20-something woman living in a rural area.


The global automotive aftermarket industry is steadily rising in value and was valued at $721.2 billion in 2021, but the entire supply chain is prone to error. With millions of parts being sold to thousands of customers, manual processes can lead to lost revenue and angry customers.

While technology can’t improve physical supply, it can alleviate the pain of these issues. In fact, a 3Gem report showed that 53% of the supply chain decision-makers surveyed believed AI advances are key to managing disruption. 

Nitin Dsouza, head of global supply chain at Publicis Sapient told IT Pro: “AI has built various scenario models and calculated the right price to buy at, when a premium is appropriate, how high it would be, and so on. It has also used shipping and transport data to predict delays in delivery.” This helps improve the buying cycle and ensures delays are minimized for customers waiting on aftermarket parts.


Safety is also another area that will continue to improve with AI and machine learning. In the same way autonomous vehicles use data analytics and machine learning to read the road and keep passengers safe, AI can be used within a traditional vehicle to improve safety. Many advanced driver assist systems (ADAS) use the same sensor and camera technology found in fully autonomous vehicles.

In many vehicles, AI and machine learning power these safety features:

  • Automatic braking
  • Collision avoidance systems
  • Pedestrian and cyclist alerts
  • Cross-traffic alerts
  • Intelligent cruise control

Other applications of AI in automotive

As well as self-driving cars, AI could improve the wider automotive experience. Here are some examples:

  • Intelligent heads-up displays that adapt to the environment
  • Transport planning to combat congestion
  • Personalization for customers within a vehicle
  • Smart city technology allows the environment to communicate with vehicles

All these uses of machine learning and artificial intelligence can help to make the driving experience smoother and more enjoyable for drivers and passengers alike.

Artificial intelligence is transforming the automotive industry in ways beyond powering autonomous vehicles. It’s used to improve manufacturing efficiency, quality control, and planning, which reduces errors and improves overall productivity but it extends beyond this, too, into sales, supply chain, and city planning. 

Overall, the impact of AI in the automotive industry has been significant, and it is expected to continue to drive innovation and improve the industry’s efficiency, safety, and customer satisfaction.


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