Artificial intelligence (AI) holds the key to a new future of value for the automotive industry. While popular attention is focused on the use of AI in autonomous cars, the industry is also working on AI applications that extend far beyond – engineering, production, supply chain, customer experience, and mobility services among others.
“Not only are AI technologies critical for enabling our autonomous
vehicles, but they are playing an increasing role in transforming our customer and employee experiences”, says Jeff Lemmer, vice president and CIO, Ford Motor Company. “Supply chain risk identification, and in-vehicle predictive maintenance, are just a few of the ways Ford is already applying AI to improve our customer and business operations.” Atif Rafiq, global chief information officer and chief digital officer, Volvo Car Group, echoes this sentiment, saying: “Car companies are actively using AI in their autonomous driving efforts and this typically gets the most headlines. But every facet of this industry can benefit, including how cars are made and sold and to invent new customer experiences.” 1
There are many examples of AI’s reach into the industry:
• General Motors’ "Dreamcatcher" system uses machine learning to transform prototyping. The solution was recently tested with the prototyping of a seatbelt bracket part, which resulted in a single-piece design that is 40% lighter and 20% stronger than the original eight-component design.2
• Continental, one of the largest automotive parts suppliers, has developed an AI-based virtual simulation program. The program can generate 5,000 miles of vehicle test data per hour, when it currently takes over 20 days from physical efforts.3
• Volkswagen built its own speech technology team at its DATA:LAB in Munich in 2017 to take over standardized communication with suppliers. The goal of the project was to support procurement processes for commodities and merchandise under $10,000. 4
• Škoda is testing the use of autonomous drones for stocktaking at its factory in Mladá Boleslav, Czechia.
The technology detects, identifies and counts empty containers outside the factory from above, three times a day, and transmits the data it collects to Škoda’s logistics department for processing.5
Given the impact that AI is having, we have undertaken significant research into AI in the automotive industry. Building on a cross-sector study we conducted in 2017, we have recently surveyed 500 automotive executives across eight countries as well as conducted in-depth interviews with industry experts and entrepreneurs (see research methodology at the end of this report). This report focuses on incumbent players in the automotive industry and not on the new entrants such as Google.
Our research finds that the industry has made modest progress in AI-driven transformation since 2017. Many organizations have yet to scale their AI applications beyond pilots and proofs-of-concept. Yet, there is a group of companies that are making significant progress in driving use cases at scale. Characteristics of this group of companies offer an insight into best practice in scaling AI.
In this report we explore:
• Where the industry stands in scaling its AI implementations
• What concrete benefits can result from scaled initiatives
• Where automotive manufacturers should focus their AI investments
• Success factors and recommendations for scaling AI.