The road to artificial intelligence in mobility—smart moves required

Artificial intelligence (AI) is the word on everyone’s lips. But in the automotive industry today, many products and services being labeled as such are in fact reliant on a form of advanced analytics (evolving from conventional algorithms) that enables those features—for example, predictive maintenance in manufacturing.

Theories of AI have existed since 1950. However, AI itself gained wider functional applicability only in the past few decades, with the rise of machine learning and deep learning. This has also been facilitated by advances such as improved algorithms and training methods, greater computing power, and the availability of large amounts of data in the cloud.

Despite these developments, the automotive industry is still only at the beginning of the AI disruption. State-of-the-art AI applications remain narrow—they can perform better than humans, but only in very specific tasks. And the level and nature of AI technology varies widely; for example, “narrow AI” encompasses classic navigation systems as well as autonomous-driving tasks processing one gigabyte of data per second, or a million times more data than current navigation systems handle. Matching human ability in an even larger number of contexts is still some years out.

For all the talk of what AI may be able to achieve, the question remains: Is it all hype, or an important technology that companies must master? A next-level improvement from AI could drive huge competitive advantage, and this is particularly true for the automotive and mobility industry. To understand the landscape, McKinsey surveyed 3,000 consumers in China, Germany, and the United States; interviewed industry leaders, including automotive incumbents, tech players, and academics; and analyzed start-ups, investments, and patents. This allowed us to develop a view on what seems likely from a market—not a theoretical—perspective.

In this article, we focus on AI systems that use machine-learning and deep-learning techniques to enhance or create new applications in the automotive industry. Players must contend with several questions on the technology and business of machine learning in automotive and mobility:

  • How important is machine learning for the industry?
  • Are consumers receptive to using AI in mobility, and what are the core applications of machine-learning technology in the space?
  • What challenges must be tackled to monetize the technology?
  • Which strategic actions might automotive and mobility players take to prepare?

Beyond the hype, machine learning could be a source of competitive advantage

Machine learning makes AI possible. Applying a practical working definition, the technology is able to deliver in three key areas in automotive and mobility:

  • act in highly complex situations (as measured by the amount of data needed to describe it)
  • cope with a high number of possible situations that cannot be covered adequately by explicit programming
  • improve over time without explicit instructions, learning from data of previously unknown situations in an unstructured way

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