In recent years, the automotive industry has been intensely discussing four disruptive and mutually reinforcing trends: autonomous driving, connectivity, electrification and shared mobility. These ‘ACES’ trends are expected to fuel growth within the market for mobility, change the rules of the sector, and lead to a shift from traditional to disruptive technologies and innovative business models.
Artificial intelligence is a key technology for all four ACES trends. Autonomous driving, for example, relies inherently on AI because it is the only technology that enables the reliable, real-time recognition of objects around the vehicle. For the other three trends, AI creates numerous opportunities to reduce costs, improve operations and generate new revenue streams. For shared mobility services, AI can, for example, help to optimize pricing by predicting and matching supply and demand. It can also be used to improve maintenance scheduling and fleet management.
These improvements through AI will play an important role for automotive firms because they enable them to finance and cope with the changes ahead of them. One expected key result from the ACES trends is a marked shift in the industry’s ‘value pools’. This change will primarily affect large automotive original equipment manufacturers (OEMs) and their business models, but the impact will be felt throughout the industry and beyond.
The products and services made possible by the ACES trends will not only impact the business of all incumbent and traditional industry players, but will also open the market up to new entrants. Many companies that were previously focused on other industries — e.g. technology players— are heavily investing in the ACES trends and the underlying key technologies. As a result, a new ecosystem of players is emerging.
New players will be important partners for traditional automotive companies. While automotive OEMs can use new players’ technology expertise to unlock value potential from AI, new players will have opportunities to claim their share of the automotive and mobility markets. To master the ACES trends, OEMs need to invest substantially into each of the four ACES — not just in their development, but also in their integration.
Our analyses has yielded the following key insights:
• In the short to medium term, there is a substantial industry-wide AI-enabled value opportunity, which by 2025, will reach a total accumulated value potential of around US$ 215 billion for automotive OEMs worldwide. This corresponds to the value of nine EBIT percentage points for the whole automotive industry, or to an additional average productivity increase of approximately 1.3 per cent per year—a significant value to boost the industry’s regular ~2 per cent annual productivity aspiration. Most of this value is derived from the optimization of core processes along the value chain.
• Even in the short term, AI can lead to efficiencies and cost savings across the entire value chain and can create additional revenues from vehicle sales and after-market sales. Most of the value is generated through four core processes. In procurement, supply chain management and manufacturing, efficiencies lead to cost savings of US$ 51 billion, US$ 22 billion, and US$ 61 billion, respectively. In marketing and sales, AI-based efficiencies both reduce cost and generate revenue, leading to a total value potential of US$ 31 billion for this process.
• While AI-enabled vehicle features can generate substantial industry-wide value in the long term, these features and services will only create limited value at the industry level in the short term. Nevertheless, generating value from these features and services is important as individual OEMs that outperform competitors with their driver/vehicle features and mobility services can gain substantial market share. These gains in market share by technology leaders are, however, small compared to the risk of losing a significant part of the customer base for OEMs that are falling behind on these features.
Four key success factors will enable OEMs to prepare for the AI transformation and to capture value from AI in the short term: Collecting and synchronizing data from different sources; setting up a partner ecosystem; establishing an AI operating system; and building up core AI capabilities and a core AI team to drive the required transformation.
OEMs need to begin their AI transformations now by implementing pilots to gain knowledge and capture short-term value. They should then establish their AI core to develop an integrated view on AI across the organization. This will enable OEMs to scale up and roll out an end-to-end AI transformation to systematically capture the full value potential from AI and build up capabilities for their long-term ACES strategies. 
Jan Brockhaus is a Senior Associate in McKinsey & Co.’s Cologne office. Johannes Deichmann is an Associate Partner in McKinsey’s Stuttgart office. Jeldrik Pulm is a Fellow in the Cologne office. Jasmin Repenning is an Engagement Manager in McKinsey’s Hamburg office. For more, the full report from McKinsey’s Center for Future Mobility, “Artificial Intelligence: Automotive’s New Value-Creating Engine,” is available online.
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This article appeared in the Winter 2019 issue. Published by the University of Toronto’s Rotman School of Management, Rotman Management explores themes of interest to leaders, innovators and entrepreneurs.
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