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For more than two years now, the automotive industry has been talking about four disruptive and mutually reinforcing major trends—autonomous driving, connectivity, electrification, and shared mobility. These trends are expected to fuel growth within the market for mobility, change the rules of the mobility sector, and lead to a shift from traditional to disruptive technologies and innovative business models.

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Artificial intelligence (AI) is a key technology for all four of the trends. Autonomous driving, for example, relies 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, for example, artificial intelligence can help to optimize pricing by predicting and matching demand and supply. It can also be used to improve maintenance scheduling and fleet management. Improvements realized through AI will play an important role for automotive companies, enabling them to finance innovation and cope with the trends ahead of them.

One expected result of the four major trends is a marked shift in the industry’s value pools. This change will have an especially large impact on big 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 trends will not only affect the business of all incumbent and traditional industry players but also open up the market to new entrants. Many companies such as technology players, which previously focused on other industries, are heavily investing in the mobility trends and the underlying 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 the technology expertise of new players to unlock the potential value of artificial intelligence, new players will have opportunities to claim their share of automotive and mobility markets. To master the four trends, OEMs need to invest substantially in each of the trends and in successfully integrating them.

Sidebar How we derived insights: Sources and methodology Our main sources include the following: The McKinsey “Auto 2030” market model, which is based on scenario-tested development of the four disruptive trends

More than 100 discussions with artificial-intelligence (AI) experts, mobility executives, and functional experts in areas including manufacturing, supply-chain management, sales and marketing, and IT

Relevant market reports on digital disruptions, AI, and automation as well as annual reports from all major automotive OEMs

More than 15 analyses on specific industry perspectives, for example, how OEMs are investing their resources and what margins can be achieved How we derived the value potential We developed a use-case landscape along the entire value chain and quantified all major use cases by identifying the status quo for a typical OEM as well as the target state for full AI application. Estimating differences in costs or revenues and margins then yielded the value potential (further details on the methodology used are provided in the report’s appendix). The following is an example for the important overarching manufacturing use case, “in-line quality control,” which is relevant for stamping, body shop, paint shop, powertrain, and final assembly (with some variations between these manufacturing steps). Status quo at a typical OEM: Quality control is partly carried out by machines, partly manually

Manual quality control often has a low detection rate for smaller issues

If a quality issue is detected, a manual intervention is required

Limited learning can be taken from issues detected as there are multiple interdependent parameters and operators typically do not know how or which parameters to optimize (making changes is too risky) Target state required for fully capturing the value opportunity: Continuous in-line quality control for automatic detection and high-accuracy detection of quality issues, for example, by recording video, sound, and process parameters

Improved manufacturing processes based on the incorporation of feedback from the detection of issues affecting quality, and therefore a large reduction in quality issues Why AI is required: AI is required for analyzing previously unavailable or indecipherable data (for instance, video or sound that previously could only be interpreted by humans) in order to detect quality issues. AI also has the ability to help detection and analysis mechanisms, improve its own accuracy by continuously learning from the issues detected, and optimize manufacturing processes by incorporating feedback and adjusting the control parameters accordingly. Impact from an example quality-control use case: Reduction of personnel costs, rework, and scrap yielding a total cost reduction of approximately 9 percent in the corresponding parts of the value chain, which corresponds to approximately $29 billion

Some of our earlier work has focused on artificial intelligence in mobility and in the industrial sector. The report on which this article is based continues that effort, drawing on insights from a multipronged methodological approach (see sidebar, “How we derived insights: Sources and methodology”). First, it maps artificial intelligence–enabled value opportunities for automotive OEMs along the three application areas of process, driver or vehicle features, and mobility services. Next, it breaks down and quantifies these opportunities. Finally, the report outlines the strategic actions that OEMs should take to fully capture the AI-enabled value opportunities in both the short and long run.

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Our analyses yielded the following key insights, which the report discusses in more detail:

In the short to medium term, there is a substantial, industry-wide, artificial intelligence–enabled opportunity that by 2025 could reach an annual value of about $215 billion for automotive OEMs worldwide (exhibit). This corresponds to nine percentage points of earnings before interest and taxes for the whole automotive industry, or a theoretical average annual productivity increase of approximately 1.3 percent over seven years —a significant value to boost the industry’s regular productivity aspiration of about 2 percent annually. Most of this value is derived from the optimization of core processes along the value chain.

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Even in the short term, artificial intelligence can lead to efficiencies and cost savings across the entire value chain. It can also create additional revenues from vehicle sales and aftermarket sales. Most of the value is generated in four core processes. In procurement, supply-chain management, and manufacturing, efficiencies lead to cost savings of $51 billion, $22 billion, and $61 billion, respectively. In marketing and sales, AI-based efficiencies both reduce cost and generate revenue, leading to a total value potential of $31 billion.

While AI-enabled driver or vehicle features and mobility services can generate substantial industry-wide value in the long term, these create limited value at the industry level in the short term. However, individual OEMs that outperform competitors with their driver or vehicle features and mobility services can gain substantial market share. These gains in market share by technology leaders are, nevertheless, small compared with the risk of losing a significant part of the customer base for OEMs that are falling behind on these features.

The road to artificial intelligence in mobility—smart moves required

Four success factors enable OEMs to prepare for the AI transformation and to capture value from artificial intelligence in the short term: collecting and harmonizing data from different sources, setting up a partner ecosystem, establishing an AI operating system, and building up core AI capabilities and an AI team to drive the required transformation.

OEMs need to start their transformation now by implementing pilots to gain knowledge and capture short-term value. Then, they should establish the 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 transformation to systematically capture the full value potential from AI and build up capabilities for their long-term strategies in confronting the four disruptive trends.

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