New technologies often emerge to great fanfare only to sink into obscurity or fall short of early expectations. Within the mobility sector, which is rapidly transforming thanks to the growth of electric vehicles (EVs), autonomous driving, and other innovations, pinpointing the potential winners—or even predicting the pace of change—can be particularly difficult.
While some uncertainty will always persist, it’s possible to better understand a technology’s potential by examining data on patents and research, investment levels, adoption rates, and other metrics. The McKinsey Technology Council follows this approach in an annual report (the latest published in July 2023)1 that examines innovations that have momentum across industries.
We have taken the council’s research a step further by conducting a data-driven analysis of 3,500 industrial companies that primarily specialize in autonomous driving, connectivity, electrification, and shared mobility (ACES).2 Our analysis focuses on ten of the cross-industry trends, ranging from next-generation software development to quantum computing to Web3, that the council highlighted (see sidebar “Ten transformative technologies in mobility”). The goal is to obtain a sector-specific view of innovation by determining how many companies in our sample are already working on products that are related to these trends or enabled by them (for instance, greater automation that requires applied-AI algorithms).3 We have found that about 20 percent of the businesses analyzed fall into this category, with most focusing on innovations related to applied AI.
While the ten tech trends already have momentum, we anticipate even more disruption in the near future because more companies are beginning to focus on these areas. As that number increases, so could the amount of emerging innovative products and services that transform vehicles, provide consumers with new mobility options, and improve revenues. Within mobility companies, these innovations could enhance every step of the value chain, including engineering, life cycle services, logistics, manufacturing and production, marketing and sales, R&D, procurement, and product development. What’s more, companies that serve the automotive market, including those in cloud computing, engineering, and semiconductors, may also find new opportunities as the ten trends accelerate.
The tech trends driving change within the mobility sector
The mobility revolution is gaining speed. Scooters and shared e-bikes are now common in many cities, and a recent survey showed that 46 percent of respondents were open to replacing their private vehicles with other transportation options over the next ten years.4 Cars themselves are also evolving, as EVs become more sophisticated and autonomous-driving capabilities—everything from blind spot sensors to self-driving vehicles—attract major investment.
Tech advances are propelling these changes and easing the transition to more sustainable and inclusive forms of transport. EV adoption is growing partly because of recent improvements in battery range and reliability, for instance. While multiple advances are transforming mobility, we have focused on ten of those cited in the most recent McKinsey Technology Council report5:
- advanced connectivity
- applied AI
- cloud and edge computing
- generative AI
- immersive-reality tech
- industrialization of machine learning
- next-generation software development
- quantum tech
- trust architecture and digital-identity tools
- Web3
Of the 20 percent of companies studied that are already working on products or services related to these trends, many have already secured investment to develop their solutions. These companies are in good financial health, with total funding above $200 billion, and 64 percent are working on applied-AI applications or tech enabled by applied AI, making that tech trend the leader by far (Exhibit 1).
The other tech trends in the top five are advanced connectivity, cloud and edge computing, Web3, and immersive-reality tech. The companies in our sample do not divide their investments equally, however, since some technologies are more relevant to specific ACES trends than others are (Exhibit 2). For instance, Web3 tech is most commonly involved in developing use cases with shared mobility, such as those related to building decentralized platforms. It is used much less frequently at companies that focus on autonomous driving.
Although only about 20 percent of the companies in our analysis are working on products or services related to the ten tech trends, this number could rise significantly in response to growing consumer demand for innovative products, such as enhanced, immersive infotainment systems in vehicles and mobility platforms that allow travelers to use different modes of transportation seamlessly in a single trip. Mobility companies are also under growing pressure to optimize internal operations and could pursue one or more of the new tech advances to increase efficiency and reduce costs.
Innovation by region
We have also conducted a geographic analysis to pinpoint where each tech has the most traction. In our sample, companies based in the United States are most likely to report that they are working on one or more of the top five tech trends (Exhibit 3). With applied AI, for instance, 33 percent of companies working on this tech are based in the United States. Since the enterprise database we analyzed did not contain all Chinese companies, it is possible that our analysis underestimates China’s contribution in some categories. The regions that have the most companies focusing on ACES trends and other digital solutions are likely to play a greater role than others in determining how future mobility evolves.
Applied AI and its transformative impact
After analyzing our results, we examined applied AI in more detail because it is by far the most popular tech of the ten trends we examined, and it is poised to disrupt multiple aspects of the mobility ecosystem. The prominence of applied AI within mobility is unsurprising because it enhances so many processes, enables automation, and addresses long-standing pain points. Consider a few examples that underline its current and growing benefits in mobility:
- Engineering and R&D. Some companies use applied AI to create and control virtual worlds in which they can train the algorithms that enable autonomous driving. Among other benefits, AI algorithms can identify weaknesses inherent in current models. They can create thousands or millions of additional scenarios for use in testing—a number that would not be possible without this tech. Rather than making software updates if an autonomous vehicle (AV) does not pass a virtual test, developers can create another scenario to get more information on the problem, saving both time and money. The algorithms can test AV performance for mundane events, such as whether the vehicle stops for a pedestrian at a crosswalk, and extremely uncommon occurrences, such as a pedestrian accidentally stepping in front of the vehicle.
- Procurement. As climate change accelerates, OEMs are using applied AI to identify environmental, social, and governance risks along the supply chain. For instance, algorithms can analyze news items about suppliers to identify potential problems, such as a history of pollution or recent scandals involving corruption, much more quickly and thoroughly than a human can. Improving sustainability might appeal to car buyers, since a recent consumer survey showed that 70 percent of respondents considered sustainable manufacturing to be an important consideration during vehicle purchase.6 In the future, AI may also help companies forecast risks more accurately and proactively suggest improvements, such as using more sustainable resources.
- Manufacturing. By using vision cameras, lidar, and radar in combination with applied AI, OEMs have improved quality control during manufacturing. For instance, one leading automotive manufacturer is leveraging AI-controlled robots to handle individual vehicle processing while maintaining rigorous quality standards. In the surface inspection phase, an advanced system uses specialized tech that projects black-and-white patterns onto the vehicle’s surface. This technique allows cameras to scan and identify even the most minor variations in reflective paintwork. The tech has been so successful for the manufacturer that lead times have decreased without any change in quality.
- Marketing and sales. Companies can use applied AI to identify customers who are at risk of being lost to a competitor and then create incentives to increase their satisfaction, potentially reducing churn and decreasing costs. Beyond customer retention, companies hope to use the tech to improve customer experience and increase their customers’ loyalty to their specific products and brands.
- Life cycle services. OEMs that incorporate applied AI into vehicles’ onboard systems can analyze consumers’ infotainment preferences and then make personalized recommendations. Additionally, a consumer survey has revealed that about 40 percent of respondents are very interested in personalized, real-time recommendations from navigation systems that are familiar with their driving patterns.7
We expect investment in applied AI to rise because OEMs are increasingly interested in automation—a shift that relies on the AI algorithms that enable automated processes. In a McKinsey survey, respondents expected spending on automation to account for more than 30 percent of their companies’ capital expenditures over the next five years, up from 22 percent for the previous five years.8 About 8 percent of respondents in the automotive sector stated that a five-year investment in automation would total more than $500 million. AI-enabled automation could improve the workplace by bridging increasing labor gaps and taking on some of the least desirable tasks (for instance, by having physical robots complement human labor).
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Beyond automation, companies are increasingly using applied AI to improve other areas of operations. For instance, some OEMs are enhancing R&D by using digital twins—a virtual representation of a product—to improve manufacturing processes.
Enabling the growth of applied AI
While OEMs and other mobility stakeholders are committed to innovation, applied-AI implementation often poses challenges. This problem arises across industries. McKinsey research shows that 90 percent of companies have launched a digital transformation, but the resulting revenue benefits have been about one-third of the expected amount.9
Within mobility, companies could potentially capture much greater revenues from applied AI if they could overcome the implementation hurdles and capitalize on the tech megatrends. Such improvement would require that OEMs and mobility stakeholders fundamentally rewire how they operate by undertaking a broad and integrated set of changes that involve strategy, organization, risk management, talent, tech, data, and the best processes for adopting and scaling new tech:
- Strategy. Achieving successful AI and digital transformations demands a strategic, top-down approach. C-suite leaders must unite around a shared vision and commitments aligned with the transformation’s ambition. Instead of pursuing individual use cases, decision makers should focus on high-value business domains, such as the complete customer journey. Within each domain, they can then identify the best use cases and solutions to pursue. For best results, companies should quantify value through operational KPIs and set clear priorities during implementation.
- Organization. Innovation requires strong project management, but results from a McKinsey survey show that 75 percent of business leaders have not yet adopted best practices.10 (Tech companies are an exception, since they tend to embed project management capabilities into their operating models.) What’s more, many companies struggle with collaboration among their business, operations, and tech functions, which can slow progress for new products and services. To overcome such problems and improve organizational capabilities, companies should consider establishing distributed, empowered product teams led by a product owner. These teams would have access to all essential data and tech, including software development tools. Thus, they would be likely to develop effective, cross-functional solutions that truly consider business interests.
- Risk management. To enhance AI capabilities, companies should reconsider and amplify risk management from project inception rather than wait until the rollout to do so. If agile teams identify risks early, they can quickly develop solutions before problems escalate and threaten development. Proactive risk management is especially vital when working on emerging tech because challenges might unexpectedly materialize. With solid risk management, companies can minimize costs and potentially avoid missteps that could damage their reputations.
- Talent. Organizations must prioritize tech talent by building internal capabilities and implementing targeted, tech-specific hiring plans. As they think about talent priorities, companies should focus on essential skills, rather than roles, and then identify capability gaps. Some companies might fill these gaps by training current employees, while others might look externally for the right talent. To enhance retention, companies should develop a compelling employee experience that covers everything from available incentives to career paths.
- Tech. Organizational changes can enhance digital innovation. Relying on multiple, distributed teams rather than a central function will expedite progress and improve the quality of any solution. A move to distributed teams will require increased reliance on automation for all steps, including quality checks and testing. It will also require the creation of self-service environments (for instance, portals that allow developers to access a company’s approved applications, collaboration tools, and data).
- Data. At most companies, digital teams spend a lot of time compiling and harmonizing data. To enhance the customer experience and reduce unit costs, all digital teams must have access to data in near real time. To make that feasible, companies should create data products, which are ready-to-use data sets easily accessible by employees. Their IT architecture should easily deliver these data from where they are stored to all relevant teams. For data oversight, companies will benefit from a federated governance model in which a data management office creates policies and provides general support while business units and functions manage some routine tasks, such as the creation of data products. Close collaboration among the chief information officer and chief data officer is also essential on data-related initiatives.
- Adoption and scalability. All too often, companies focus on developing solutions and give less attention to ensuring their widespread adoption. To avoid this trap, businesses should create new engagement models, incentives, and performance metrics to encourage the steady uptake and use of applied AI. Scaling innovation is another frequent pain point because expectations may vary by end user, market, and plant location. A few changes can help avoid duplication of effort and rework, however. Companies that focus on “assetization”—creating solutions that can be reused in any group without changes—may have an advantage when scaling. For instance, they could write code in blocks that are easily reused and create data products suitable for all locations.
As more mobility companies begin to pursue innovations related to the ten discussed tech trends, disruption will be inevitable and the stakes will become higher. Every business within the industry, including traditional OEMs, must understand which innovations have momentum and which leading-edge technologies are best for their vehicles and other products. Even companies in other industries, such as semiconductors, should take heed of tech trends within mobility because they could affect revenues from the automotive sector, which often includes some of their most important customers. Those companies that become aware of the most important trends now may gain an early advantage as the market evolves.
