Technology is transforming mobility, and more companies are beginning to investigate disruptive technology. This is our analysis on the trends to watch.
New technologies often come into the system with a big bang laden with unrealistic promises, only to under-deliver. Predicting the pace of change in the mobility sectors, which is rapidly transforming because of the rise of EV and other innovations, is particularly difficult.
Although uncertainty remains, we can examine patents, research, investments, and adoption rates to help understand a technology’s potential. We analyzed thousands of companies in the mobility sector and found that 20% are applying at least 1 of 10 transformative technologies. As more companies focus on these areas, we expect new innovative products in this sector, enhancing the value chain and ancillary segments like cloud computing and semiconductors.
Vehicles are becoming more sophisticated, sustainable and inclusive. Improvements in battery range and reliability is pushing EV adoption. Here are the 10 technologies driving the future of mobility:
- applied AI
- advanced connectivity
- 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
The companies that are applying these technologies have secured more than $200 billion of investment, and a significant majority of them are utilizing applied-AI, making it the leading tech trend.
In the future, AI can help companies forecast risks more accurately and suggest improvements.
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 has reduced lead time without affecting quality, by leveraging AI-controlled robots to handle individual vehicle processing. The robots project black-and-white patterns onto the vehicle’s surface and allow cameras to identify even the most minor variations in reflective paintwork.
Marketing and sales. Companies can use applied AI to improve CRM, identifying customers who have high risk of churn, then create incentives to increase their satisfaction.
The number of companies working on the 10 tech trends is rising significantly due to the growing demand for products such as enhanced vehicle infotainment systems or platforms that allow travelers to use different modes of transportation seamlessly in a single trip. Existing mobility companies are also under growing pressure to pursue new tech advances to increase efficiency.
Applied AI and its transformative impact
Applied AI is the most popular tech of the 10 trends, and it is poised to disrupt multiple aspects of the mobility ecosystem, enhancing many processes, enabling automation, and addressing long-standing pain points.
Applied AI’s current and growing benefits in mobility:
Engineering and R&D. Some companies use applied AI to create virtual worlds in which they can train the AI for self-driving. It can identify weaknesses in current models and create thousands or millions of additional scenarios for use in testing. Example, if a self-driving car does not pass a virtual test, developers can create more scenarios to test on rather than making software updates, saving both time and money. The algorithms can test for events such as object detection and pedestrian detection.
Procurement. OEMs use AI to identify ESG risks along the supply chain. AI can analyze news about suppliers to identify potential problems, such as pollution or corruption scandals, quicker than a human can. Sustainability appeals to car buyers, since a recent survey showed that 70% of respondents considered sustainable manufacturing to be a key consideration during purchase.
Life cycle services. OEMs that incorporate applied AI into vehicles’ onboard systems can analyze consumers’ infotainment preferences and then make personalized recommendations. A consumer survey has shown that 40% of respondents are very interested in personalized, real-time recommendations from navigation systems.
OEMs are increasingly interested in automation. In a survey, respondents expected spending on automation to account for more than 30% of their capital expenditures over the next 5 years. Furthermore, AI-enabled automation could improve the workplace by bridging increasing labor gaps and taking on the least desirable tasks. Beyond automation, some OEMs are enhancing R&D by creating a digital twin of a product to improve manufacturing processes.
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:
Strategy. Achieving AI and digital transformation requires a top-down approach with unified C-suite leadership, focusing on high-value business domains rather than individual use cases, and prioritizing value with operational KPIs.
Organization. Strong project management is essential, but often lacking outside tech companies, and promoting collaboration among business, operations, and tech functions is crucial. Establishing empowered product teams with access to necessary data and tools can drive innovation.
Risk Management. Risk management should be integrated from the start of projects, with agile teams identifying and addressing risks early to minimize costs and prevent reputational damage.
Talent. Prioritizing tech talent through internal capability building and targeted hiring, focusing on essential skills, identifying gaps, and enhancing retention with compelling employee experiences is vital.
Tech. Using multiple, distributed teams can expedite digital innovation, increasing reliance on automation and creating self-service environments.
Data. Ensuring digital teams have near real-time access to data and creating easily accessible data products is important, supported by a federated governance model for data oversight.
Adoption and Scalability. Encouraging widespread adoption of AI solutions with new engagement models and incentives, and focusing on creating reusable solutions to avoid duplication of effort, are key to scaling innovation effectively.
