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Artificial Intelligence (AI) in Automotive – Thematic Research

The automotive industry is fiercely competitive and currently defined by high volumes and low margins. AI’s potential can protect margins and brand offering in the face of the long-term existential threats of sustainability, overcapacity, and the prospect of decreasing volume due to the challenge of shared mobility.

AI enables automakers and suppliers to differentiate their offering, as well as helping them operate in an agile relationship with real-world events. This is necessary to mitigate crises like COVID-19 and the automotive chip shortage, in addition to the longer-term challenges of shared mobility disruption and the pivot to electrification.

This thematic research report takes an in-depth look at current and potential uses of artificial intelligence (AI) technologies across the entire automotive value chain, including in autonomous vehicles (AVs), supply and manufacturing, sales, and the aftermarket. It examines the challenges faced by the automotive industry and how AI can mitigate these supported by primary research case studies and growth forecasts for AVs. The report highlights top mergers and acquisitions related to the theme as well as the other AI activities of major automakers, suppliers, vendors, and specialist vendors. Finally, GlobalData’s Parts and Tires and Vehicle Manufacturing scorecards identifies leading and lagging companies based on the key themes impacting the automotive sector.

Who should buy?

Executives in the automotive and adjacent industries who want to understand the themes disrupting their competitive landscape and how AI affects these.

Specialist vendors who want to identify market opportunities and competitor innovation.

Our unique differentiator, compared to all our rival thematic research houses, is that our thematic engine has a proven track record of predicting winners and losers.


The six main industry challenges that traditional automakers and suppliers face, along with thorough discussion of how AI can be used to mitigate them.

Primary research case studies on the use of AI in battery management and demand planning.

AV growth forecasts to 2035.

Details of key automotive mergers and acquisitions (M&A) which have an AI thesis, including year of deal and deal value.

Detailed profiles of leading AI adopters, leading vendors, and specialist vendors within the automotive sector.

GlobalData’s Parts and Tires and Vehicle Manufacturing scorecards, which rank the leadership of companies in the key themes disrupting their industry. This is informed by GlobalData’s comprehensive tracking of AI related deals, job openings, patents ownership, company news, financial and marketing statements.

Key Highlights

Automakers face several threats to their largely low-margin models, including sustainability, overcapacity, shared mobility, and the prospect of declining market volume.

Auto suppliers and makers amass a lot of data which they do not use effectively. Data volume will only continue to grow as autonomous, software-defined, and connected vehicle functions increase in number and scope. The fundamental AI technologies of data science and machine learning (ML) are designed to quickly assimilate large volumes of data, understand what it means, and promptly apply the insights that emerge. Automakers must develop their own capabilities in data and AI to avoid outsourcing value-add opportunities to large technology providers.

AV developments are underpinned by AI chips, computer vision, and ML. AV volume will increase with a CAGR of 35% between 2025 and 2035. However, AI is important across the whole automotive value chain. Suppliers and automakers benefit from computer vision and smart robots alongside data science and ML to streamline production, while sales and the aftermarket profit from conversational platforms and context-aware systems alongside data science and ML.

AI plays a crucial role in closing the feedback loop between supply and demand by incorporating sale and post-sale vehicle data into predictive modelling. Leading automakers – especially those operating on a high margin basis – are beginning to deploy these kinds of techniques, as our case study research shows.

Reasons to Buy

Prioritise investments in the areas of AI which will deliver the best results using recommendations on the areas of the value chain you should focus on and the parts you can confidently ignore.

Develop value propositions for your AI services that will resonate with prospective clients in the automotive industry by uncovering the business challenges they face.

Formulate AI investment plans and shortlist vendors by understanding priority uses for AI, lists of relevant specialist suppliers, and valuable lessons from competitors who have already made investments in AI.

Identify the emerging trends in AI specific to the industry and how these developments might advance in the future.

Understand what kinds of AI-related innovation major players, leading vendors, and specialist vendors are engaged in.

Get ahead on AI adoption – the cost-cutting of moonshot projects brought about by COVID-19 means that some existential threats (like autonomy and shared mobility) have temporarily abated, hence AI adoption is more important than ever for automakers to avoid falling further behind the big technology players.

Companies Mentioned

AEye, Argo AI, Aurora Innovation, BMW, Cerence, Cognata, Continental, Cruise, Daimler, Denso, Ford, GM, Honda, Horizon Robotics, Hyundai, Hyundai Mobis, Magna International, Porsche, Robert Bosch, Scale AI, Seeing Machines, Tesla, Toyota, UVeye, VW

Table of Contents


Value chain

Automotive challenges

The impact of AI on automotive

Case studies

Market size and growth forecasts

AI timeline


Sector scorecards


Further reading

Thematic methodology


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