The Capgemini Research Institute has found that just 10% of major automotive companies are implementing artificial intelligence (AI) projects at scale, despite evidence that the technology could add millions to their profits.

A study carried out by the research body found that AI could increase the operating profit of businesses in the sector by up to 16% but revealed that fewer automotive companies are implementing AI than was the case in 2017. 

The “Accelerating Automotive’s AI Transformation: How driving AI enterprise-wide can turbo-charge organizational value” study surveyed 500 executives from large automotive companies in eight countries, building on a comparable study from 2017, to establish recent trends in AI investment and deployment.

A statement issued alongside publication of the study said: “Scaling of AI has seen a slow growth. Since 2017, the number of automotive companies that have successfully scaled AI implementation has increased only marginally (from 7% to 10%). 

“However, more significant was the increase in companies not using AI at all (from 26% to 39%).”

According to the report, just 26% of companies are now piloting AI projects (down from 41% in 2017).

This is maybe due to companies finding it harder to realize a desired return on investment, the statement said.

The results also reveal a significant regional disparity, with 25% of US firms delivering AI at scale, compared to 9% in China (note, this is a significant increase from 5% to 9%), 8% in France, 5% in Italy and 2% in India.

The Capgemini Research Institute’s study highlighted the following potential reasons for the modest progress in relation to AI implementation:

  • The roadblocks to technology transformation are still significant, such as legacy IT systems, accuracy and data concerns, and lack of skills.
  • The hype and high expectations that initially came with AI may have turned into a more measured and pragmatic view as companies are confronted with the reality of implementation.

Automotive organizations can drive significant reward from scaled AI.

The modest progress in implementing AI projects at scale represents a major missed opportunity for the industry.

Modelling in the report, based on one typical Top 50 Original Equipment Manufacturer (OEM), estimates that delivering AI at scale could achieve increases in operating profit ranging from 5% (or $232m) based on conservative estimates, to 16% (or $764m) in an optimistic scenario.

But AI will also drive efficiencies in the car retail sector, with benefits to be realised in lead generation, customer enquiries, aftersales and stocking processes.

Capgemini noted that Volkswagen is already accurately modelling vehicle sales across 250 auto models in 120 countries using machine learning.

In manufacturing, tyre manufacturer Continental is generating 5,000 miles of vehicle test data an hour through an AI-powered simulation, compared to 6,500 miles a month it was getting through physical test driving.

“With AI-empowered visual inspection we have sensibly reduced the ratio of false positives with respect to the previous systems,” said Demetrio Aiello, head of the AI & Robotics Labs at Continental. “I am very confident that if we can deploy AI to its fullest potential it would have an impact on performance equivalent to almost doubling our capacity today.”

Markus Winkler, Executive Vice President, Global Head of Automotive at Capgemini, said: “These findings show that the progress of AI in the automotive industry has hit a speedbump.

“Some companies are enjoying considerable success, but others have struggled to focus on the most effective use cases, vehicle manufacturers need to start seeing AI not as a standalone opportunity, but as a strategic capability required to shape the future which they must organize investment, talent and governance around.”

He added: “As this research shows, AI can deliver a significant dividend for every automotive business, but only if it is implemented at scale.

“For AI to succeed, organizations will need to invest in the right skills, achieve the requisite quality of data, and have a management structure that provides both direction and executive support.”