Artificial Intelligence In Automotive Industry: Surprisingly Slow Uptake And Missed Opportunities

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The automotive industry is one of the most high-tech industries in the world – so a headline finding in a report published this week was, on the face of it, somewhat surprising. Capgemini's report – Accelerating Automotive's AI Transformation – found that during 2018, the number of companies in the industry deploying AI "at scale" grew only marginally by 3%. This reflected that just 10% of respondents surveyed said that their organizations were deploying AI-driven initiatives across the entirety of its operations "with full scope and scale," during 2018, compared to 7% in 2017. The relatively slow pace of growth is evidence that "the industry has not made significant progress in AI-driven transformation since 2017", the report concludes – a surprising finding given the scale of investment and enthusiasm shown by industry leaders. I spoke to one of the report's authors, Capgemini's Ingo Finck, who told me "To an extent, I did find this surprising, because from the discussions we've been having with these companies we see that the vast majority – more than 80% - mention AI in their core strategy. "It's clearly a strategic factor for them, so yes … we were surprised by the relatively slow growth rate." Before we start delving into the possible reasons for this slow uptake, it's worth noting that there is a key geographic variation: In China, the number of automotive companies working at scale with AI almost doubled, from 5% to 9%. This is explained to some extent by the comparatively "open" approach taken by China's AI giants, such as Baidu's development of the open source Apollo platform. This has involved it partnering with over 130 other businesses and organizations. Finck explains that the slow growth demonstrated in other regions could be down to the fact that organizations are taking a more mature approach to AI deployment. This might mean they are moving away from "try everything and see what works" methodologies, towards focusing on proven use cases that can then be scaled. Another disparity is apparent when we consider the sizes of the businesses that are reporting growth in AI deployments. "We can see that the smaller companies are struggling more with AI – whereas with larger companies [with revenue of $10 billion plus] the adoption rate is higher.