Long before the Covid pandemic gave the automotive industry a significant digitalisation boost, they were considered the most important bridge builders into the digital age: the Chief Digital Officers. The most prominent representative at the time was Johann Jungwirth, who was supposed to pull everything to "digital" in the Volkswagen Group, but quickly encountered many internal resistances and after just under three years in office as Chief Digital Officer, fled back to the tech world. Other CDOs in the industry had similar experiences. Today, hardly anyone talks about them anymore, the task of digital transformation increasingly (again) lies with the CIOs or - when we talk about the product car - with the CTOs or software chiefs.
This does not stop companies in the industry from continuing to bundle responsibilities in newly created positions and to raise new "Chiefs". Particularly in the spotlight are now C-level managers who are supposed to drive the key technology of digital change across the company: artificial intelligence.
"The design possibilities that arise for automotive companies through AI are so far-reaching that they require a central bundling of responsibility at C-level," emphasises Malte Broxtermann, partner at Berylls by Alix Partners. The so-called Chief AI Officer (CAIO) can help, given the high expectations of top management, to enable the entire organisation in terms of AI, make successes visible and remove difficulties, according to the expert.
"For me, it's clear," says Broxtermann, "creating an AI-friendly culture and setting an AI strategy that suits the company is hardly feasible without corresponding C-level backing. The CAIO function ensures that there is a central 'pacemaker'."
At Mercedes, AI and data come from a single source
And exactly such "AI Pacemakers" are now increasingly found at more and more major car manufacturers. In early March, General Motors appointed Barak Turovsky as its first Chief AI Officer. The 49-year-old, who previously oversaw artificial intelligence at Cisco and Google, is tasked with optimising products, processes, and customer experience across the company using AI at the traditional manufacturer.
Shortly thereafter, it became known that Mercedes-Benz also wants to consolidate the topic of AI in a new C-level function. Daniel Eitler, who has been responsible for cybersecurity at the Swabians as global CISO since 2021, is now also to drive forward the topics of AI and data in a leading role. At Mercedes, the newly created organisation is now called Global Cyber Security, Data and AI, bringing together the most important and at the same time most challenging digital topics in a function critical to success.
Eitler was promoted to the leadership team by Mercedes CIO Katrin Lehmann, who has already stirred up quite a bit of dust in her first year as the new IT chief at the premium OEM from the region. AI and data are fundamental to her IT strategy and must be thought of even more "hand in hand" in the future. This puts the young IT decision-maker fully in line with the company. Mercedes-Benz expects enormous efficiency gains in purchasing, production, (software) development, and customer interaction through the use of smart and generative algorithms.
"Just like the CDO, the CAIO now faces a similarly large transformation task," explains Berylls expert Malte Broxtermann. "If you imagine the diverse application scenarios for AI and are willing to rethink entire value creation processes instead of automating individual steps, this can and will be a major change for many automotive companies that needs to be accompanied and orchestrated."
This is what Daniel Eitler plans at Mercedes
The expectations of the CAIO could hardly be greater. Daniel Eitler explains to automotiveIT how he intends to meet this mammoth task. "My mission is to fully exploit AI as a transformation turbo at Mercedes-Benz." His approach is based on three core principles that don't seem so technical at first glance.
Firstly, under the motto "Leadership through Knowledge," Eitler wants to enable management to understand the potential of AI, "so that it can pass on these insights and promote a culture of innovation." Secondly, employees at Mercedes should have comprehensive access to AI technologies and be motivated to use them "creatively and productively." And thirdly, according to Eitler, AI should of course make a tangible contribution to value: "We focus on use cases that deliver the greatest added value for our customers and increase productivity along the entire value chain. This is how we maximise the benefit of our investments."
Eitler sees himself as a kind of orchestrator, holding all the strings in terms of data and AI and thereby increasing the effectiveness of the technologies across the group: "My task is to optimally set up the control centre. Through central tools, methods, processes, and training that make the implementation of AI easy, fast, secure, and within the framework of our internal AI principles." This "AI Code" at the Swabians is based on the four pillars of responsible use, explainability, privacy protection, as well as safety and reliability.
Company Insider or Techie?
To tackle this mammoth task in a multinational corporation, it will be advantageous for Eitler that he already knows Mercedes across departments and national borders through his previous role as Global CISO and can possibly anticipate company-specific dynamics. "Like in cybersecurity, AI does not only work in isolated instances but across the entire company," Eitler describes it. He therefore relies on a strong internal network and more speed in implementation.
His counterpart Barak Turovsky at GM, however, cannot boast this "company insider" status. Before his engagement with the US car manufacturer, the recognised AI expert was active in the tech and software world of Silicon Valley, including almost eight years at Google, where he was responsible for AI-driven product development. The difficulty that outsiders face in traditionally very hierarchical car manufacturers has been shown, among other things, by the "JJ at VW" case, but the newcomer factor could prove to be a bonus, especially in an innovation topic like artificial intelligence.
Malte Broxtermann agrees: "The software industry, in particular, is certainly more shaped by data-driven and scaling-oriented working methods than other, more analogue industries. This is undoubtedly advantageous for the broad adoption of AI."
CAIOs must establish themselves quickly
But regardless of their previous career path, the newly installed CAIOs will undoubtedly also encounter internal resistance. Although the new decision-makers - as can be seen very well in the example of Mercedes - will initially have a lot of backing from top management. After all, it was the company's leadership that recognized the potential of AI and a key function like the CAIO. "However, the substantive board that needs to be drilled does not become thinner as a result," emphasizes Berylls partner Broxtermann. "Because with the corresponding role, there are typically high expectations for AI adoption, scaling, and measurable business contribution. And all of this is parallel to the already ongoing responsibilities of the CIOs."
According to a Gartner survey, the responsibilities for AI initiatives in companies worldwide currently lie more with the CIO at 25 percent than with an explicit AI leader (16 percent). This is followed by department heads with 12 percent and the CTO with 10 percent of respondents who see it this way. The CDO almost no longer appears in this survey.
So that the CAIO does not suffer the same fate of relevance as the Chief Digital Officer sooner or later, it is important for the new AI decision-makers to break down silo thinking as quickly as possible, create clarity in structures and processes, and ultimately achieve a noticeable value contribution from AI and data in collaboration with management and the workforce. No easy task.
This article was first published
at automotiveit.eu
