Barak Turovsky, former Chief AI Officer at General Motors, shares insider insights into one of the most critical yet misunderstood executive roles in modern corporations. With a distinguished career spanning Google, Cisco, and AI startups since 2014, Turovsky led the first scaled deployment of LLMs and Deep Neural Networks with Google Translate before joining GM.
Turovsky’s CAIO role at GM, which no longer exists following the company’s restructuring of its software and AI organization, reported directly to the head of software engineering. He argues that while the title may vary, successful AI implementation requires dedicated leadership with deep technical expertise to drive organizational change and commitment from the top.
Using a compelling restaurant analogy, Turovsky explains the CAIO role as a “master chef” orchestrating three primary resources: AI infrastructure and models (kitchen equipment), data and internal assets (ingredients), and talent at various expertise levels (restaurant staff). For companies requiring cutting-edge AI solutions—particularly in untouched areas like AI for physical products such as automobiles—developing proprietary solutions becomes essential when standard versions fall short.
The role encompasses three critical buckets of responsibility. First, AI talent management focuses on hiring and retaining top-tier teams capable of navigating novel spaces with motivation and flexibility. Second, creating a culture of innovation involves working with internal stakeholders resistant to change and fostering organizational transformation. Third, organizational change management requires mapping company needs, identifying both AI enthusiasts and skeptics, and establishing a framework with clear top-down goals while empowering bottom-up champions.
Turovsky emphasizes that securing top talent remains the hardest and most important aspect of the job. Traditional functional leaders like CTOs or CIOs may lack the specialized AI knowledge required for successful integration. In large organizations, powerful executives often want the benefits of scaling AI without the responsibility, creating a critical need for someone with deep AI expertise to “direct the traffic.” The CAIO cannot work in isolation—every function needs identified champions who are nurtured and empowered to drive AI adoption throughout the organization.
Key Quotes
I do believe successful AI implementation requires someone in leadership to drive that change, as well as commitment from the top.
Turovsky addresses skepticism about whether companies need dedicated AI officers, emphasizing that regardless of title, AI transformation requires specialized leadership with executive backing to succeed.
Traditional large companies have powerful executives who want to own the benefits of scaling AI, but not necessarily the responsibility.
This observation explains why dedicated AI leadership is crucial—existing executives often lack the expertise or willingness to take ownership of AI transformation challenges.
The hardest and most important part of the job is securing top talent.
Despite infrastructure and data challenges, Turovsky identifies talent acquisition as the CAIO’s most critical responsibility, reflecting the severe shortage of qualified AI professionals.
The CAIO can’t do all the magic while everyone else just sits there.
Turovsky emphasizes that AI transformation requires organizational buy-in and distributed champions across functions, not just top-down directives from the AI executive.
Our Take
Turovsky’s account reveals a fundamental tension in corporate AI adoption: the gap between executive ambition and organizational capability. His restaurant analogy effectively demystifies the CAIO role, but the elimination of his position at GM raises important questions. Is the CAIO a transitional role that becomes obsolete once AI is embedded organizationally, or did GM prematurely restructure before achieving transformation?
The emphasis on talent over technology is particularly insightful. While vendors promise turnkey AI solutions, Turovsky’s experience suggests that proprietary expertise remains the true competitive advantage, especially for physical products where AI applications are nascent. His framework for organizational change—identifying champions and creating top-down/bottom-up alignment—offers a practical roadmap beyond the hype. As companies navigate AI integration, those that recognize it as a people and culture challenge rather than merely a technology problem will likely achieve sustainable competitive advantage.
Why This Matters
This insider perspective illuminates a critical leadership gap as companies rush to implement AI strategies. With AI transformation becoming imperative across industries, understanding the CAIO role’s scope and challenges is essential for corporate success. Turovsky’s experience highlights why traditional IT leadership often falls short in AI implementation—the technology requires specialized expertise that CTOs and CIOs may not possess.
The article underscores a broader trend: AI integration is fundamentally an organizational change challenge, not just a technology deployment. Companies investing billions in AI infrastructure often fail because they lack the leadership structure to drive adoption and manage cultural resistance. The talent shortage Turovsky describes reflects the industry’s most pressing constraint, with demand for AI expertise far outpacing supply.
For businesses, this reveals that successful AI adoption requires dedicated executive leadership with both technical depth and change management skills. The restructuring that eliminated Turovsky’s role at GM may signal broader questions about how corporations organize around AI—whether dedicated CAIOs remain necessary or if AI becomes embedded across all functions. This evolution will shape competitive advantage in an AI-driven economy.
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Source: https://www.businessinsider.com/former-chief-ai-officer-general-motors-describes-role-2025-12