I'm the former Chief AI Officer at General Motors, and I’ve been working on AI and large language models since 2014—long before they became a global phenomenon. My career spans some of the most influential tech companies, including Google, where I led the first large-scale deployment of LLMs and deep neural networks in Google Translate, and Cisco, where I served as VP of AI. I also held executive roles at a computer vision AI startup. When General Motors approached me for the CAIO role, it felt like a unique opportunity to apply AI to physical products—a space that’s complex, hands-on, and deeply rooted in engineering. I reported to the head of software engineering, who in turn reported directly to the CEO. The role no longer exists in its original form, as GM restructured its software and AI teams after I left. But during my time there, I saw firsthand how critical it is to have a dedicated leader driving AI transformation. People often ask whether a company really needs a Chief AI Officer. The title may vary—some call it Head of AI, Chief Technology Officer for AI, or something else—but the function remains essential. Functional leaders like the CTO or CIO may have strong technical backgrounds, but they often lack deep AI expertise. When you’re integrating AI into software and products, you need someone who understands the nuances of model development, data pipelines, and deployment at scale. In large organizations, executives often want to claim the benefits of AI without taking on the responsibility. That’s where a CAIO comes in—someone who can bridge the gap between vision and execution, and who has the technical depth to guide the organization through uncertainty. I use a restaurant analogy to explain the role. Think of a CAIO as the master chef. The kitchen equipment represents the AI infrastructure—cloud platforms, GPUs, and model frameworks. The ingredients are the data: internal datasets, training materials, and domain-specific information. And the staff? That’s the talent—engineers, data scientists, product managers, and researchers at every level. The complexity of the final product depends on the company’s goals. If you're making a simple dish, off-the-shelf tools and standard models may suffice. But for a high-end, custom dish—like a complex AI system for autonomous vehicles—you need to build your own. That’s where the master chef’s skill is most needed. The most challenging part of the job? Securing top talent. Vendors promise quick results—like a toaster oven that bakes a perfect soufflé in minutes. But if your ingredients are late, inconsistent, or low quality, and your team is disengaged, the final product will fail. Customers come in hungry, expect a full meal, and leave without paying. So what should a CAIO focus on? Three key areas. First, talent management. In a new, uncharted space, the pool of qualified people is small. You need to attract and retain elite talent—people who are not only skilled but also adaptable and passionate about innovation. Second, fostering a culture of innovation. You must work with teams used to traditional processes and help them embrace change. This requires patience, communication, and constant education. Third, driving organizational change. You need to map the landscape—identify champions, skeptics, and early adopters. Create a framework that works both top-down and bottom-up. Empower teams across departments, give them ownership, and build momentum. The CAIO isn’t a magician who does everything alone. They’re the architect of a system where AI can thrive—where the right ingredients are available, the tools are in place, and the team is ready to cook.