First is the AI strategy leader. This person serves as the connective tissue across the enterprise, defining the AI roadmap, evolving the operating model and orchestrating how the rest of the CoE supports the broader organization. They think through priorities, risks and investment sequencing, and often develop reusable assets such as intake forms, validation templates and lifecycle checklists that domain teams can adopt and adapt for their own use cases. They also play a critical role in promoting awareness and adoption of responsible AI frameworks, facilitating reviews for sensitive or cross-functional use cases, often via an ethics or risk committee.
The second essential role is the architect. This individual owns the technology architecture that underpins enterprise-scale AI. They’re responsible for designing and maintaining the shared infrastructure: things like secure, GPU-enabled sandboxes, model registries and MLOps pipelines. These inputs allow domain teams to build and deploy responsibly and efficiently. They also define and enforce enterprise-wide data governance standards, recognizing that, like any technology, AI depends entirely on the quality and context of the data it consumes.
Next is the teacher, a role we think every CoE should prioritize early. This person leads the education motion across the organization, building awareness around the benefits and risks of AI and enabling teams to upskill continuously as the technology evolves. They’re responsible for designing role-based learning programs and for training the spokes on key delivery processes and enterprise guidelines.
