Summary
In this module, you learned how an AI Center of Excellence (CoE) helps an organization adopt AI by aligning business goals, setting strategy and standards, establishing governance guardrails, creating reusable patterns, supporting intake and prioritization, and building AI skills. You reviewed a responsibility model where the AI CoE guides adoption, platform teams provide governed foundations such as identity, networking, policy, observability, and approved services, and workload or business teams own use cases, domain data, integration, delivery, and business outcomes. You also saw that modern enterprise AI work usually involves selecting and evaluating models, building copilots and agents, grounding responses in enterprise data, connecting agents to approved Model Context Protocol (MCP) servers that expose tools and resources where supported, and operating solutions in Microsoft Foundry rather than developing foundation models from scratch. You also considered MCP governance practices such as approved-server allow lists and allowed-tool lists, user or admin approvals, audit logging, and prompt or context data-sharing review before using third-party MCP servers. Finally, you explored multidisciplinary role archetypes that might include executive sponsorship or AI leadership, CoE or advisory leadership, business and domain owners, product or strategy ownership, platform and workload teams, AI app or agent engineering, data stewardship and engineering, responsible AI, governance, risk, security, evaluation, and operations. Not every organization needs every title, but successful adoption requires clear ownership across these areas.