Four Things to Watch in Enterprise AI Infrastructure

Enterprise AI is moving beyond individual copilots, chatbots, and proofs of concept. As adoption grows, a new infrastructure layer is emerging around the recurring technical problems that every AI application should not have to solve independently. In this session, you will learn about four pieces of enterprise AI infrastructure that are likely to shape how organizations build and operate AI at scale. You will see how model infrastructure is evolving to handle hosting, inference, routing, capacity, governance, and cost management. You will learn what agent infrastructure needs to provide for identity, permissions, state, observability, and lifecycle management. You will explore how MCP infrastructure can create a governed and reusable way for agents to discover and access enterprise tools. You will also learn why context infrastructure is becoming critical for supplying AI systems with trusted enterprise knowledge, semantics, provenance, permissions, and operational state. Along the way, you will see how Microsoft, AWS, Google, and the broader AI ecosystem are beginning to implement these patterns, where the technologies overlap, and where they solve different architectural problems. You will leave with a practical framework for identifying these emerging infrastructure categories, understanding the problems each one addresses, and deciding where they fit within your enterprise AI architecture.