Intelligence layer becomes the enterprise AI control plane for enterprise AI
As enterprises accelerate past AI experimentation into full-scale production, the central challenge has shifted from accessing models to managing the organizational context those models need to act reliably. The pressure to govern costs, secure data and maintain accountability is now redefining how companies architect their entire AI intelligence layer.
That convergence of AI adoption and infrastructure discipline is at the heart of what FinOps X 2026 brought to the surface. The industry is reaching a critical inflection point where token economics, governance and data readiness must align before enterprises can unlock sustained value from AI agents, according to Cyril Belikoff (pictured), vice president of commercial cloud and AI at Microsoft Corp.
“You really want an intelligence layer, a layer that has context within your organization that you can train once — on who you are, how you work, your organizational structure, your documents, your meetings, but also your structured data, your business processes,” Belikoff said. “You want a single layer that you can have in your organization that is consistent for many, many years.”
