The intelligence layer emerges as the control plane for enterprise AI
As enterprises move beyond AI experimentation into full-scale production, the central challenge has shifted from accessing models to managing the organizational context they 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 [...
Key Takeaways
- Establishing a robust intelligence layer provides the critical organizational context needed to manage unbudgeted token costs and govern AI agents safely.
- "You want a single layer that you can have in your organization that is consistent for many, many years.
" Belikoff spoke with theCUBE's John Furrier and Paul Nashawaty at FinOps X 2026 , during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio.
- "We help customers to turn it into their IQ or company IQ," he said.
"What platform do they need in place to consolidate their data across their organization so that it's joined, clean and ready for AI to use?
- Microsoft is embedding those governance controls directly inside GitHub Copilot so that model selection, content safety and security decisions appear in the developer's natural workflow, he noted.
Alongside those controls, Microsoft has built out Agent 365 to give organizations a way to register, monitor and govern AI agents the same way they manage human employees - applying identity, access controls and accountability from the start.
- " Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of FinOps X 2026 : (* Disclosure: TheCUBE is a paid media partner for the FinOps X event.
Stats & Key Facts
- #UPDATED 09:44 EDT / JUNE 10 2026 AI Microsoft builds a single AI intelligence layer so enterprises don't have to start over by Kelly Knight SHARE As enterprises move beyond AI experimentation into full-scale production, the central challenge has shifted from accessing models to managing the organizational context they need to act reliably.
- #Alongside those controls, Microsoft has built out Agent 365 to give organizations a way to register, monitor and govern AI agents the same way they manage human employees - applying identity, access controls and accountability from the start.
Establishing a robust intelligence layer provides the critical organizational context needed to manage unbudgeted token costs and govern AI agents safely. UPDATED 09:44 EDT / JUNE 10 2026 AI Microsoft builds a single AI intelligence layer so enterprises don't have to start over by Kelly Knight SHARE As enterprises move beyond AI experimentation into full-scale production, the central challenge has shifted from accessing models to managing the organizational context they 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 [and] your meetings, but also your structured data, your business processes," he said.
"You want a single layer that you can have in your organization that is consistent for many, many years. " Belikoff spoke with theCUBE's John Furrier and Paul Nashawaty at FinOps X 2026 , during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how Microsoft's intelligence layer strategy, agent governance and marketplace ecosystem are helping enterprises move AI from experimentation to full-scale deployment.
For more details please read the original article at SiliconANGLE AI.
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