Multicloud FinOps Agentic Intelligence

Rogers cloud economics & AI cost governance command center

AzureAWSGCPFY26 Q3 · Updated 04:00 UTC

Executive Briefing

Board-level summary — FY26 Q3 cloud and AI economics

Prepared by the Executive Briefing Agent from the consolidated findings of the specialist agent fleet. Figures are normalized across Azure, AWS and Google Cloud.

Distribution: CFO, CIO, Group Exec Committee

Cloud and AI spend increased this quarter because AI workloads, data analytics, and network processing expanded across Azure, AWS, and Google Cloud without consistent cross-cloud governance. The Multi-Cloud FinOps Agentic Intelligence identified $18.7M in annual savings opportunities, including idle resources, rightsizing, AI spend rationalization, and workload placement improvements. The recommendation is to preserve multi-cloud flexibility while centralizing visibility, accountability, and AI cost governance through a common FinOps operating model.

Annual run rate

$112.4M

Savings identified

$18.7M

Overrun risk if no action

$9.4M

AI spend under review

$14.8M

Executive decisions required

Select to record approval in the governance audit trail

Operating model recommendation

Common FinOps model across all three providers

Centralize visibility

One normalized cost model in Microsoft Fabric and OneLake, with FinOps hubs ingesting all three providers daily.

Distribute accountability

Every workload and agent carries a business owner, technical owner and budget envelope enforced through Entra ID and policy.

Govern AI as a cost class

AI spend measured per workflow outcome, with Agent365 certification, thresholds and retirement workflows.