In this interview from FinOps X 2026 in San Diego, Marco Meinardi, vice president analyst at Gartner, joins theCUBE's John Furrier and theCUBE Research's Paul Nashawaty to discuss how FinOps is evolving from cloud cost control into a broader discipline for managing AI value across the enterprise. Meinardi explains that AI fundamentally complicates cost attribution — not only because agentic workloads can generate runaway token costs, but because end-user interactions with AI applications now directly influence spend outside the organization's control. He highlights why traditional tagging strategies fall short in AI environments, where instrumentation and telemetry must be embedded at the application level to connect infrastructure spend to business outcomes.

The conversation also explores how data sovereignty and regulation — not cost alone — are keeping AI workloads on premises, particularly for organizations wary of entrusting sensitive data to US-based providers. Meinardi details how FinOps maturity is shifting from reactive cost cleanup to a shift-left model embedded earlier in the software development life cycle. He also outlines the convergence of historically separate disciplines — FinOps, IT financial management (ITFM) and IT expense management (ITEM) — as metered SaaS usage, AI agents acting as digital workers and hybrid inference infrastructure blur traditional domain boundaries. From the evolving Gartner Magic Quadrant for cloud financial management tools to the anticipated consolidation between cost platforms and LLM observability, Meinardi maps how the industry is building the governance infrastructure that serious AI investment demands.