PM2ALL - Gestão de Projetos | IA | Intelligent PMIS

FinOps for AI — Article 2 of 5 The previous article in this series established the diagnosis: AI workloads have broken the traditional cloud cost model, and the FinOps practices inherited from the infrastructure era are structurally inadequate to govern the economics of tokens, inferences, and autonomous agents. The deterioration of the Cloud Efficiency Rate is not a tooling problem. It is a governance model problem. If the first article explained why the cloud efficiency paradox exists, this article explains why solving it requires a new governance and measurement foundation. The article does not yet describe the full operating model of FinOps for AI. It defines the foundation on which such an operating model must be built: governance, visibility, attribution, unit economics, and adaptive budgeting.

Most project managers might not describe tracking as the most exhilarating part of their job. However, its significance can't be overstated, as it forms the foundation for answering the crucial question on every stakeholder's mind - "How's the project progressing?" Establishing a baseline is certainly essential, but its true value is realized when used in conjunction with regular tracking. In the absence of a baseline or consistent tracking, any answers given to the progress question would be speculative at best.