As AI adoption accelerates, many organizations have visibility into AI spend but struggle to explain how that spend translates into business value. Traditional FinOps practices focus on cost tracking and optimization, yet leaders increasingly ask: what outcomes are we getting from our AI investments—and how can we prove it?
This lightning talk introduces a practical, FinOps-aligned framework for correlating AI expenses with measurable business outcomes such as revenue impact, customer experience, and operational efficiency. Using real-world, anonymized examples, I will show how teams can operationalize this correlation through techniques like workload tagging, cost allocation across AI use cases, and rule-based mapping between LLM telemetry (tokens, requests, latency) and business KPIs.
Attendees will learn how to define and apply correlation rules, visualize outcome-linked cost dashboards, and embed these insights into existing FinOps workflows for prioritization, budgeting, and executive reporting.