The AI to ROI team, Ray Rike and Peter Buchanan, mark the official launch of The Big Book of AI Metrics, an 180-page, 81-metric operator's reference guide built to close the gap between AI adoption and AI ROI. Twenty-seven percent of executives say AI has met their ROI expectations, enterprise AI token spend is up 13x since last year, and most companies still can't explain what they got for the investment. Ray and Peter break down why that gap exists and what to do about it.
Topics covered:
Why adoption, utilization, and outcomes are three different things, and why most companies stop measuring at adoption
The five layer causal chain framework: input signals, leading indicators, operational KPIs, financial outcomes, and strategic value
Why establishing a baseline before deployment is the single most skipped step, and why skipping it turns results into opinion instead of evidence
Four real world case studies: Petrobras ($120M in tax savings), Stocks Insurance (83% reduction in claims processing time), Uber's cautionary token budget blowout, and Klarna's revenue per employee gains
Three actions operators should take this week: define the outcome metric, establish a baseline, and build a measurement cadence before and after deployment
Key quote: "Adoption still is not ROI. Outcomes are ROI. And outcomes that translate into better financial performance, that's true ROI that a CFO, investor, and a board of directors can get behind." - Ray Rike
The Big Book of AI Metrics is organized into 13 functional roles, covering both operating executives investing in AI to improve their functions and B2B software executives whose product economics now depend on token consumption, inference costs, and gross margin impact.
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