The rise of AI FinOps: From CIO-led innovation to CFO-led AI economics
By Vidisha Suman, Senior Partner, and Shiv Raj, Principal, Kearney
In the first phase of enterprise AI adoption, the focus was primarily on experimentation, accessibility, and deployment speed. As unit economics kicks-in, AI will rapidly evolve from an innovation initiative into a recurring operating expense, forcing enterprises to rethink how intelligence is allocated, governed, and scaled across workflows.
Hence, the central enterprise question is increasingly practical rather than conceptual: what is the cost required to achieve a successful business outcome?
The relevant framework is workload economics.
Two dimensions matter most.
The first is cost intensity, which represents the total cost required to deliver an AI-enabled outcome, including inference, orchestration, governance, remediation, or supporting infrastructure.
The second is value density, which reflects the measurable business value generated by a use case, including revenue uplift, cycle-time reduction, quality improvement, labour savings, or risk reduction.
Low Value Density
High Value Density
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