AI adoption is scaling fast—and so are the costs. As organizations move from isolated use cases to enterprise-wide adoption, AI spending can nearly quadruple. And with 93 percent of organizations in a McKinsey survey reporting that they’ve exceeded their AI budgets, managing these costs is a growing priority. Even as token prices fall, rising large language model usage and increasingly complex AI systems can keep enterprise spending climbing.
But managing AI economics goes beyond limiting consumption. The bigger opportunity is to get more value from the intelligence organizations consume. Pankaj Sachdeva, Wasim Lala, and coauthors argue that CIOs should manage AI demand around business outcomes, with greater visibility into consumption, smarter model selection, and stronger accountability for business outcomes—a shift away from “tokenmaxxing.”
The other side of the coin is where AI creates value. “AI should be understood not simply as a labor-saving technology but also as one that enables cheaper and better decisions,” write Lari Hämäläinen and Steffen Fuchs in a recent article. Making decisions faster, easier, and at lower cost can help organizations get more from existing assets and capture opportunities.
Explore the insights below for more on turning AI investments into greater value.
Improving the economics of agentic AI: A guide for business leaders
The decision dividend: How AI creates economic value
Where AI agents pay off: A practical guide to the economics of agentic workflows
Is that AI agent worth it? Agentic economics and the modern operating model
Frontiers of compute: The technologies to reduce AI inference costs
The cost of intelligence: How CIOs can manage AI demand at scale