Our client wanted to better understand how leading enterprises are managing the rapidly growing costs associated with AI adoption, particularly token consumption and usage across different functions and use cases.
The objective was to assess how leading enterprises manage AI token consumption and costs, identify effective optimization and governance approaches, and understand how AI cost management is expected to evolve.
10EQS combined expert interviews with targeted secondary research to assess how enterprises manage and optimize AI-related costs. The study examined AI spend visibility and attribution, token and inference cost drivers, governance and budget controls, optimization measures, and approaches to measuring business value, while identifying emerging best practices and expected market evolution.
10EQS identified that AI costs are becoming increasingly distributed and complex as enterprise adoption scales, with tokens representing only one component of the broader cost base. The research highlighted leading practices around use-case selection, model routing, caching, cost attribution, and federated governance, while finding that sustainable AI economics increasingly depend on linking spend to measurable business outcomes rather than simply minimizing token consumption.
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