CIO confession 2 of 6: Your AI agents look healthy. Are they paying off?

A Dataiku and Harris Poll survey of 685 enterprise CIOs finds that accountability for AI returns is increasing faster than organizations’ ability to measure value, with only 24% reporting mature ROI measurement across most or all AI initiatives and 41% doing so even for select use cases. AI cost tracking is fragmented across models, infrastructure, tools, teams, and agents, while just 21% of CIOs have near-real-time, attributable visibility into advanced AI workload costs; major concerns include limited cost visibility, hidden expenses from agent proliferation, and redundant tools. Token, compute, and unpredictable usage costs are already influencing AI planning, approvals, and scaling decisions. The report argues that technical indicators such as uptime and response speed are insufficient without evidence that agents complete their intended work and improve business outcomes. It recommends evaluating every production agent through four connected areas: cost, technical performance, agent effectiveness, and business impact, enabling CIOs to compare investments, prioritize successful initiatives, and provide boards and CEOs with evidence of AI ROI.

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