How IT leaders can measure the ROI of agentic AI initiatives

As organizations transition from experimenting with generative AI to operationalizing agentic AI, which can perceive, decide, and act autonomously, the challenge of measuring return on investment (ROI) becomes evident. With a significant portion of CIOs either having adopted or planning to adopt agentic AI, there is a noted risk that most use cases may not meet expected value. Traditional ROI models fall short due to the nondeterministic nature of agentic AI, which impacts costs and value metrics such as quality and scalability. IT leaders are encouraged to adopt new financial frameworks that account for the variable nature of these systems. Essential steps include setting baselines for human task metrics before AI deployment and addressing overlooked costs like application development, integration, and data management. New metrics such as Agent Value Multiple (AVM) and Success Rate are proposed to accurately assess the value and efficiency of agentic AI. By thoroughly evaluating these factors, organizations can ensure that their AI initiatives yield tangible productivity gains and sustainable business value.

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