Agentic automation, software that reasons and adapts to complete tasks, is showing up on AI center of excellence (AI CoE) roadmaps. But the standard way companies justify automation investments, hours saved times labor cost minus build cost, was designed for rule-based tools like robotic process automation (RPA). That model misses most of the value agents create.

In this post, we introduce a framework AI CoE leaders can use to build a business case that captures the full value of agentic automation. You will learn why the RPA-era ROI model falls short, how to measure the value it misses, and which workflows are worth automating with agents.

The classic return on investment (ROI) model was built for stable, high-volume, rule-based work: count the transactions, measure the minutes, multiply by a loaded rate, subtract the build cost. RPA earned its place on those terms.

The model assumes the world it was built for. It assumes processes are stable, so it ignores the cost of maintaining automation as they change. It assumes tasks are rule-based, so it has no line item for exceptions. It assumes the work is the whole job, so it never counts the cost of human oversight. And it treats a saved hour as banked value, when freed capacity often refills with backlog and never reaches the profit and loss statement (P&L). Underfunding the work that turns saved hours into results is the mistake McKinsey says companies keep making: successful AI transformations follow a “1:3:5 pattern,” where “for every dollar invested in agentic technology, organizations spend three on process redesign and five on capability building and adoption.

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