AWS released a framework to help AI centers of excellence build a business case for agentic automation, addressing limitations of traditional ROI models. The framework introduces the Agentic Value Model, which measures four dimensions of value, including exception handling and decision quality, to capture the full value of agentic automation.
The traditional ROI model, designed for rule-based automation like RPA, fails to account for the value created by agentic automation, which often lies in exception handling and decision-making. AWS guidance highlights that correcting errors can cost 1.5–4 times the original transaction, and human error can account for 2–15 percent of operational costs.
Agentic automation is built on AWS’s machine learning and automation platforms and targets workflows that require judgment, exceptions, and coordination across systems. The framework is available to AI centers of excellence and aims to help organizations redeploy freed labor to measurable outcomes.
"The real gains come from redesigning the workflow around agents, not from dropping an agent into an unchanged process," said AWS Prescriptive Guidance. This approach ensures that automation investments are aligned with business outcomes and process improvements.
The announcement follows McKinsey’s findings on the 1:3:5 pattern of AI investment, where organizations spend three times on process redesign and five times on capability building. AWS emphasized that the framework helps avoid underfunding the work that turns saved hours into results.
AWS did not specify the exact implementation timeline for the framework, and the open question remains about how to effectively measure and realize the full value of agentic automation. The framework is part of a broader effort to support organizations in adopting agentic automation effectively.
Source: awsml
