Beyond hours saved: Building the business case for agentic automation
The RPA-era ROI model misses most of the value agentic automation creates. This post gives AI center of excellence leaders a framework to size the full value of agents across time savings, exception handling, decision quality, and maintenance economics, and to prioritize which workflows to automate first.
Agentic automation, software that reasons and adapts to complete tasks, is appearing on AI center of excellence (AI CoE) roadmaps. Traditional return on investment (ROI) models were built for rule-based, stable, high-volume processes, but agentic automation offers more value. This framework explores the full value of agentic automation and how to measure it.
The classic ROI model is designed for rule-based tasks and fails to capture most of the value created by agentic automation. Companies often misunderstand the benefits of automation, underestimating the exceptions, decision quality, and change resilience that agents can provide. By considering four dimensions of value, a more comprehensive business case can be built.
Time savings are still vital, but agents can extend it to a larger base of work. Exception handling is crucial, as errors can cost significantly more than the original transaction. Decision quality is another important factor, as agents can apply common policies at scale and provide better decision quality. Finally, change resilience and maintenance economics must be considered, as scripts can become brittle, and agents may offer better maintenance solutions.
The value realization condition ensures that every benefit is converted into economic value and accountable for.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
