AWS proposes framework to build business case for agentic automation
Agentic automation—software that reasons and adapts to complete tasks—is appearing on AI Center of Excellence roadmaps, but the classic ROI model built for rule‑based RPA fails to capture its full value. The article explains that the traditional method overlooks exception handling, decision‑quality gains, and the cost of maintaining brittle scripts, which agents can mitigate.
Key points
- Traditional RPA ROI models miss exception handling, decision quality, and change resilience in agentic automation
- AWS's Agentic Value Model measures four value pools and uses a complexity‑risk matrix to prioritize workflows
- Early Amazon Quick Automate deployments reported up to 91% cost savings and 96% faster data acquisition (Kitsa) and 75% automation of reviews (dLocal)
AWS introduces the Agentic Value Model, measuring four dimensions: time savings, exception handling, decision quality, and change‑resilience, each tied to a realization mechanism. It illustrates the approach with a claims‑triage example (200,000 claims, $45/hour labor, 70% automation) and early deployments of Amazon Quick Automate at Kitsa (91% cost savings, 96% faster data acquisition), dLocal (up to 75% of merchant‑compliance reviews automated), and Genpact (supply‑chain risk analysis cut from days to minutes). The post also offers a two‑by‑two complexity‑risk matrix for workflow prioritization and guidance on presenting a portfolio‑level business case to leadership, citing McKinsey’s finding that fewer than 10% of enterprises have scaled agents to tangible value.
Beyond hours saved: Building the business case for agentic automation
AWS Machine Learning Blog · 7 October 2026
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This text was published by AWS Machine Learning Blog and written by Manish Ballal. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
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