Introduction
Over the past three years, I’ve led the implementation of AI Agent automation across our insurance operations, transforming how we handle email-based workflows. What started as a pilot project to process claim-related emails has evolved into a sophisticated system handling thousands of documents daily, reducing manual processing time by 85% while improving accuracy.
But here’s what I’ve learned: not every process is a good candidate for AI Agent automation, and jumping into automation without a proper framework costs both time and money.
In this article, I’ll share the exact methodology we use to evaluate, prioritize, and implement AI Agent automation—specifically for process automation scenarios like ours where AI Agents read, classify, extract, and integrate data across multiple systems.