How AWS Finance Boosts Efficiency Using Amazon Quick

AWS ML Blog · rss · 2026-07-08

The AWS Finance team previously spent hundreds of hours each month on data aggregation and report generation. This article shares how they leveraged **Amazon Quick**, a generative AI assistant, to significantly boost efficiency across two core workflows: 1. **Strategic Customer Risk & Scenario Analysis**: Previously, analysts could only conduct deep-dive analysis on a third of their strategic accounts (taking about 6 hours per customer). Now, using the Quick conversational agent, the team queries millions of rows of Redshift data directly via natural language. It automatically runs regression analysis, Monte Carlo simulations, and multi-factor scenario modeling, cutting per-customer analysis down to roughly 10 minutes and achieving full coverage of the customer portfolio. 2. **Weekly Business Review Automation**: Financial analysts used to spend entire Monday mornings pulling data from multiple systems, analyzing trends, and drafting executive reports. Now, via scheduled tasks in Quick Flows, regional chat agents automatically analyze revenue performance, correlate unstructured field reports, and generate executive summary points with business insights every Monday morning. This case study illustrates how AI tools effectively break down data integration bottlenecks, allowing finance professionals to shift from "pulling data" to high-value strategic analysis and business partnering.

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