AWS Finance teams faced significant challenges in compiling data for financial planning and analysis (FP&A). Analysts spent hundreds of hours each month on data extraction, reconciliation, and reporting, often leaving little time for strategic work. The process involved pulling numbers from multiple systems, building charts, and writing commentary to answer straightforward questions about revenue performance. This manual effort was time-consuming and limited the depth of analysis possible. With Amazon Quick, AWS Finance teams now use generative AI to automate data preparation and analysis, allowing them to focus on strategic activities like risk assessment and revenue growth. Source: awsml
Amazon Quick, a generative AI assistant, connects across enterprise data and applications, enabling business users to search, analyze, and take action through natural language. It handles the complexity of querying millions of rows, running advanced analytics, and automating recurring workflows. In one use case, the team built a chat agent that connects directly to enterprise data sources, delivering insights through natural language conversations. The agent queries millions of rows across Amazon Redshift data tables instantly and searches external data signals. This allowed analysts to evaluate statistical forecasts, run regression analysis, Monte Carlo simulations, and perform scenario modeling across multiple factors in approximately 10 minutes per customer. The team now covers their entire customer portfolio with greater depth than before. Source: awsml
The transformation was evident in the team’s ability to shift focus from data compilation to strategic work. Before Amazon Quick, analysts could only deep-dive roughly a third of strategic customers in the time available between bottom-up inputs and when top-level targets were due. A single customer analysis consumed up to 6 hours of manual work, including extracting data, running models, and documenting findings. After implementing Amazon Quick, the team now covers their entire portfolio with greater depth, and finance professionals spend time on strategic priorities instead of compiling data or writing complex queries. Source: awsml
