The morning routine for finance professionals often involves a significant amount of time spent on data compilation. At AWS, Financial Planning and Analysis (FP&A) teams were dedicating hundreds of hours each month to gathering numbers from various systems, reconciling sources, and preparing reports. This tedious process left little time for actual analysis and strategic planning.
Enter Amazon Quick, a generative AI assistant designed to streamline these workflows. By connecting to enterprise data and applications, Amazon Quick enables business users to search, analyze, and take action through natural language processing. This powerful tool handles complex queries, advanced analytics, and automates recurring workflows, freeing up teams to focus on more strategic tasks.
Scenario Modeling and Risk Analysis Across the Strategic Portfolio
One of the most time-consuming tasks for AWS Finance teams was setting financial targets for strategic customers. This process required reconciling bottom-up forecasts from business teams with top-down projections from leadership, all while identifying potential risks hidden in historical data.
The team developed an Amazon Quick chat agent that connects directly to enterprise data sources, delivering sophisticated insights through natural language conversation. This agent queries millions of rows across Amazon Redshift data tables instantly, while also searching external data signals.
Before implementing Amazon Quick, analysts could only deep-dive into roughly a third of strategic customers due to time constraints. A single customer analysis consumed up to 6 hours of manual work, including data extraction, model running, and documentation. With Amazon Quick, the agent evaluates statistical forecasts, runs regression analysis, Monte Carlo simulations, and performs scenario modeling across multiple factors in approximately 10 minutes per customer. It surfaces risks and opportunities that manual analysis might miss, allowing the team to cover their entire customer portfolio with greater depth.
“We have expanded from deep-diving a third of our strategic customers to covering our entire portfolio. Our finance team now spends time on what matters: partnering with the business to drive revenue, not compiling data or writing complex queries.”
— Geoff Winkler
The chat agent allows analysts to ask questions in natural language, such as “Run an opportunity and risk assessment for our top strategic accounts.” Amazon Quick then queries millions of rows, runs advanced analytics, and synthesizes structured data with unstructured insights from field reports and pipeline data. It performs bull versus bear analysis by reviewing accounts with upside potential based on contract renewal timing and pipeline strength, and flags accounts with risk exposure. These insights, which traditional models might miss, are now readily available.
Because there’s no coding barrier, every finance professional on the team becomes a data analyst. Teams can customize agents for different regions or business units, and the insights refresh automatically.
Weekly Business Reviews from 6 Hours to 10 Minutes
Regular business reviews are another recurring ritual that occupies FP&A teams. At AWS, every week, insights on revenue performance need to be compiled, analyzed, and packaged for leadership. This preparation traditionally consumed an entire Monday.
The AWS Finance team solved this by deploying Amazon Quick chat agents specific to each geographic region, connected through Flows to automate workflows that run on a set cadence without manual intervention.
Before implementing Amazon Quick, FP&A analysts spent a full morning compiling data from multiple systems, analyzing trends, manually reaching out to sales leads for customer anecdotes, and preparing talk tracks. The process was manual, repetitive, and left little time for strategic work.
With Amazon Quick, the Flow runs automatically each Monday morning. Region-specific chat agents analyze revenue performance across multiple dimensions: by charge type, by customer segment, and by growth contribution. They prepare comprehensive insights with ready-to-use talk tracks for leadership. Fresh analysis is waiting before the workday begins.
Amazon Quick doesn’t only report numbers. It connects structured data from financial systems with unstructured insights from field reports to get to the why behind the trends. It examines customers across over a dozen dimensions, identifies patterns, and flags anomalies with context.
“These insights are prepared automatically every Monday morning. Our team now spends time on strategic priorities instead of compiling disparate data. We spend more time on the why and on driving business outcomes.”
— Geoff Winkler
The Shift from Data Compilation to Strategic Partnership
These use cases highlight a common theme: the bottleneck wasn’t analytical skill but data compilation. Data was scattered across systems, requiring hours of manual extraction before any real analysis could begin.
Amazon Quick removes this bottleneck by connecting directly to enterprise data sources and letting finance professionals interact with their data through natural language. The result isn’t just incremental efficiency; it changes how finance teams spend their time, shifting focus from data compilation to strategic partnership.
