Last quarter, at the AI for Finance Forum in New York City, co-hosted by AWS and CFO Leadership, financial industry leaders came together to address the same question: How do we move from AI experimentation to real business value?
In the end, the answer isn't about technology. It's about clarity. Be clear about the problem you're trying to solve, how you'll measure success, and how AI will fit into your existing team.
Two ideas stand out from this day. A practical framework for working with AI agents and three Amazon Finance use cases that show what “good” actually looks like.
How Amazon Finance leverages AI in three real-world examples
The keynote included three Amazon Finance use cases that demonstrate these principles in action. It wasn't the technology that made them so convincing. It was clarity about the problem, the solution, and the outcome.
Acceleration of settlement of accounts
Amazon's finance team was spending 35,000 hours a year manually performing variance analysis. They built an AI-powered agent for proactive anomaly detection. The analysis of variance task was reduced from 15 minutes to 1 minute, a reduction of 93% and 14,000 hours of manual time saved. The purpose of this job is to help accountants increase productivity by eliminating repetitive tasks, allowing them to focus on judgment, analysis, and business partnerships.
Integrated financial workbench
The team spent several days collecting data from multiple sources for the month-end report. Amazon has created an AI-powered integrated workbench with configurable workflows. As a result, month-end report generation time has been reduced by 60-70%. By integrating trusted data sources and strict guardrails, we designed a system that avoids AI illusions, which is essential for financial applications where accuracy is non-negotiable.
FinOps work center powered by AI
Amazon's FinOps team handles more than 10 million operational cases annually across more than 10 disconnected systems. They implemented guided workflows powered by AI. As a result, first-time resolution rates improved by 35% and repeat case rates decreased by 38%. This is a strong indication that accuracy is improved because cases are solved correctly the first time. At the same time, the team's throughput increased by 34%. This is the rare case where you don't have to choose between productivity, accuracy, and quality of service. Investments in AI have achieved all three at once.
Agent as Teammate Framework
What ties these examples together is a mental model worth considering. As part of the event, Lindsey Drake, vice president of finance at AWS Applied AI Solutions, shared how Amazon Finance approaches complex challenges with a combination of technology and talented people. The same philosophy applies here. Think of your AI agent as a teammate, or more specifically, an intern on your team.
Like any other intern, AI agents require clear instructions, proper context, and consistent supervision. They excel at high-volume, repetitive tasks and enable team members to be assigned to strategic priorities. You wouldn't hand an intern a complex acquisition model and walk away. The same guardrails apply to AI agents.
But this is where the investment pays off. Just like a good intern becomes more competent over time, an AI agent can improve with proper training and feedback loops. The key is to build integration into your workflow. That means establishing review processes, creating human checkpoints, and designing handoffs between agent and human work.
Investing in both your agents and how they work in conjunction with your existing team will unlock real benefits. Agents handle day-to-day tasks at scale around the clock, while human teams focus on decision-making, strategy, and relationship-building.
The pattern behind the results
What unites these three examples is not the sophistication of AI. It's a discipline. Each challenge started with a specific, measurable problem. Before building anything, each defined success in business terms. And each treated AI as a tool that augments human work, rather than replacing human judgment.
For financial leaders considering AI, these examples provide a practical template. Identify where your team is drowning in manual, repetitive tasks. Design AI solutions with clear guardrails and human oversight. And measure outcomes that matter to your business, not just “I’m using AI.”
Finance teams that win with AI aren’t the ones with the most ambitious pilots. They solve real problems one use case at a time.
what to do next
For finance leaders ready to move from experimentation to results, take these four steps this month.
- Identify one painful, large-scale process. Look for manual, repetitive, and time-consuming tasks. Analysis of variance, data collection, and reconciliation are good starting points.
- Define success before you build. What business outcomes will be improved? Time savings, fewer errors, shorter cycle times, better customer experience? Write it down.
- Let's start small with guardrails. Pilot with one team, one process, and one clear scope. Incorporate human review checkpoints. Treat your AI agents like interns, not experts.
- Measure and repeat. Track results against defined success metrics. Learn what works. We will expand from there.
The question is not whether AI will transform finance. It's whether the team is ready when that happens.
