Last quarter, the AI for Finance Forum in NYC, co-hosted by AWS and CFO Leadership, brought together finance leaders wrestling with the same question: How do we move from AI experimentation to real business value?
The answer, it turns out, isn’t about the technology. It’s about clarity. Clarity on the problem you’re solving, how you’ll measure success and how AI fits alongside your existing team.
Two ideas from the day stand out. A practical framework for working with AI agents, and three use cases from Amazon Finance that show what “good” actually looks like.
How Amazon Finance Uses AI in Three Real Examples
The keynote included three use cases from Amazon Finance that demonstrated these principles in action. What made them compelling wasn’t the technology. It was the clarity around problems, solutions and outcomes.
Financial Close Acceleration
Amazon’s accounting teams spent 35,000 manual hours annually on variance analysis. They built an AI powered agent for proactive anomaly detection. Variance analysis tasks dropped from 15 minutes to one minute, a 93 percent reduction, saving 14,000 manual hours. This work is all about making accountants more productive by eliminating repetitive work so they can focus on judgment, analysis and business partnership.
Unified Finance Workbench
Teams spent multiple days gathering data from several sources for month end reporting. Amazon created an AI powered unified workbench with configurable workflows. The result was that time to create month end reports dropped by 60 to 70 percent. By integrating authoritative data sources and strict guardrails, they designed the system to avoid AI hallucinations—critical for finance applications where accuracy is non-negotiable.
AI Powered FinOps Workcenter
Amazon’s FinOps teams process over 10 million operational cases annually across more than 10 disconnected systems. They implemented AI powered guided workflows. The result was that first time resolution rates improved by 35 percent and case reopen rates dropped by 38 percent—a strong signal that accuracy improved as cases were resolved correctly the first time. At the same time, team throughput increased by 34 percent. This is the rare case where you don’t have to choose between productivity, accuracy and service quality. The AI investment delivered all three simultaneously.
The Agent as Teammate Framework
What ties these examples together is a mental model worth considering. As part of this event Lindsey Drake, VP Finance, AWS Applied AI Solutions, shared how Amazon Finance approaches complex challenges with a combination of technology and talented people. That same philosophy applies here. Think of AI agents as teammates—specifically, as interns on your team.
Like any intern, AI agents need clear direction, proper context and consistent oversight. They excel at high volume, repetitive work, freeing team members for strategic priorities. You wouldn’t hand an intern a complex acquisition model and walk away. The same guardrails apply to AI agents.
But here’s where the investment pays off. Just as good interns become more capable over time, AI agents improve with proper training and feedback loops. The key is building integration into your workflow. That means establishing review processes, creating human in the loop checkpoints and designing handoffs between agent and human work.
When you invest in both the agent and how it works alongside your existing team, you unlock the real advantage. Agents handle routine work at scale around the clock at scale while your human team focuses on judgment, strategy and relationship building.
The Pattern Behind the Results
What connects these three examples isn’t AI sophistication. It’s discipline. Each one started with a specific, measurable pain point. Each defined success in business terms before building anything. And each treated AI as a tool to augment human work, not replace human judgment.
For finance leaders exploring AI, these examples offer a practical template. Identify where your team is drowning in manual, repetitive work. Design AI solutions with clear guardrails and human oversight. And measure outcomes that matter to the business, not just “we’re using AI.”
The finance teams winning with AI aren’t the ones with the most ambitious pilots. They’re the ones solving real problems, one use case at a time.
What To Do Next
For finance leaders ready to move from experimentation to results, here are four steps to take this month:
- Identify one high pain, high volume process. Look for work that is manual, repetitive and time consuming. Variance analysis, data gathering and reconciliations are good starting points.
- Define success before you build. What business outcome will improve? Hours saved, error reduction, faster cycle times, better customer experience? Write it down.
- Start small with guardrails. Pilot with one team, one process, one clear scope. Build in human review checkpoints. Treat your AI agent like an intern, not an expert.
- Measure and iterate. Track results against your defined success metrics. Learn what works. Expand from there.
The question isn’t whether AI will transform finance. It’s whether your team will be ready when it does.
