How to find the best AI Use Cases for Finance
Recently, Rippling hosted an AMA session with me and I got a lot of great questions from the finance community about AI adoption.
“What are some use cases for AI that my finance team can get started with?”
That question makes sense. Most finance teams already have established processes and want to understand where AI can help. Phrased this way, we usually hear familiar ideas: draft memos, perform technical accounting research, run quick data analyses.
These are solid first steps, especially for colleagues who have never used ChatGPT or similar tools at work. Yet after the initial excitement, many teams slide back to their regular routines, and the AI pilot remains an interesting experiment rather than a daily tool.
If that pattern sounds familiar, try a different starting point.
Start with “What is not working for us?”
Spend time, either individually or as a group, identifying the gap between your goals and today’s reality:
Close timing. Do you need to shorten month-end close by ten days?
Manual errors. Are high-risk journal entries still keyed by hand?
Workload. Are late nights becoming normal as you keep the finance machine running?
If the problems are obvious, great. If not, apply a helpful constraint:
“If we had to close revenue on BD+1, what changes would make that possible?”
BD+1 leaves no room for last-minute manual entries. Numbers post automatically on BD0, and the team reviews, adjusts estimates, or books accruals the next day.
Define “good” before you automate
Starting with a real challenge provides clear benefits:
Motivation is built in. Everyone already cares about the pain point.
The finish line is measurable. For example, “We are done when revenue closes on BD+1.”
Effort is transparent. A time-boxed target reveals every manual step that still needs automation.
Consider FP&A. If updating the monthly model takes four hours after accounting finishes, set success as the flash report being ready within 30 minutes. That goal forces a close look at every “quick” spreadsheet tweak that quietly adds up.
Why this approach leads to lasting change
AI can remove manual data entry, automate spreadsheet routines, and streamline reconciliations, but these gains matter only when they address problems your team already feels. When you anchor a project to a real objective, you ensure that:
The solution tackles a genuine bottleneck.
The team stays engaged because the outcome matters.
The improvement endures instead of fading after a brief trial.
So rather than collecting a generic list of AI ideas, start by clarifying what is not working today. Set a concrete target, then explore how AI tools can help you reach it. Once the problem is clear and the goal is specific, the right use case will surface naturally and it will stick.
Live AI Masterclass on June 13
I’m partnering with Angela Liu at GaapSavvy to present an AI Masterclass for Accounting. This is a deep-dive into how finance teams can deploy AI today, full of use cases and implementation examples. We still have some spots left, register here
And now for something new…
Introducing the Lumera Podcast!
In this first episode (36 min), I chatted with Edwine Alphonse, Senior Controller at Ramp, about her early career, her transition to Ramp, and how her team has been able to build a world class organization. She led Ramp’s accounting function during a period of incredible growth and her team is remarkably lean and efficient given the complexity of their business.
Edwine recently left Ramp to start a new venture. Check out her work at yourstartupcpa.com, and subscribe to her newsletter here!
I saw the Ramp logo and had to pause and click.
Ramp is really make a splash for businesses who need great spend management.
I'm learning more about it so I can recommend it to clients who need something like that.