Beyond the Finance Department: AI Use Cases with Enterprise Impact

Two years ago, if you'd told me Finance would be one of the leading departments on AI adoption, I would've laughed. Finance departments aren't exactly known for being first movers on shiny new tech. We're the people who make you justify every budget line item and ask uncomfortable questions about ROI.

But here's the thing: 2025 has flipped the script. Finance is actually perfectly positioned to drive AI adoption—not just for ourselves, but for the whole company. The use cases you have in finance are not limited to the finance department.

Why? Because the problems we're solving aren't really finance problems. They're business problems that just happen to run through Finance.

Let me walk you through three AI use cases we're rolling out. Each one started in Finance. Each one is making a real difference across every department in the company.

Why We Went with Microsoft 365 Copilot

Quick aside before we dive in: we're using Microsoft 365 Copilot for all of this.

The decision was pretty straightforward. Copilot lives inside the tools everyone already uses—Outlook, Excel, Word, Teams. We didn't want to force people to learn a new platform or add another app to their already-cluttered desktop.

This wasn't about chasing the hottest AI tech. It was about making adoption as painless as possible. When AI shows up in tools people already use every day, you don't have to convince anyone to try it. You just show them what's suddenly possible.

Okay, now let's talk about what we're actually doing.

Use Case 1: Those Expensive Consultant Questions

Everyone Has This Problem

You know how this goes. Someone in Operations needs to figure out inventory valuation methods. HR has questions about equity comp structures. Legal is trying to understand some cross-border contract thing. Sales wants competitive pricing research.

And what happens? Someone calls in consultants. Five grand here, fifteen grand there, sometimes fifty grand for what's basically research and methodology questions that don't need custom analysis.

Every department does this. Every department watches its budget disappear. And everyone wishes there was a better way.

Enter AI

Now we're using Copilot to handle most of these research questions in-house. Need to understand revenue recognition rules? Ask Copilot. Want to compare transfer pricing approaches? Copilot's got it. Looking for best practices on performance management? Same deal.

Look, we're not trying to eliminate consultants. There are definitely times you need deep expertise and custom analysis. But for the research-heavy, "help me understand this concept" questions that used to eat up consultant budgets across the company? AI handles it now.

What Changed

We're already seeing drops in outside consulting spend across various departments. But honestly, the speed matters just as much as the savings. We're getting answers in minutes instead of waiting weeks.

And here's what surprised me: people actually prefer it for these kinds of questions. No scheduling dance. No waiting for proposals. No wondering if you're asking something dumb. Just fast, solid research to help you make a decision.

Use Case 2: Contract Review (The Bottleneck Everyone Hates)

It's Not Just a Finance Thing

Contract review is everyone's nightmare, not just ours.

Sales needs to review customer contracts before closing deals. Procurement reviews supplier agreements. HR handles employment contracts and NDAs. Operations deals with vendors and service agreements. Finance reviews financing deals and partnerships.

And in every single case, contract review becomes this massive bottleneck. Not because anyone's slow—but because thorough contract analysis just takes time. And we're all drowning in contracts.

How AI Helps

We're using Copilot to speed up contract review across the board. The AI quickly identifies key terms, flags potential issues, compares contracts to our standard templates, and surfaces weird stuff that needs human eyes.

It doesn't replace human judgment—we still review everything and make the final calls. But it slashes the time we spend on that initial heavy lifting of reading, analyzing, and comparing contract language.

The Real Win

The time savings are significant, sure. But here's what actually matters: this lets the business grow without adding more people.

Think about it. Your sales team can handle more deals. Your procurement team can evaluate more suppliers. Your HR team can scale hiring. All without everyone screaming for more budget and more headcount.

This is AI as a growth enabler, not just a cost-cutting exercise. And that's a way more interesting story than "we automated away some jobs."

Use Case 3: Forecasting—The 50% Goal

The Time Sink That Hurts Everyone

If you've ever been part of a forecasting cycle, you feel my pain. Department heads spend hours gathering data, building projections, attending forecast meetings, revising numbers, and sitting in more forecast meetings.

Multiply that across every business unit, every department, every region, and you're looking at hundreds—maybe thousands—of person-hours per cycle. And most companies do this quarterly, monthly, or even more often.

It's not just Finance's time. It's everyone's time.

Our Ambitious Target

We're implementing AI-assisted forecasting right now with a pretty bold goal: cut forecasting cycle time by 50%.

That's not a random number—it's based on what Microsoft has reported from their own implementations. Are we there yet? Nope. We're still early in the rollout. But it gives us something concrete to shoot for.

Why Everyone Should Care

A 50% reduction in forecasting time doesn't just help Finance. It frees up every leader to actually think strategically instead of just compiling data. It means faster business decisions. It means your product team has more time to build, your sales team has more time to sell, and your operations team has more time to optimize.

This is what "company-wide impact" actually looks like. The time savings multiply across the entire organization.

Why Finance Makes Sense to Lead This

You might be wondering: why is Finance the right team to drive these initiatives?

A few reasons:

• We already own these processes. Forecasting, financial research, contract oversight—these already flow through Finance somehow.

• We speak ROI like a native language. Finance teams are built to measure impact. We can actually quantify savings and time reductions in ways that make business cases stick.

• We know everyone. Finance touches every department. We see pain points across the whole organization, not just in our corner.

• We want to enable growth. Modern Finance isn't just about controlling costs—it's about helping the business scale efficiently.

Put it all together, and Finance is actually well-positioned to pilot AI stuff that works for the whole company.

What We've Figured Out So Far

A few things we've learned:

• Don't overthink the measurement. We're not tracking time savings with some elaborate system. We're asking people for their estimates. And that's fine. It's enough to make smart decisions and show impact. Don't let "perfect measurement" stop you from moving forward.

• Go after bottlenecks that everyone feels. The best use cases aren't Finance-specific. They're organization-wide pain points that Finance just happens to be positioned to fix.

• Tell the growth story, not the cost story. Yeah, we're cutting costs. Yeah, we're creating efficiency. But the story that gets executive attention is: "This lets us grow without proportionally scaling resources." That's what gets the budget approved.

• Start with the safer bets. We didn't lead with forecasting transformation. We started with research questions and contract review—stuff where AI helps humans but doesn't replace judgment. Build some wins before tackling the complex stuff.

• Be real about where you are. The forecasting thing is a target, not a victory lap. Being honest about what's working and what's still in progress builds way more trust than overselling early results.

What's Coming Next

We're just getting started. These three use cases are the beginning, not the finish line.

What makes this exciting isn't really the technology. It's what becomes possible when you free up human time and brainpower from repetitive, time-consuming work. When your team spends less time compiling and more time thinking. Less time hunting for information and more time making decisions. Less time managing processes and more time enabling growth.

That's the promise. And based on what we're seeing so far, Finance is actually in a pretty unique position to deliver it—not just for ourselves, but for everyone.

The future of Finance isn't about replacing people. It's about multiplying what people can do. And that future's arriving a lot faster than most companies realize.

What's your Finance team doing with AI? What bottlenecks are you going after? I'd love to hear what's working—and what's not—in your world.