There’s a lot of noise around artificial intelligence, and CFOs are feeling the heat.

Three-quarters of finance leaders view digital transformation as a strategic priority, American Express found in a recent survey. This is driven by pressure to adopt innovative technologies such as AI in the finance function and beyond, according to the research.

At this point, the question isn’t whether CFOs need AI, but how long they can survive without it.

However, CFOs don’t need to fear AI itself. Contrary to some of the hype, it’s not coming for your job. Quite the opposite: it’s here to help you do your job faster and smarter. It can help you draft reports, alert you when actuals don’t match forecasts, and even flag potential risks. But the final decision is still up to you.

For the CFO who ignores AI, the real danger is the possibility of being replaced by a finance leader who has learned to embrace the technology.

Still, it’s important to understand that AI is not a magical tool. Its results very much depend on how effectively it’s used. Here are key strategies for unlocking AI’s true value in the finance function:

Lay the foundation before you jump in

AI isn’t a plug-and-play fix. You need to build a solid foundation for it to work.

First, clean up your financial data. It has to be organized, integrated and easy to access. Without that, AI will only add complexity instead of creating clarity. It’s like hiring a CFO and giving that person zero context; it won’t make much of an impact.

If your data is messy or scattered, you’re setting up a scenario where your AI tool will just spit out junk. But if your data is organized, AI becomes a powerful ally.

One key is making sure you’ve taken steps to reconcile discrepancies between your ERP system and subsidiary ledgers, standardize accounts across business units and consolidate data into a single source of truth.

Once that’s done, AI can really start to deliver. It can spot patterns you might not catch, reveal insights you might’ve missed, make forecasts more accurate, and help you make smarter, data-driven decisions.

But if you jump in too soon, before your data is ready, you’ll just end up with expensive, confusing outputs.

Be intentional with AI

Start small, but aim big. Try AI for cash flow forecasting, or revenue predictions, to see some quick wins.

Also, keep in mind that AI should enhance your processes, not replace your judgment. You should have a solid understanding of your business first, and then use AI to share that understanding with the rest of the team.

If your team relies too heavily on AI, or lets it think for them, they’ll lose the nuance that comes with human understanding. Make sure they know how to validate AI’s outputs and use it deliberately to improve their strategy.

AI tools are built on large language models, which means they’re trained on text, not numbers. Think of AI as having a kind of human-like intuition — it’s great at understanding and generating language, but not so great at math. That’s why it can make your financial models more accessible and understandable, but won’t actually replace them.

Make AI invisible (in a good way)

AI shouldn’t be something you need to “talk to” all the time. Most of us think of AI as something you interact with through chat interfaces like ChatGPT, which is fine but also very limited. 

You don’t want AI that only responds when prompted — you also want AI that quietly works in the background, helping you without disrupting your flow.

AI should be deeply integrated into your systems, giving you insights as you work, not after. The goal is for AI to feel like an extension of the way you already work as much as possible.

To get there, look for AI tools that connect deeply with your data sources — to get your full business context — and provide insights without interrupting your workflow.

Get the human element right

Set up an AI oversight team that ​​​​​includes people from Finance, IT, Legal and Risk to set clear policies for using AI, and keep everything aligned. Make sure that your own team is equipped with AI training. Help them understand what AI can and can’t do, so they can validate its outputs. Work closely with your CIO or CTO to make sure your tech stack supports your AI goals.