When I meet with other CFOs, I often hear a recurring concern: We already feel behind on AI. And when the primary question finance leaders are facing has shifted from “What’s your AI strategy?” to “What’s the return?” seemingly overnight, it’s no wonder this sentiment is so widespread.
But here’s the truth. The only leaders who are behind are the ones waiting for the perfect conditions to get started
Forward momentum is critical because I believe the finance function is at an inflection point. We have the opportunity to become the proving ground for Enterprise AI because we touch every part of the organization. The operating model, cost structure, planning cycle and performance data all run through us, and our peers trust us that data we produce is always accurate.
If we can rebuild finance around AI while preserving that trust, we can establish a model the rest of the enterprise can follow. Here are three ways leaders can bring this to life.
Get the data foundation right
First and foremost, you need a data strategy before you can have an AI strategy.
A model can't settle definitions your company hasn't agreed on. If revenue means six different things across six systems, AI won’t fix the underlying problem. The foundation must be governed, contextualized data that is accessible where it lives. In a world where everyone has access to the latest models, your customer history, operational knowledge, and institutional context are assets no competitor can replicate.
Every output also needs a path back to its source. When someone asks, "Where did that number come from?" there should be a clear answer.
A strong data foundation sets the stage for everything else.
Redesign one workflow before you measure the return
The first instinct with AI is to make manual tasks faster. But the real leap comes from reimagining the work altogether.
For example, a deal desk can become a hub where agents bring together work that used to pass between legal, billing, and FP&A, while people stay in the loop to make the judgement calls.
At Snowflake, our data science team uses machine learning to forecast revenue every day, so leadership can see where the quarter stands at any point. At quarter-end, automated financial metrics give the board an early read on day one, with the formal close and audit to follow.
What changes is where our team spends their energy. Instead of spending 80% of our time gathering data and 20% on analysis, we can flip that ratio and help the organization make faster, more informed decisions.
Start with one process your team repeats. Reimagine it, show what's possible, and let what you learn guide the next investment. Treat early experiments as innovation. The goal isn’t “tokenmaxxing” but rather intelligence efficiency. That means measuring how effectively you convert compute, models, data, and context into meaningful results. The organizations that win won't be the ones with the most deployments. They'll be the ones with the most thoughtful approach to these components.
Build a team that can carry the change
You can't license leadership. Buying technology doesn't change how people think, work or make decisions. This is a human transformation more than a technology one, and it starts at the top.
A few power users won’t transform a finance organization. Broad adoption requires training, show-and-tells where people can share what they’ve built, and time to experiment. It also takes leaders who admit some ideas won't work.
At Snowflake, we've rewritten job descriptions and added AI assessments to our interviews. But hiring isn’t enough. We also have to help the people already on our teams learn to use AI well and to push back on what it produces.
I’ve started carving out additional one-on-one time each week with my team just to talk about how they’re leveraging AI. My role is to listen, connect dots and help remove roadblocks, and I expect my leaders to do the same.
When you focus on people first and give them the permission to experiment, you will find great ideas come from all levels. That’s how you know you’re on the right track.
As the pace of innovation to scale, finance is becoming the proving ground for the Agentic Enterprise, where data, AI and people work together to run the business. The CFO's leading the way ensure they have the right data foundation in place to make room for innovation and focus on enabling their people every step of the way. The key is getting started.
