5 Ways AI can help FP&A leaders navigate uncertainty

Welcome, everybody, to Finance AI Academy by OneStream. My name is Tiffany Ma, Global Director of Product Marketing for AI and Operational Analytics.

And today we'll be talking to Praz about AI and FP&A. Thanks so much, Praz, for being here. Thanks, Tiff, for having me. Hi, everyone. My name is Praz Chatterjee. I'm the Global Director of Product Marketing here at OneStream, focusing on planning and analysis. What are your opinions of your top ways of AI of how it can help FP&A leaders navigate market uncertainty?

Finance spends a lot of time right now generating forecasts after forecasts. And we all know that forecasts are obsolete from the time they're done. If you can deliver value through more accurate forecasts, that's clearly a first step forward. The next step is faster re-forecasting. Finance at this point in time is obviously involved in a lot of turbulence within the marketplace.

There's distractions with tariffs, supply chain issues, commodity prices going up and down, currency issues as well. And all these things come together. The business asks finance for faster re -forecasting as well as more accuracy.

So the key thing here is finance being able to use AI to leverage the speed through which they can take all that granular data and generate more faster forecasts. How do AI tools support more accurate forecasting? The advantage to more accurate forecasting is often forecasts, they rely on too much gut feel. They look at past trends or stale data. And, you know, bias and outdated assumptions creep in way too quickly. The advantage with AI is that it uses machine learning to scan your entire data set, both internal and external.

And they can identify subtle patterns that humans might miss. They can run algorithms in parallel as well and choose the best fit for your forecast each time. Ultimately, the result is that forecasts become more reliable with much less manual work. In uncertain markets, this really means that even a few percentage points of accuracy can mean millions of dollars saved. And more importantly as well, so much time better allocated. You mentioned more rapid forecasting.

Can you explain how AI tools support more rapid forecasting and scenario analysis? What we know about rapid forecasting and scenario analysis is that static forecasts that are usually generated, they are outdated immediately. By the time a CFO sees the numbers, the assumptions are already outdated. But what AI allows you to do is it continuously ingests new data.

It's updating forecasts dynamically as well. Finance teams with this, they can run what-if scenarios instantly without any wait. When the business comes to them, they don't say, I'll get back to you. They get back to the business in real time. So whether it's an interest rate spike, a supply chain disruption, or a demand spike, finance has the answers.

And the result is that business leaders, they don't have to wait for the next planning or forecasting cycle. They can pivot strategies in real time and help guide the business with confidence.

How are AI tools able to provide early warning signals? Great question, Tiff. So what happens is whether we call it early risk detection or anomaly detection, finance often spots risks way too late. You know, after margins are hit or revenue slips. And this is because there's too much data for them to consume. The advantage with AI is that AI models, they flag anomalies so much earlier.

Whether it's an unexpected dip in a product line or a sudden surge in the cost of logistics. AI can detect that data and those anomalies as well. And they're also able to show sensitivity. You know, what drivers matters the most and where the risk exposure really is. The result is, instead of being caught flat-footed, FP&A becomes the radar system for the business, spotting the storms before they hit. You mentioned how AI tools support enhanced collaboration as well. Can you explain how this works?

In uncertain times, like that we're living in right now, frequent forecast changes, they can really erode trust. And business leaders start doubting the value of finance and the numbers they're providing. The AI advantage is that AI models are explainable.

They show the why behind a number shift. For example, FX and seasonality might have accounted for 60% of a variance. And because forecasts are created within the same platform, used for reporting and planning and all of your data, everyone is working from the same version of the shoes.

The result is, there's greater trust, there's faster alignment, and better decisions across the enterprise, allowing finance to be a better business partner. So we talked a lot about the automation of creation of forecasts and the scenario model in the machine learning. So what we didn't talk a lot about was the why behind it, which is freeing up FP&A's time from those menial time-consuming tasks to the more strategic tasks.

Can you explain a little bit more about that? So the challenge is that there's way too much time wasted for finance and FP&A in pulling data from multiple sources, reconciling the numbers, and ultimately adjusting spreadsheets. The AI advantage is that it automates a large part of the forecasting process.

Instead of wrestling with the data and parsing it, analysts can focus on storytelling, insights, and really advising business leaders. The result is that finance evolves from number crunchers to true business partners, shaping the strategy and the decisions of the enterprise. Praz, thanks so much for joining us today on Finance AI Powered by OneStream. Thanks for sharing your insights and your knowledge and for being with us today. Thanks so much, Tiff, for having me here, and I'm looking forward to doing it again.