Welcome to the AI Finance era

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

Today we'll be talking to Scott Leshinski about the finance AI era. Thank you, Scott, for being here with us today.

Great to be with you, Tiffany, and great to be with everyone today. My name is Scott Leshinski. I am the Executive Vice President of AI and Operational Analytics at OneStream Software.

So, Scott, we've all seen the transformative power of AI in finance. How it's made finance teams move faster, act smarter, and lead with confidence.

So what are the key challenges that you're seeing facing finance teams in this current year and the coming years ahead?

We're living through one of the most significant transformations in the Office of Finance. And in terms of the challenges, you know, for decades finance leaders have been conditioned to accept various trade-offs, the trade-offs between speed at which they operate processes, and the accuracy that those processes are able to deliver. Between the granularity of analysis and the time required to perform that analysis, between black box models versus transparent insights, and between specialized tools that ultimately require very sparse resources and expertise.

To platforms that really democratize knowledge within the business. And what's exciting now is with AI, we're eliminating those trade-offs and those legacy challenges. So let's double click into how AI applies to finance. What capabilities does AI bring to the table at finance? And what are the potential business benefits?

Yeah, so, earlier I spoke about some of the trade-offs that the Office of Finance had been conditioned to accept.

And I would start off by hitting on, we're seeing, particularly in the Office of Finance, business processes that once took days or weeks of manual effort to perform.

So things like planning, forecasting, reconciliations, data analysis. Now being able to be performed in hours— and sometimes minutes—within the business.

As an example, we've had the privilege to partner and deliver over 120 customer projects leveraging our AI solutions.

And just to give some illustrative data points, if you look at the imperative around how do we improve things like forecast accuracy?

We've seen our customers now, on average, improve their forecast accuracy by 24%. We've seen our customers, on average, reduce the amount of time required to generate the plan or forecast by 86%.

But this isn't just driving efficiency or accuracy for the sake of accuracy. It's the derivative business value that's unlocked by those capabilities.

One of our customers in the manufacturing space has been able to reduce the amount of time generating the forecast by 94%.

And ultimately compress their forecast availability from what historically was workday ten, now down to workday two. Which is allowing them, particularly in this era of volatility, to generate a new forecast on a more frequent basis.

The most common question that we often get is how to get started with AI. So, Scott, can you tell us a little bit about what you think are critical success factors for finance teams to successfully adopt AI and really embrace the finance AI era?

Yeah, of course, Tiffany. Number one is starting off with a clear business use case. So when we think about AI, what's really important here is value-driven adoption.

Having clarity on what is the business problem, the business use case that you want to leverage AI to tackle, and how do we think about the outcome and the success factors that we want to use to measure the outcome?

Incredibly important. Number two is really around transparency. So, we can't emphasize enough how important transparency is.

There has to be a high level of transparency and explainability around the results that are coming out of this.

Number three, what I would hit on is around data. Now data quality is going to be a consistent discussion point in an AI-related initiative.

However, there are new capabilities here leveraging AI and ML that can help to address some of the historic challenges with data quality.

Two other factors that I would highlight are executive sponsorship. It's incredibly important if you're thinking about deploying these capabilities at scale within the business to have the right level of executive sponsorship, whether it's from finance, whether it's from the business systems or IT executive sponsorship is incredibly important.

And then the last piece, particularly in this day and age, is the aspect around upscaling the talent to make sure that they're prepared to effectively leverage these new AI-powered solutions. Thank you so much, Scott, for sharing your insights and experiences with us today. Tiffany, it was great to be with you and great to be with everyone today. We couldn't be more excited about where we are in this finance AI journey, and the opportunity that lies ahead. So, we look forward to continued discussion.