AI Built For Finance

Hi, everyone. Welcome to Finance AI Academy powered by OneStream. My name is Tiffany Ma and today we'll be talking to Pam McIntyre about AI built for finance. Thanks so much, Pam, for being here with us today. Thanks, Tiffany. Happy to be here. My name is Pam McIntyre. I'm the Senior Vice President of Finance and Corporate Control at OneStream. So, Pam, AI is everywhere today and there's certainly no shortage of AI tools available for corporate finance teams.

Tell me, what makes OneStream's approach different from others? We've always talked at OneStream about being purpose-built for finance by finance.

The reason I joined OneStream was to be part of the future of what finance people can do and the contribution we can make to companies and performance. And AI is just part of the next step. It's what finance people need to use and leverage to be smarter, faster and better at what we do today, be able to contribute more to the business. What exactly do you mean by purpose-built for finance?

When I talk about purpose-built for finance, there's workflows, there's data, there's tasks that our team has to use on a regular basis to manage the processes. And the AI tools that we've developed have specifically assisted with those. They understand the structures. They understand the reporting needs. They understand the accounts and the reports that exist. The agents and the system can understand how to run through the details, how to look into the different reports and find answers for an end user without the person having to actually drive into that data.

When we talk about different tools, it understands the workflow, it understands the reporting, it understands all of the data that already exists within the system.

It's structures, it's consolidation, it's planning versus actuals, it's forecast scenarios. All of that, the agents and the AI tools that are built in our OneStream platform are literally using everything that exists within it, leveraging them to be able to do the work faster for your end users. You mentioned transparency.

Can you explain what you mean by that and how you as finance can get comfortable with the results that come out of the agents? When the agent comes back with a reply, it not only comes back with the answer, but how it came up with the answer, all of the steps it took to actually find the data, to do the calculations, and actually links to how you actually calculate those numbers or where the data came from, the cubes, the reports, etc.

It even extends to our sensible AI forecast as well. When you build a forecast using our sensible AI, it builds the calculations, the assumptions that were taken, and you can accept them and basically import them to your forecast as well.

It's not just a background calculation that gives you a number and you have no idea how it was developed. It truly gives you the breakdown, just like you would audit work papers to come up with the answer or you'd have to show your work back in school. The agent or the machine learning model is actually giving you all of that background, letting you give feedback on whether it did a good job.

Pam, if you could tell us a little bit about how OneStream's accounting and finance team is leveraging AI in your internal financial processes.

Our accounting and finance team, when we were actually partnering with the AI team, we actually went through and had a broad spectrum of people that were part of the process.

It was super experienced, very technical people on our team to a brand new hire that had just joined the company and had never, ever logged into OneStream. We gave them both the same tasks and over a couple of weeks, we were able to see that both teams were able to find the same results, to do the same analysis and expedite how quickly they could learn OneStream.

There's basically data within the system that are training tools, learning how to do administrative tasks, learning how to build different things within the system. And our brand new employee was able to ask the questions, find the data, build the things that he needed to do to do the work and find the same results. It was truly amazing to see how quickly with zero training, zero onboarding, that he was able to learn the system and start, using the system on a day-to-day basis.

So Pam, what's on the horizon for the future? What would be Pam McIntyre's dream use case? The future of AI and where it will continue adding value is really understanding the business as it's happening instead of after the fact.

Looking at things on a daily, monthly, weekly, all of that data that we're really understanding and digesting on a daily basis will help AI and help us understand anomalies and what's going on in the business.

From on time deliveries or quality issues or things like that, that rather than seeing the results after month end, if we build these things into the data that we're digesting on a regular basis, that will truly add value to the business, to shareholders, to cut losses earlier, to identify issues and respond to them very quickly and help us forecast them into the future as well with machine learning and AI analytics. Those tools will continue evolving and we'll be adding value and adding routines to our business.

But really getting rid of that manual work that teams do, that's already in process. The future will be automating cash forecasting, automating the processes, understanding the business.

And that will be really exciting to see how finance evolves from being the record keeper that we historically saw it being, to becoming truly part of the business, which is where we're at today, to being part of the forecasting strategy into the future. Pam, thanks so much for joining us today on Finance AI Academy, powered by OneStream. Thanks so much, Tiffany. Appreciate you having me. Thank you.