James Kelly from YourTreasury AI explains how AI tools cut manual work in cash forecasting and improve forecast quality. He covers practical applications of AI in finance and makes the case for why finance teams should adopt these tools now.
The one that springs to mind, ice is some work that we did on cash forecasting with the company. Now, this isn't the sort of classic we put in the machine learning tool and nobody had to do anything anymore thing. This was just we took the existing process and we redesigned everything so that effectively we replicated what were the business teams doing, were using spreadsheets, populate the spreadsheets that were coming in and then for the central team, like, how could they make sure that the spreadsheets were received on time? Automated chasers. So we took a process that was taking 40 hours a week across six finance staff and we took it down to three hours and the quality of the output was improved, the speed of everything obviously significantly improved. It wasn't a huge lift. We weren't having to go through and create brand new data sets or anything else. It was simply, how do we take what we've got and then incrementally build from there? So we've then gone on and we've gone, how do we improve the forecasting for the worst performers and all of that kind of stuff. We're now in a situation where most of the AI tools are good enough that you should be using them. If you're turning around and saying, I don't have time to find out how to use this, or this isn't important to me, you've got what's effectively a free tool sitting there that can drive significantly improved performance. There is a little bit of a call to action there because I think we are now at a point where there's a level of maturity that means that now's the time to get involved.
