An FP&A director for a local public medtech company, jokes about going to the beach with all the extra time he’ll save leveraging an AI/ML software like Precanto instead of relying solely on Excel formulas to run his models. 

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But there’s a small part of him that might be serious. 

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He’s an early tech adopter who is passionate about leveraging the power of artificial intelligence and machine learning to generate better data insights and achieve significant time savings for finance leaders. He believes this will speed up the forecasting process by having predictable headcount and personnel spend.

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“I've always liked to be as confident as possible to know that we can repurpose our investments if we have savings, or if we're running over, we need to pull back. And the traditional exercise takes a bit of time, because our traditional exercise relies on an accounting close, and looking at actuals, and updating and speaking to the business and updating forecasts,” he says. 

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“A machine learning tool should be able to give us a ballpark estimate that is almost like a confirmation of what we may already know, but provides another data set and another mathematical confirmation. Do we have either an opportunity to reinvest or an opportunity, or a need to pull back if we want to achieve our numbers?”

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Precanto is a compelling challenger model to predict headcount and personnel spend. 

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“I think of Precanto as a second set of eyes — another data point to look at how my modeling is working and what I should be improving upon.”

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Without AI, running the process from job requisition goes through HR, payroll and online or offline spreadsheets.  The movements of all those assumptions and reconciliations to be as precise as possible occurs on a monthly basis. 

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“It's a multifunctional part of our jobs to go through this process and make sure that we're all aligned on positions that have been filled or open, and that the information we have is accurate,” he says. 

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He’s long been interested in bringing on a predictive analytics or machine learning tool to help with the forecasting process.

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“A good team of intelligent people will point out differences, and we should identify and understand all the reasons why they think there's a difference,” he says. “And I think machine learning or predictive analytics says the same thing. It provides another data set using mathematical equations, which I think is very helpful.”

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“I could allocate 10 hours to download all the data into Excel and manage it and run it and do exactly what these algorithms and this machine learning should do fairly quickly for me, and also be able to generate it with a user interface that's much faster and easier. So it's a time-saving exercise, and also an accuracy exercise.” 

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Joking about the beach aside, who couldn’t use 10 extra hours to focus on strategy and guidance for the business? 

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“There's always a list of things to do,” he says. “And the more we leverage data, data management and analysis more effectively with tools, allows the team to elevate their learnings and also their decision making processes. 

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