Employ's about 5,000 people in its finance team, a number that has remained largely flat in recent years, even though the company’s operations, profit and market capitalization have grown tremendously. Microsoft had 181,000 employees at the end of June, when its fiscal year closed, up from around 163,000 a year before. The host of technologies, including artificial intelligence, bots, the cloud, data lakes and machine learning, are helping Chief Financial Officer Amy Hood keep a tight lid on finance head count. Cory Hrncirik, who works on Ms. Hood’s team and leads Microsoft’s Modern Finance initiative, told WSJ’s CFO Journal about the new tools, and why the organization still uses Excel for some tasks. The first part of a series that focuses on how CFOs and other executives digitize their finance operations. Edited excerpts follow.
Mr. Hrncirik: About seven or eight years [ago], we moved all of our data to the cloud. You have to deal with looking ahead and trying to understand the future of your team. We call that strategy and forecasting. We think about the manual tasks that we have to do, and we think about how we automate those. We focused a lot about streamlining our data, creating one source of truth. We want to use technology for areas where [it] is suited to streamline and simplify the work that our people do. We want them focusing on areas that, frankly, technology still can’t help us solve very well, like negotiating with business partners or looking for greenfield opportunities or managing complex projects. Our first foray into machine learning was in the forecasting arena. Forecasting is something that every finance group does, regardless of company or organization. For most, it takes a lot of time. For most, it’s a lot of heavy lifting in Excel, and it was for us as well. Just to put that in perspective, we typically would spend about three weeks every quarter building a forecast, and we would involve a thousand people in that process, creating Excel spreadsheets in all of our subsidiaries and in all of our product teams. And then bubbling those forecasts up until they reach the CFO.
We introduced machine learning back in 2015, and within two quarters we realized that our algorithms were not only performing as well as the human-based process, but we cut our variance rate in half from about 3% to 1.5%. [Now], we can actually turn those models around in about 30 minutes. We then push the insights out to our people around all of our subsidiaries. They still have a chance to look at them because they bring unique knowledge of local markets. They’ll often say, “Oh, the all-up number looks perfect,” but we want to adjust the seasonality or the split between different products or things like that. Machine learning doesn’t always perform really well at the deep, granular level.
We’ve branched out and employed [it] in things like compliance. We employed it in speeding up our internal audit process. We employ it in predicting recessions. We use it in our treasury group for analyzing documents from governments around the world to understand possible risks. We use it even to identify which invoices can be automated and which need human intervention.
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