QA Cory Microsoft Finance AI Exceltrentmann


Qa cory microsoft finance ai exceltrentmann term suggest that the use of artificial intelligence (AI), bots, and other technologies in its finance department has allowed them to maintain a constant headcount.

In a QA session Cory who leads Microsoft modern finance project, said that though AI is better and it helps automate tasks but we do love excel as it is special and will be used for next 1 decade!

QA Cory Microsoft Finance AI Exceltrentmann – The Wall Street Journal Interview

Although Microsoft Corp.’s activities, profit, and market capitalization have all increased dramatically in recent years, the number of individuals employed in the finance department has remained relatively stable at around 5,000. At the conclusion of its fiscal year in June, Microsoft employed 181,000 employees, up from roughly 163,000 a year earlier.

Chief Financial Officer Amy Hood has been successful in limiting the number of finance employees by making use of cutting-edge technology such as AI, bots, the cloud, data lakes, and machine learning. WSJ’s CFO Journal spoke with Cory Hrncirik, a member of Ms. Hood’s team and the leader of Microsoft’s Modern Finance effort, about the new tools and why the company still relies on Excel for some activities.

This article is the first in a series that will examine the role of the chief financial officer (CFO) and other executives in the transition to digital finance. Excerpts have been condensed for readability.

WSJ: When did Microsoft start its digital transformation?

Mr. Hrncirik : We made the switch to cloud storage about eight years ago. Future-planning and trying to figure out where your team is going is something you must deal with. Strategy and foresight are the terms used for this. We consider the time-consuming manual processes we must endure and consider methods to automate them. We put a premium on centralising our data and developing a single truth.

WSJ: “What’s in it for finance workers?”

Sir Hrncirik: To expedite and simplify the job that our people undertake, we want to use technology where it is appropriate. We want them to put their efforts into things like negotiating with business partners, exploring greenfield opportunities, and managing difficult projects, all of which are areas where technology has yet to prove itself useful.

WSJ: What percentage of your business is dependent on AI?

Sir Hrncirik: The area of forecasting was where we initially started using machine learning. Every business or organisation with a finance department conducts periodic forecasts. The majority of people need a lot of time to complete this. For most people, including ourselves, this entails a lot of grunt work in Excel. To put that in context, we used to spend around three weeks each quarter producing a projection, during which time we would involve one thousand individuals across all of our subsidiaries and product teams in creating Excel spreadsheets. The next step is to have those projections bubble up to the CFO.

Our variation rate was reduced from roughly 3% to 1.5% when we implemented machine learning in 2015, and within two quarters we recognised that our algorithms were doing as well as the human-based method. These models can be flipped around in as little as half an hour at this point.

WSJ: This is indeed the forecast for the upcoming quarter. What used to take three weeks now takes maybe half an hour?

Sir Hrncirik: You’re absolutely right. Then, we broadcast the information to our employees in every one of our divisions. They should nevertheless give them a go because of the insider information they may provide on the local market. We want to change some of the seasonality or the split between different products, etc., they remark, but the total figure seems excellent. At the most detailed levels of analysis, machine learning is not always effective.

WSJ: Are there other use cases?

Sir Hrncirik: We have widened its application by using it for things like compliance. It was put to use to hasten our in-house auditing procedures. That is why we use it to foretell economic downturns. The Treasury Department utilises it to assess global government papers for potential threats. We utilise it to determine which invoices can be processed mechanically and which ones require human attention.

WSJ: What needed to change for that?

Sir Hrncirik: When I first started out in the workforce, I [had to] link to fifty separate data sources in order to get information into Excel, and then manually generate insights from that data. More than a hundred distinct data sources have been relocated. We’ve combined them all in one place, a “data lake,” where cloud storage makes it easy to access and work with all of the information at once. Secondly, standardised reports and analytical frameworks need to be developed so that people from different parts of the world can have meaningful conversations about the same business.

WSJ: Are you using bots?

Sir Hrncirik: Our first step into the realm of AI was the creation of virtual agents. Through natural language processing, this AI is able to not only comprehend what is being said (in more than 60 languages, by the way), but also to infer what is being meant and to organise portions of a discussion into a coherent thread. Virtual agents now handle about 30 percent of all [internal] queries, or about one million requests each day.

WSJ:Where are you putting these bots to work?

Specifically, Mr. Hrncirik, [for instance] in the realm of invoice payments. A large percentage of the thousands of invoices we process each month come from the same few hundred vendors. After doing so, we determined that automation of approximately 70% of invoicing was possible. A machine-learning system we created was used to identify and compensate the missing 70%.

The programme also alerts the user, “Hey, by the way, we’ve found an irregularity or abnormality in this region, or in this particular SKU or category of goods.”

WSJ: How precise is the technology?

Sir Hrncirik: Our previously average 2% mistake rate is now under 1%. It’s so precise because no human beings are responsible for entering data into the system or performing any of the computations or other processes involved.

WSJ: Will you be able to reduce the number of financial employees?

Sir Hrncirik: The size of Microsoft’s finance department grew in tandem with the company itself, as is typical of such departments. As we expanded our business in the 1970s, 1980s, and 1990s, we hired more employees.

We were prompted to take action by the Great Recession of 2008–2009. At Microsoft, we’ve decided to maintain the same number of [financial] employees. After ten years, we can say that we’ve accomplished that goal. Our income has [almost] tripled over that same period. Despite the increased complexity of the business and a market cap that is now over $2 trillion, we have kept [approximately] the same number of individuals [in finance].

WSJ: Is the finance organization still using Excel?

Sir Hrncirik: Excel is a fantastic programme, and we use it frequently. In some contexts, Excel is indispensable, and that’s the case even today.

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