The Power of AI and ML for Tech and Media CFOs

虽然它看起来像其他新闻标题these days gives ChatGPT or AI top billing, business leaders have been looking for ways to harness the power of artificial intelligence (AI) and machine learning (ML) for quite a long time. And for leaders in the technology and media industries, finding the right opportunities to leverageAI and MLcould make the difference between their business’s success and failure.

According to an October 2022Deloitte survey, 72% of tech, media, and telecom leaders said they strongly believe AI will be very important to their ability to be competitive over the next five years. But it’s not an easy path: According to arecent Workday survey, only 21% of tech leaders said they were confident in their ability to make data-informed decisions in real time.

Justin Joseph, senior director of industry product strategy at Workday, put it this way: “AI- and ML-driven insights are so powerful because technology companies are sitting on the greatest amount of data out there. They just haven’t been able to tap into it.” And leaders aren’t always confident in their data. According to Workday’srecently released AI IQ survey, 77% of respondents are concerned about the timeliness or reliability of the underlying data.

While applications using AI and ML span across many areas of the business, the office of the CFO at tech and media companies is turning a particularly close eye toAI and ML. Let’s look at a few examples of where finance leaders could put this technology into action.

Putting AI and ML to Work

First, AI and ML can impact thequote-to-cashprocess. While this term isn’t commonly used outside the world of finance, it’s an omnipresent process in our daily lives. Simply put, quote-to-cash is the end-to-end sales process, the journey from prospect to customer. It could be as complex as a large company purchasing new financial software, or as simple as a consumer paying for an in-app purchase that helps them beat the next level of their favorite game.

Tech and media companies have a lot riding on improving the quote-to-cash process. If they can build in efficiencies, they can take someone from prospect to customer that much faster. And here’s where AI and ML come into play. If tech and media companies can find ways for AI and ML to automate, for example, 80% of the process involved in quote-to-cash, they can focus their strategic energy on the other 20%.

For Workday customerBlue Yonder, AI and ML make a huge difference in the process of customer payment matching (also known as invoice matching). In this process, invoices are cross-referenced to supporting documents to make sure that vendor payments are made correctly.

A senior accounting specialist at Blue Yonder shared this: “When you’re working through payments and invoice matching, it can take anywhere from an hour to all day to complete. With the help of machine learning, the customer payment matching capability in Workday picked up the three invoices for a payment missing remittance information. I was really impressed by that. Instead of spending time investigating and researching other payments, I was able to apply the recommendations right away.”

Anomalies. Now there’s a word that makes finance leaders shiver. Anomalies are no friend to finance. No CFO wants surprises in their processes (closing the books) or financial results (such as revenue, billing, spend, and payments). So for finance leaders in tech and media, anomaly detection with AI and ML (such as the capabilities built intoWorkday Financial Management) makes it possible to detect and fix costly errors before they happen. And while you’re at it, add efficiencies across the entire process.

另一个词,可能会在同一个类别as anomalies: audits. While the IRS may come to mind for most individuals when they hear that word, for finance leaders the power of AI and ML come into play for a proactive approach, or as some may call it, always-on audits. What does this mean? The idea is simple yet potentially game-changing: Instead of only running audits on a periodic basis, using AI and ML to constantly monitor your finances makes it possible to stay abreast of what’s happening on a real-time basis. As a result, risk mitigation is much easier and process, control, and governance are being met continuously—and much more accurately than if humans had the final say.

And finally, another area finance leaders have to be on the lookout for is rogue spend. In the current tech and media economic environment, the pressure is on CFOs to tighten their overall spend and improve their policies and controls. They don’t want to discover money spent on something that’s not providing value or spend that’s categorized incorrectly, leading to budget hassles. This is another key area where AI and ML can help tech and media finance leaders, as it can be used to constantly scan systems to look for spend that may be miscategorized. Efficiency gained, errors reduced—that can help alleviate the pressure that tech and media CFOs are under.

One important caveat to all this: Without the right enterprise cloud management system in place, even the most cutting-edge AI and ML solutions risk falling short. Tech and media finance leaders must make sure that, like Workday, their systems provide a single source of cloud data so that any applications ofAI and MLare accurate and effective. Workday provides a massive amount of data to work from. The bigger the data set, the better the results, and the more AI and ML are able to adapt and learn. So forfinance leaders, Workday’s inherent structure and plenteous data makes the goal of a zero-day close that much more achievable.

With clear eyes to see through the hype and a solid cloud foundation in place, finance leaders in tech and media should continue asking themselves how they can apply the power of AI and ML to improve efficiency, reduce errors, and keep real-time tabs on the holistic financial picture.