Human vs AI: For CFOs, it should be humans AND AI

Presented by Planful

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When done well, business technology enhances and elevates work, yet leaves the worker in control. Good technology is a superpower, helping people to do, see and understand more without requiring extensive training or complex interfaces. For the Office of the CFO, dealing with constantly shifting economic conditions, a faster pace of business, and an overwhelming flood of data, the need for such enabling technologies has never been greater.

However, when it comes to the flood of data, finance and accounting teams can’t be expected to gain the skills of a data scientist and work endless hours to capture, analyze and pull value quickly from more and more data. You need better tools to empower them to move fast. There are proven technologies that can help today: artificial intelligence and machine learning (AI/ML). These capabilities need to be applied in a way that considers the people in finance and accounting.

People-first technology

Technology is extremely powerful and novel, accomplishing amazing tasks humans may be incapable of doing at speeds never before possible. But some technologies provide solutions people might not want or that don’t fit into existing processes (e.g. Google Glass, 3D television, Windows Phone, etc.). To be successful, no matter how cutting-edge, technology must solve a problem and people must want to use it.

Translating that to the Office of the CFO, finance leaders want technology that helps them do their jobs with more speed and accuracy. It should support existing finance and accounting workflows, enhance their years of training and expertise, show tangible value and be something they want to use.

Consider the ubiquitous capabilities that check your spelling and grammar across devices. You know intuitively how it works, can ignore it if you like, and can control how and where it adds value. AI/ML highlights the words and phrases it thinks are amiss and then you decide. You don’t need to be a language expert, a spelling bee champ, or have a Modern Language Association (MLA) handbook on your desk. The technology does the work to help you avoid errors, and it does so seamlessly, and contextually without adding time, and you remain in control.

If you’re old enough to remember the speech-to-text technology from the late 90s, you’ll also remember that it failed miserably. It promised a wonderful future where keyboards would be replaced with microphones for voice-controlled computers and faster, easier content creation. But the developers failed to take one important aspect into account: people don’t want to talk to their computers. That’s why you’ve rarely heard a coworker drafting a report by voice, even though it’s been possible for over 20 years.

You don’t have to be a data scientist

Software developers need to understand their audience and how they work, identify their challenges and develop a solution for the people actually doing the work. A challenge we’ve all been faced with is the unrelenting growth of data. We’re generating more of it, from more tools, and it’s arriving at a faster pace. There’s no way workers can sift through all of this data and learn how to be a data scientist to find anomalies, opportunities and statistically relevant trends within myriad datasets. But AI/ML can help people in finance and accounting take advantage of their data in new, advanced ways and without requiring them to become data scientists.

Even the smallest business is faced with an overwhelming number of financial data sources, from ERP to billing to payments to CRM platforms and more, all critical to the primary functions for the Office of the CFO. When you also consider non-financial data points for clicks, pipelines and quotes from a CRM system, the possibilities may feel daunting. As data volumes grow, spreadsheets and other legacy finance technologies add even more friction to already slow manual processes. They also force you to summarize data and consequently overlook potentially critical details. Those older technologies also scare away the bright, in-demand digital natives companies are so desperate to recruit and retain.

But you don’t need to be a data scientist or work every weekend to absorb and process the flood of data. AI/ML can evaluate the data much faster, make sense of it, spot errors and outliers, and highlight them for you, especially if, like spell check, it’s built into the tools you’re already using.

Technology can understand data faster than you

Gartner shows the AI/ML derivatives of computer vision, data labeling and annotation, and intelligent applications are well on their way up the “slope of enlightenment” to the promised land on the “plateau of productivity.” AI/ML technology already works, so maybe it’s the implementation that’s the problem?

Here’s what I’ve found: people want to be involved in the analysis and decision-making, especially when it comes to finance and accounting processes. Putting people directly into the loop is the subtle difference many software developers miss. For example, our Planful Predict: Signals AI/Ml product highlights outliers and anomalies so the user can determine if the indicators are relevant or not. It’s a helpful suggestion to dig deeper, not a mysterious, unknown change that you stumble upon later and are unable to explain.

Let’s not forget those digital natives, too. AI/ML are no longer emerging technologies; they’re part of the fabric of today’s technology landscape. If you want to hire and retain the brightest minds, they’ll want to be equipped with the latest tools.

Let technology enhance how people work

People, especially those managing the various functions within the Office of the CFO, are rightly averse to risk. In a spreadsheet, the data is always a couple of clicks away, even if it takes you hours to decipher formulas and dig through tabs to find it. That’s comforting, and allows them to feel in control. But deploying AI/ML doesn’t mean you have to give up control.

Using AI/ML as a helper rather than a doer is where you can accelerate finance and accounting processes while keeping people in control. That retains the benefits of AI/ML but implements it in a way that’s people-first. It lets the technology take on the slow, tedious work of scanning billions of data points for errors or compiling millions of rows to predict potential future scenarios. That time is then given back to the Office of the CFO so people can make better decisions, spend more time with the business, or perhaps get home in time for dinner.

The bottom line is that it’s the people who matter most. Taking into consideration their needs and then addressing those needs lets them control how they use their time. By understanding what your people need — time, accuracy, control — you can learn to love the AI/ML technologies that can give it to them.

To learn how AI/ML is transforming the Office of the CFO, read the ebook: It’s Not Too Late for CFOs to Get In the Artificial Intelligence Game.

Sanjay Vyas is Chief Technology Officer at Planful.

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