With 80% of U.S. chief financial officers stating they already operate or will soon operate automation on the job in the form of AI, machine learning, and data analysis, CFOs are starting to focus on the best use cases for automated technologies.
CFOs are using automation tools for high-profile company channels like accounting, sales, e-commerce, payrolls, and “customer facing” responsibilities like self-checkout, according to a recent study by the finance and technology advisory firm Gross Mendelsohn.
“For finance teams, automation is a godsend for productivity, governance, efficiency, and accuracy,” the company notes. “ In turn, it frees up the resources needed to improve strategic functions related to data analysis and financial planning and analysis (FP&A). As these technologies become the norm, lacking them will become a strategic disadvantage.”
Now, industry analysts are also looking closely at the best possible uses for AI and automation in the corporate finance realm.
Take the business analytical firm Gartner, which is out with a new study on “the best uses for AI in corporate finance.”
“Organizations ignoring these use cases should have a good reason for doing so because they offer the best combination of feasibility and business benefit,” said Mark D. McDonald, senior director of research in the Gartner Finance practice. “Looking to apply AI to other use cases before getting these five working effectively is likely leaving process efficiency and business performance gains on the table.”
One word of caution: McDonald advises companies that use cases “are commonly implemented and effective, but the most valuable use cases exploit a company’s unique strengths and allow it to further differentiate itself.”
“Top Five” AI Use Cases
Here are the five “best AI cases” as laid out by Gartner, which recommends that all CFOs consider using them.
Demand / Revenue Forecasting: Using both internal and external sources of data, models “predict demand and associated revenue across a variety of dimensions including business unit, product line, SKU, customer type and region,” Gartner says.
Anomaly and Error Detection: Anomaly detection uses a series of machine learning (ML) models to highlight transactions or balances that are in error or potentially violate accounting principles or policies.
“A comprehensive solution will also include real-time analysis during data entry preventing errors from entering the workflow and avoiding costly downstream corrections,” McDonald notes.
Decision Support: ML prediction algorithms designed to predict outcomes based on current data are used to predict outcomes when alternative data values are used.
“Using models with hypothetical data predicts the result of alternate decisions,” Gartner says.
POC Revenue Forecasting: Or POC accounting, ML models forecast the percentage-of-completion metrics (e.g., hours, cost, units, weight, etc.) “to predict POC revenue and the total completion effort remaining,” the study states.
Cash Collections: ML models are used to forecast when customers will pay invoices triggering proactive collection efforts before payments are past due.
“Using the predictions from these models, collections staff focus their efforts on at-risk accounts. Forecast cash collections also contribute to overall ML-driven cashflow forecasting,” McDonald adds.
Start with Forecasting
All of the above are top-notch ways to immediately put AI tools to use in the finance department. Unsurprisingly, some of those use cases are more equal than others.
“Forecasting is a popular use case in finance departments because legacy processes are manually intensive and notoriously unreliable. AI excels at automation and improving accuracy.” McDonald says. “Many pre-configured software packages address common finance processes such as accounts receivable and accounts payable but be aware that use cases which address unique business needs, such as forecasting, will require some internal skills to build.”
Brian O’Connell, a former Wall Street bond trader and best-selling author, is a prominent figure in the finance industry. With a substantial background as an ex-Wall Street trader, he has authored two best-selling books: ‘The 401k Millionaire’ and ‘CNBC’s Creating Wealth’, demonstrating his profound knowledge of finance and investing.
Brian is also a finance and business writer for esteemed national platforms and publications, including CNN, TheStreet.com, CBS News, The Wall Street Journal, U.S. News & World Report, Forbes, and Fox News.
