Best AI “Use Cases” for Corporate Finance

The artificial intelligence market may seem vast and expansive for corporate financial managers who are unfamiliar with the technology.

That’s no surprise given the massive size of the market. The global AI market was valued at $428 billion in 2022 and is expected to grow to just over $2 trillion by 2030. That’s a projected compound annual growth rate of 21.6%, according to Fortune Business Insights.

For some more clarity on the AI market and what it could mean for chief financial officers and the corporate finance sector, Stamford, Conn.-based Gartner, Inc. recently listed five of the best “use cases” for finance. In addition, it advises companies to make the listed case uses a top priority in late 2023 and 2024.

“Organizations ignoring these use cases should have a good reason for doing so because they offer the best combination of feasibility and business benefit,” says Mark D. McDonald, senior director, of research in the Gartner Financial 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.”

Here are the top use cases listed by Gartner. Note the company advises CFOs to “take into account the maturity and needs of their own finance organization because the applicability may vary across organizations and industries,” McDonald says. “These 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.”

Use Case #1 Revenue Forecasting

Gartner says corporate finance specialists can leverage AI-generated data models to make better decisions and optimize company dollars.

“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,” the company states.

Use Case #2 Anomaly and Error Detection

According to Gartner, 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,” the company adds.

Use Case #3 Decision Support

Here, machine learning prediction algorithms designed to predict outcomes based on current data “can be used to predict outcomes when alternative data values are used,” Gartner notes.

“Using models with hypothetical data predicts the result of alternate decisions,” Gartner adds.

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