Finance departments get increasingly confident about AI

ARTIFICIAL intelligence (AI) has made significant progress over the past year or so. In fact, it’s made a mark in every department and division of a company, irrespective of the industry it is deployed in.

Companies like General Electric, Boeing, and Google use AI for internal applications as much as they do for products they create for their customers.

According to a new Gartner study, finance teams are growing increasingly confident about AI and expect to use more of the technology in their day to day operations in the coming year.

“More than a quarter of organizations surveyed expect to deploy some form of artificial intelligence (AI) or machine learning in their finance department by 2020. Moreover, half the respondents expect to deploy predictive analytics in the same period,” said Gartner Senior Director Analyst Christopher Iervolino.

Finance professionals understand that deploying new-age technologies such as AI can help significantly reduce costs, improve control, and uncover fresh insights — all of which could help give the business a new competitive advantage.

According to Gartner, although interest in using AI in the finance department is rising, there’s not much that is being done to make it a reality.

The lack of working AI deployments is no big surprise, because the technology is not yet built into most FP&A application suites. There is tremendous potential for transformational improvement, but these capabilities are only just becoming mainstream so the deployment expectations we see in the survey may be unrealistic for many,” explained Iervolino.

Getting started with AI in finance

In the finance field, accuracy and compliance with procedures and regulations are critical. As a result, professionals are usually not very keen on adopting new tools and methods that could change how they work.

With AI, however, the finance department doesn’t have much to worry about. It helps avoid common and costly human errors and ensures people have more time to focus on what matter most.

For organizations just getting started with AI in the finance department, Gartner recommends the following steps:

Step 1: Examine current processes and tools

It’s important t find existing shortcomings that could be improved with a more data-intensive approach, using more operational data and more direct participation from lines of business.

Once found, it’s important to prototype proven vendor capabilities against these shortcomings.

Step 2: Expand financial analytics capabilities in existing solutions

Gartner believes it is important to look for underutilized capabilities the organization already has, such as data discovery, forecasting probability, correlation, and exception detection functions.

If needed, businesses should invest in analytics specialists or training to properly prototype these capabilities against known FP&A pain points.

Step 3: Pursue new AI opportunities

Once the finance organization has properly evaluated its existing tools, and built the expertise to use them, it will be in a strong position to build a business case to invest in an AI initiative, if the potential is identified.

Moreover, it may also demonstrate the need for the finance department’s involvement in existing AI initiatives.

“There is a tendency within finance organizations to approach FP&A improvements in a tactical way, characterized by a finance-siloed focus on areas such as workflow control, automating business calculations and consistency,” explained Iervolino.

“Leaders should consider that a strategic approach to FP&A will provide significantly more value by supporting key partnerships between finance and LOBs, by providing analytics and decision support, and through integrated financial planning and modeling.”

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