Gartner: Four steps to ensure financial AI applications are implemented correctly

• Artificial intelligence (AI) is increasingly important to finance, but CFOs are still learning when and how to successfully deploy them.
• Investing in artificial intelligence and other digital initiatives can blunt the negative effects of economic stress in the short term and build competitive advantage in the long term.
• In finance, successful AI implementation requires the right leadership, talent, software investment and experimentation.

The use of artificial intelligence remains an emerging priority for CFOs as they move their finance functions further into a digital future. Of those using AI, 75% said they started using it within the past two years.

"The use of artificial intelligence in finance is still in its infancy, and most early adopters have little awareness of the expected returns on such projects," said Alexander Bant, research director at Gartner. “Defining financial use cases is key – for digital initiatives in general and AI projects in particular. Ultimately, the goal is to improve your competitive position and prepare for an autonomous future, especially in today’s economy.”

Gartner research shows that leading AI deployers engage in four common behaviors that enable them to quickly meet or exceed the expected impact of their AI projects and deliver key financial and business results.

“On average, finance departments that took these four actions found twice as many AI use cases as departments that didn’t take these actions,” Bant said. "This translates into more important business results, such as new product lines, and financial department results, such as greater accuracy and shorter process times."

四Key actions to ensure the success of financial artificial intelligence:

1. Hire external AI professionals

There are three options to ensure talent with AI skills and expertise: hire new talent, upgrade existing talent skills or borrow talent from the IT department. Organizations that focus their talent strategies on recruiting external employees with AI skills are more likely to become leading AI finance organizations.

AI specific talent brings valuable experience in handling the nuances of AI. This enables organizations to overcome inertia in using AI

applications and shorten the technology learning curve. While upskilling finance staff may be less expensive, doing so may slow progress and introduce a greater potential for error. New AI professionals change traditional processes and ways of thinking by bringing new ideas to support AI deployments.

2. Invest in software with embedded AI capabilities for quick success

Buy software with embedded AI capabilities to experiment with AI and apply it to financing use cases to quickly build solutions for unique business problems of pilot. Building in-house AI solutions for all financial processes creates more work and less bandwidth to explore new pilots or use cases.

3. Experiment early and widely

Top financial AI organizations take a “quick trial and error” experimental approach to AI deployment rather than making a few big bets. Early leaders will use AI more and deploy it faster.

The three most common AI use cases are accounting processes, back-office processing, and cash flow forecasting. Customer payment forecasting is a use case explored by half of leading organizations, but rarely among less successful organizations.

4. Choose an analytical AI implementation leader

Enterprises must choose the right people to lead AI deployments to realize benefits. This might mean the head of financial planning and analysis (FP&A) or the head of financial analytics leading the AI ​​implementation, rather than the controller.

The FP&A and Financial Analytics leader has successfully led AI with his strong analytics and data background. They rely less on understanding traditional financial processes and more on understanding the complexities of AI in a business environment.

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