Leveraging AI isn’t just about investing the most time or money.
Artificial intelligence (AI) is growing in importance for finance, yet CFOs are still learning when and how to successfully deploy it.
Investing in AI and other digital initiatives can blunt the negative effects of economic pressures in the short term and build competitive advantage in the long term.
In finance, successful AI implementation requires the right leadership, talent, software investments and experimentation.
The use of AI is still an emerging priority for CFOs pushing the finance function further toward a digital future. Of those using AI, 75% report they started in just the past two years.
“The use of AI in finance departments is still nascent, and most early adopters rarely realize the anticipated returns from such projects,” says Alexander Bant, Chief of Research at Gartner. “Defining the use cases in finance is key — for digital initiatives in general and AI projects in particular. Ultimately, the goal is to drive your competitive position and prepare for an autonomous future, especially in today’s economy.”
Gartner research shows that leading AI deployers share four common behaviors that enable them to quickly meet or exceed the expected impact of their AI projects and deliver critical finance and business outcomes.
“Finance departments taking these four actions are finding twice the number of AI use cases, on average, compared to those who aren’t taking them,” says Bant. “This translates to more significant business outcomes such as new product lines, as well as finance department outcomes such as greater accuracy and shorter process times.”
There are three options for securing talent with AI skills and expertise: hire new talent, upskill current talent or borrow talent from the IT department. Organizations that focus their talent strategies on hiring outside AI-skilled employees are significantly more likely to become leading AI finance organizations.
AI-specific talent brings invaluable experience in working with the nuances of AI. This allows the organization to overcome inertia in working with AI applications and shortens the technical learning curve. While upskilling finance staff may be less expensive, doing so runs the risk of slowing progress and introducing greater potential for error. New AI-specific talent changes traditional processes and mindsets by bringing fresh ideas to support AI deployment.
No. 2: Invest in software with embedded AI for quick wins
Purchase software with embedded AI capabilities to experiment with AI and apply it to finance use cases to quickly build pilots for unique business problems. Building in-house AI solutions for all finance processes creates far more work and reduces bandwidth to explore new pilots or use cases.
Top finance AI organizations take a “fail fast” experimental approach to AI deployment rather than make a few big bets. With early pilots comes more uses of AI, and deployment is faster as you can zero in on the most successful pilots.
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 very few of the less successful ones.
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No. 4: Choose an analytical AI implementation leader
You must select the appropriate person to head AI deployment to realize the benefits. This could mean the head of financial planning and analysis (FP&A) or the head of finance analytics leading AI implementation rather than a controller.
Heads of FP&A and finance analytics are successful in leading AI due to their strong analytical and data backgrounds. They rely less on understanding traditional finance processes and more on understanding the complexities of AI in a business setting.