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Is it difficult to implement financial AI? Gartner: Four steps to triple AI use cases
Is it difficult to implement financial AI? Gartner: Four steps to triple AI use cases
Getting the right use of artificial intelligence in finance isn’t just a matter of investing the most time or money.
According to Gartner research, four implementation behaviors are critical in quickly realizing some financial artificial intelligence (AI) plans, including plans to meet or exceed expected effects and achieve key financial and business results.
Jacob Joseph-David, research director of Gartner’s finance practice, said, “The use of artificial intelligence in finance departments is still in its infancy, with most people only starting to use it in the past two years. Most people have also failed to achieve this quickly. Expected returns from similar projects.”
With artificial intelligence in its infancy in finance, CFOs lack a clear definition and strategy for success. Gartner identifies four key actions for CFOs to succeed in financial artificial intelligence (see figure below).
Joseph-David said, “Departments that took these four actions had twice the average number of AI use cases compared to departments that did not take these actions. The result was more significant business outcomes, Such as new product lines and financial department results such as greater accuracy and shorter process times."
Four actions to drive financial AI success ( Source. Gartner, June 2022)
Gartner: Four actions to drive success with financial AI
Hire external AI expertise
In general, it’s important to There are three options for securing talent with AI skills and expertise: hire new talent, upskill existing talent, or borrow talent from the IT department. Organizations that focus their talent strategies on hiring external people with AI skills are significantly more likely to become leading AI finance organizations. Yet about half of financial organizations see upskilling as a primary talent strategy.
AI professionals can provide valuable experience in handling the nuances of AI, which can enable organizations to overcome the inertia of working with AI applications and shorten the technology learning curve. Conversely, upskilling finance staff, while potentially cheaper, risks slowing down progress and potentially introducing large potential errors. Additionally, new AI professionals can provide opportunities to move beyond traditional processes and ways of thinking in terms of supporting new ideas for AI deployment.
Invest in embedded artificial intelligence software to achieve quick profits
Some companies use the method of purchasing embedded artificial intelligence function software. These businesses can more easily experiment with AI and apply it to more financial use cases. These use cases also make it easier to launch pilot projects for unique business problems. In contrast, building an in-house AI solution for all finance processes would create more work and reduce the opportunity for finance to explore new pilots or use cases.
Carry out pilot projects early and widely
The top financial artificial intelligence organizations adopt an experimental approach to artificial intelligence deployment by trying many times without fear of failure, rather than making big bets. With more early-stage pilot projects, there will be more use cases for AI and faster deployment because organizations can focus on the most successful pilot projects.
Typically, the most successful organizations are still exploring the same use cases as the less successful organizations, with the three most common use cases being accounting processes, back-office processing, and cash flow forecasting. One exception is customer payment forecasting, where approximately half of leading organizations’ explored use cases include customer payment forecasting, but less successful organizations rarely touch on customer payment forecasting.
Selecting Analytics AI Implementation Leaders
CFOs must select the right people responsible for AI deployment to realize the benefits of AI. For example, this might mean selecting the head of financial planning and analysis (FP&A) or the head of financial analysis to lead the implementation of AI, rather than selecting a senior executive at the top.
The Director of Financial Planning and Analysis and Financial Analysis’ success in leading artificial intelligence is due to their strong background in analytics and data. They rely less on an understanding of traditional financial processes and more on an understanding of the complexities of AI in a business environment.
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