The State of AI in Finance: Insights from The CFO Alliance Roundtable Series

To launch The CFO Alliance Q4 2023 Roundtable Series, CFO Alliance founder Nick Araco and First Water Finance CEO Ben Lehrer hosted Ranga Bodla of Oracle NetSuite for an interactive discussion with CFO Alliance members on the state of AI adoption in the finance function. The virtual roundtable event serves as the kickoff for quarterly events held around the country, spurring the dialogue that gains momentum as insights are gathered and shared as the discussion moves from city to city.

Member participants had a lot to add on this top-of-mind topic, sharing their experiences, successes, and pain points. Comments such as spending a year or more just to organize data for centralization and organizational consistency speak to the road ahead in finance. Other key takeaways from the virtual roundtable event are as follows:

We remain at early stages of the AI ‘revolution’

The discussion began with an acknowledgment of the relatively low adoption rate of AI within finance and accounting, with participants at only a 6% adoption rate within the finance function. Much of the conversation was rooted in the “data maturity model”, a phased framework used to assess an organization's data environment and related readiness for analytics and AI adoption. The five stages are Manual, Reactive, Descriptive, Proactive, and Transformed.

60% of participants categorized their organizations as operating at or between the Manual and Reactive stages of the data maturity model. Ranga offered up that this mirrored other data and related conversations, confirming this as the current norm in finance. Similar to how self-driving car technology exceeds the capabilities of what existing road infrastructure can handle, data (completeness, integrity, and centralization) and disparate systems are the great limiter in businesses being able to take advantage of what AI can do for finance teams.

Tools are outpacing time and teams (prohibiting readiness)

One of the key constraints identified in AI implementation was the lack of time to invest in research and implementation. 35% of participants identified time as the top constraint in making the next advancement within the data maturity model. With the potential of what AI can do most for finance teams (saving time, doing things faster), this is quite the conundrum, as lack of time is holding back AI advancements whose ROI is, in part, in getting that time back!
 

Additionally, 45% of participants identified team and stakeholder issues as their top hindrance. This includes shortage of horsepower, headcount, and capabilities, as well as challenges in gaining stakeholder buy-in on the value and ROI of AI applications.  The time-to-invest restriction reflects the ongoing undercapitalization of the CFO seat and the finance function. Our mid-year check-in report highlighted that teams were operating with at least one person down, and workloads were anticipated to increase. This remains the case, leaving very little time for finance teams to make time and capital investments in advancing the data maturity model and deploying AI.

Trust remains a limiting factor

What other factors are impeding participants' progress in AI adoption? Two top issues identified are data security and opaqueness in AI processes (a lack of transparency in understanding how AI algorithms work). These surpass concerns related to team capabilities, financial resources, data availability, and leadership knowledge/buy-in. Building trust in AI technologies, while ensuring data security, will occur through internal training, the addition of incremental skillsets, and due diligence. Ben emphasized, within First Water’s FP&A recruiting efforts, the team places value on coding experience, even if the languages don’t directly apply to existing processes. He stated, “We value finance professionals who have exposure to data architecture, extract-transform-load (ETL) processes, and/or visualization platforms, as the foundational knowledge and related 'data mindsets' make these professionals more adaptable to advancing AI readiness and the deployment of new tools.”

Conclusion

While AI adoption remains relatively low, the conversation emphasized the importance of addressing data readiness, investing in capable teams, and building trust to unlock the full potential of AI in finance. Overcoming constraints related to time, team capabilities, and stakeholder buy-in will require a joint commitment from leadership and finance teams to narrow the gap between tools and readiness.

There is minimal disagreement that AI applications can produce efficiencies for finance teams, yet for the majority there is much work to be done to effectively get out of the starting gate. As the conversation continues within The CFO Alliance, the collaborative exchange of ideas and insights serve as an invaluable resource for finance leaders to gain leading-edge knowledge and advance their organizations into an AI-enabled future.

Interested in joining other financial leaders in discussing their usage of AI? Join us and fellow CFOs at our next Roundtable Series on The State of AI in Finance event page.

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