4 Questions About Embracing AI in Finance, Answered

  • By AFP Staff
  • Published: 4/29/2024

4 Questions About Embracing AI in Finance HeaderAs finance directs more of its attention to artificial intelligence, many professionals face big questions about how to get started — and how to capitalize on the steps they have already taken.

As part of the AFP FP&A series, AI-Powered Finance: What to Do Today, exclusively sponsored by OneStream, Justin Kuzma, FPAC, Sr. Director, FP&A, U.S. Steel, and Ashok Manthena, Founder, ChatFin, discussed how to successfully build an organization’s foundation for an AI program. They concluded that preparing your organization for AI requires financial leadership, dedicated funding and people-driven initiatives.

Below are answers to four questions that were asked during Kuzma and Manthena’s session, “Ready, Set, AI: Embracing AI in the Finance World.” Read more about their session in “5 Insights on AI-Powered FP&A.”

How should we think about our current projects — like new Enterprise Resource Planning (ERP) or Enterprise Performance Management (EPM) system — to prepare for future projects with predictive and/or generative AI?

Kuzma: The most crucial aspect is maintaining high-quality data integrity and robust metadata management. To guarantee the ERP captures pertinent future data, it’s essential to involve a group of diverse enterprise stakeholders, not just IT and finance, in the system design. Comprehensive upfront research is also advantageous, as some of these systems inherently integrate AI capabilities, which can lessen the time to formulate a separate AI strategy and diminish the overall total cost of ownership (TCO).

Can we target doing proof of concept (POC) with a limited data set, or should we invest our time in expanding the data to include all variables for AI to make a more accurate call?

Manthena: Starting a POC with a limited dataset can be an effective strategy, especially in predictive analytics. This approach allows you to assess the potential of your project without the initial heavy investment in data collection. As your POC evolves and you gain insights into its accuracy and the key drivers impacting your business, you can incrementally expand your dataset. This iterative process helps refine the model's accuracy over time by identifying and integrating more relevant variables.

How does the champion of AI within finance explore the best technology? Or is this exploration for the IT team?

Manthena: The finance champion plays a crucial role in identifying the specific challenges and needs within their department, as they have a deep understanding of the financial intricacies and pain points. While the exploration of the best technology solutions might initially stem from this financial insight, it's essential for the finance champion to collaborate closely with the IT team. This partnership ensures that the selected technology not only addresses the financial department's requirements but also meets the standards for accuracy, security and compliance. Essentially, while finance identifies the "what" and "why" of technology needs, IT helps with the "how" to implement these solutions effectively.

How do you get your organization to help build AI use cases when employees fear that if they share a good use case, they will no longer be employed?

Kuzma: Adopting new technologies often brings about apprehension related to change and job stability. It’s crucial to communicate the objectives of the technology being implemented clearly. For instance, AI can be utilized to reduce personnel expenses over time, but it might also serve as a valuable tool for enhancing FP&A’s ability to make better enterprise decisions. In this scenario, employees would ascend the talent ladder by mastering the technology, potentially leading to more fulfilling roles that drive company impact rather than merely handling data.

Ready to go deeper? Recordings from the FP&A series are available to AFP members on AFP Learn. Not yet a member? Join today to get access.

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