In a recent survey by research and consulting firm Gartner Inc., finance leaders identified generative AI (GenAI) as a significant tool for improving forecast and budget variance explanations.
The survey showed that finance leaders leverage GenAI in explaining variances in forecasts and budgets. Gartner emphasized the growing integration of GenAI within business intelligence tools, which allows users to quickly analyze and understand the causes of discrepancies in financial data through natural language queries.
“Forecast and budget variance explanation as the top choice reflects the availability of embedded GenAI interfaces within business intelligence tools,” said Clement Christensen, senior director analyst, Research, in the Gartner Finance practice. “This enables users to perform natural language queries to quickly assess known common causes of variance.”
Conducted in November 2023, the survey included responses from 100 finance leaders, providing insight into the anticipated impact of GenAI on finance functions in the upcoming year.
Finance leaders also pointed to revenue and spend data classification and management reporting as other areas where GenAI is expected to play a pivotal role. Recent advancements in GenAI have enhanced its ability to support these tasks by generating hypotheses about business performance, which can then be tested through statistical models.
Challenges in implementing GenAI
Despite the enthusiasm for GenAI’s potential, finance leaders anticipate several challenges in its implementation. These include concerns about talent, data accuracy and governance, technical compatibility, budgeting, and change management. Data accuracy and talent shortages were noted as the most pressing issues, reflecting the relative inexperience many finance teams have with GenAI.
“GenAI is fundamentally about large language models, but finance is driven by numerical data,” Christensen explained. “Many finance leaders are cautious about relying on GenAI for complex calculations. In the near future, GenAI will likely serve as a user-friendly interface to interact with other AI models that are more adept at handling numerical data.”
For finance leaders looking to integrate GenAI into their operations, Christensen advises a collaborative approach. This includes involving finance leadership and IT teams early in the discussion to align priorities and set realistic expectations. He also recommends assessing potential GenAI vendors to determine the most suitable offerings for their organization’s needs.
A thorough audit of critical data, in collaboration with data owners, is essential before implementing GenAI. This process will help identify any necessary data modifications to ensure the AI model operates effectively and accurately.
Christensen concluded with a reminder of the cautious optimism surrounding GenAI: “While there’s a clear potential for GenAI in finance, concerns about reliability, accuracy, auditability, cost, and data privacy and security need to be carefully addressed.”
You must be logged in to post a comment.