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Finance leaders see GenAI to have most immediate impact on forecast, budget

The original article was published on FutureCFO.

Finance leaders are now seeing generative artificial intelligence to have the most immediate impact on explaining forecast amd budget variances, according to Gartner, Inc.

A recent Gartner survey revealed that 66% of finance leaders think the technological advancement will affect these organisational functions, as it also showed that there are high levels of uncertainty among finance executives regarding the challenges the implementation of GenAI will bring about.

Finance leaders say GenAI will have the most impactful use case in forecast/budget variance explanations, with the respondents anticipating revenue/spend data classification and management reports as the next most impactful use cases for GenAI in finance.

The survey of 100 finance leaders also revealed the GenAI use cases that corporate finance leaders anticipate will have the most impact on their function in 2024.

Most Impactful Anticipated Use Cases for GenAI in Finance in 2024

“Forecast and budget variance explanation as the top choice reflects the availability of embedded GenAI interfaces within business intelligence tools,” says Clement Christensen, senior director analyst for Research in the Gartner Finance practice. “This enables users to perform natural language queries to quickly assess known common causes of variance.”

Gartner says recent GenAI advancements have refined models, so they are more capable of supporting tasks related to forecasts and variances, industry information and other factors that generate hypotheses around business performance, which can be tested through statistical models.

Challenges ahead

When it comes to potential challenges around implementing GenAI, Gartner notes that finance leaders expect to contend with issues around talent, data accuracy and governance, technical compatibility, budgeting and change management.

Data accuracy and talent limitations cause slightly more concern, although the fairly even distribution of other potential barriers reiterates financial leaders’ relatively limited experience with GenAI.

“GenAI is all about large language models, but the core of finance’s work isn’t in natural language, it’s in numbers, so many finance leaders are still waiting to see a GenAI application that can reliably handle complex calculations,” Christensen says. “For most finance teams, GenAI will likely be an interface to interact with other AI models based on machine learning, or other non-generative models for the next few years.”

Finance leaders seeking to adopt GenAI in their function should keep an open mind and involve key stakeholders, including the finance leadership and IT teams to discuss priorities and expectations.

Further, finance executives should also identify when to approach vendors to help determine which GenAI offerings are worth acquiring for the organisation’s needs.

Finally, CFOs should audit critical data with respective owners before implementation, to decide what modifications must be implemented for use by a GenAI model.

“Finance leaders see potential in the accessibility of GenAI in finance, but valid questions on reliability, accuracy, auditability, and cost, as well as data privacy and security still remain,” says Christensen.

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