Most Finance Leaders Expect GenAI to Revolutionize Forecast and Budget Variance Explanations — Gartner Survey

The survey showed that 66% of finance leaders believe that generative AI will massively impact "forecast/budget variance explanations."
Most Finance Leaders Expect GenAI to Revolutionize Forecast and Budget Variance Explanations — Gartner Survey
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Gartner carried out a survey of 100 finance leaders which unveiled the GenAI use cases anticipated to have the most impact on corporate finance leaders’ functions in 2024. Top off the list, the survey showed that 66% of finance leaders believe that generative AI will massively impact "forecast/budget variance explanations."

As reflected in the survey results, there are heightened levels of uncertainty among finance leaders about the impending challenges of generative AI implementation. Further, revenue/spend data classification and management reports follow as the second most impactful use case for Gen AI in finance, according to respondents.

“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.”

Key challenges for finance leaders implementing GenAI

When it comes to potential challenges around implementing gen AI, finance leaders contend with the following issues:

  1. Talent

  2. Data accuracy and governance

  3. Technical compatibility

  4. Budgeting

  5. Change management 

Among the aforementioned issues, 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,” said Christensen. “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.”

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