There are five generative AI use cases for CFOs, said Alexander Bant, vice president for the Gartner Finance practice recently.
“When we look at some of the more publicised uses of generative AI, such as mimicking well-known authors, it might be hard to see how this can apply to a finance function,” he noted. “But an internally managed generative AI, as opposed to public-facing tools such as OpenAI’s ChatGPT and Google Bard, trained on corporate data has the potential to perform important tasks within finance.”
According to him, the top generative AI use cases for CFOs are as follows.
Contract and document review
Generative AI can scan contracts for errors and specific terms. Additional algorithms allow users to ask questions using natural language to get answers about terms and provisions. The same algorithms are used to summarise and categorise documents for sorting, review, and retrieval.
Financial and management reporting draft creation
Generative AI can compose first drafts of management analysis and discussion talking points, as well as financial footnotes that finance teams evaluate and refine.
Generative AI can review large collections of existing financial policies, like T&E policies, and provide initial recommendations for how those policies could be applied for finance teams to evaluate and refine.
Generative AI can translate code from older coding languages, like COBOL, into more modern programming languages, like SQL, KnowledgeSQL, and Python.
Forecast and budget variance explanation
Generative AI can provide explanations of forecast and budget variances for FP&A teams to use in business reviews, as well as further synthesise those trends and insights for executive and board consumption.
Driving force of generative AI adoption in corporate finance
There are several, but arguably the most direct driver is that boards and C-suites are impressed with the potential for innovative generative AI use cases to drive growth and profitability, Bant pointed out.
If implementing generative AI can make the finance function more agile, and able to provide higher quality analysis and better strategic support to the business, it could be a significant source of competitive advantage for leading adopters, he added.
In addition, investors expect new sources of growth and productivity, and ultimately better margins, which is placing pressure on leaders to not get left behind in the AI race, Bant noted.
While many employees have concerns about job losses due to AI, if they begin to see the technology as an essential part of doing their work, they will be more likely to quit organisations where they aren’t able to fully leverage generative AI, he said.
When it comes to regulators, they expect organisations and their leaders to comply with responsible generative AI regulations, and this will likely lead to adoption in a formalised and controlled manner rather than leaving employees to experiment with the technology, Bant predicted.