Generative AI in the Finance Function of the Future

Finance functions of global companies have not escaped the buzz surrounding the transformative potential of generative AI tools, such as ChatGPT and Google Bard. To see beyond the hype, CFOs need a nuanced understanding of how these tools will reshape work in the finance function of the future.

As with other technologies, the adoption of generative AI in finance functions will likely follow an S-curve pattern. (See Exhibit 1.) Currently, finance teams are considering how the technology can augment existing processes by creating text and conducting research. Looking ahead, the integration of generative AI will transform core processes, reinvent business partnering, and mitigate risks. Generative AI will eventually collaborate with traditional AI forecasting tools to create reports, explain variances, and provide recommendations, thereby elevating the finance function’s ability to generate forward-looking insights. The enhancements will empower finance professionals to make more informed strategic decisions, leading to improved operational efficiency and effectiveness.

But, the adoption of generative AI in finance functions entails challenges, including accuracy and data security and privacy. To overcome the obstacles and stay ahead of the adoption curve, now is the time for CFOs to learn about the applications of generative AI in finance functions that will have the most impact and prepare to capitalize on emerging capabilities.

Current and Near-Term Applications Augment Existing Processes

So far, generative AI tools are primarily used to process and generate text and images. Their ability to generate numerical analyses with the accuracy required in finance is still evolving. The tools can perform an initial pass at analyzing limited data sets, but the reliability of outcomes must improve before human intervention is no longer required. In contrast, the traditional applications of AI in finance functions can reliably analyze numerical data for forecasting and risk assessment, among other use cases. Some use cases may therefore be specific to either generative AI or traditional AI techniques, while for others it may be possible to apply the technologies in combination. (See Exhibit 2.)

At present, the integration of generative AI into finance functions focuses on augmenting existing processes through narrative generation and one-off analysis of small data sets. Current and near-term applications across the finance value chain include the following:

• Finance Operations. Creating preliminary drafts for tasks that are text-heavy or require minimal analysis, such as drafting contracts and supplementing credit reviews. (See “Case Study: Supplementing a Credit Review.”)
• Accounting and Financial Reporting. Offering initial insights for successive iterations of financial statements during month-end closures or assisting with audit trails for reclassification memos.
• Finance Planning and Performance Management. Performing ad-hoc variance analysis of the company’s structured or unstructured data sets (for example, comparing actuals to plans) and creating reports for business partners to explain their unit’s financial performance.
• Investor Relations. Supporting most aspects of the quarterly earnings calls. (See “Case Study: Drafting Responses for Investor Relations Calls.”)

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Source - BCG‍