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.”)
Tomorrow’s Generative AI Capabilities Will Be Transformative
As generative AI’s ability to accurately analyze large data sets improves and finance professionals become more adept users of the technology, we expect to see a gradual increase in the number of AI-driven “copilots” or “assistants” that operate alongside practitioners. We also envision the seamless integration of traditional AI and generative AI into combined use cases. For example, a traditional AI forecasting tool could produce forecasted financials, while generative AI could explain variances and, more important, offer recommendations on different forecast scenarios and associated business decisions.
As a result, the next generation of finance copilots will empower the finance function of the future in three significant ways:
Transforming Core Processes. An increasing array of generative AI assistants will continually transform core finance processes, such as contract drafting, invoice processing, and general-ledger reviews. (See “Reviewing General-Ledger Entries.”) Initially, focused assistants may improve the efficiency of specific processes by approximately 10% to 20%. However, as tools and capabilities develop, they will augment a larger portion of overall finance operations tasks. Eventually, as use cases expand along the S-curve, generative AI will integrate flawlessly with processes that are currently manual or tedious.
Reinventing Business Partnering. Generative AI will provide support to the finance function’s business partners. This could encompass insights into financial forecasts, scenario planning throughout the budget cycle, and faster and more comprehensive business intelligence. (See “Case Study: Generating Business Intelligence and Strategic Insights.”) Finance activities that are currently so tedious that they hinder the gleaning of insights can be overhauled to enable rapid and clear insight generation. Pairing generative AI with traditional AI use cases will further enhance capabilities.
Managing and Mitigating Risk. Finance teams are already using AI in audit and control environments, such as to identify anomalies that might be indicators of fraud or noncompliance. The next wave of generative AI could go further by predicting and explaining anomalies. The timely identification and communication of the associated risks could prevent undesirable audit findings. (See “Case Study: Risk Identification.”)
The Challenges to Adoption
Compared with previous technologies, such as robotic process automation and process mining, the barriers to experimenting with generative AI in finance functions are relatively low. However, several critical challenges must be addressed or managed to fully unleash the technology’s potential in the finance function of the future. Among others, these include:
- Data Accuracy. Generative AI tools, especially early versions, can struggle to perform accurate calculations. Ensuring highly accurate calculations requires diligence in designing generative AI tools. Alternatively, teams can use workarounds to generate content based on calculations performed outside of generative AI tools. These challenges are expected to diminish with continued advancements, as demonstrated by rapidly improved capabilities from GPT-3 to GPT-4, which includes a code interpreter plug-in.
- Leaks of Proprietary Data. When training generative AI models in the public cloud, companies transmit proprietary data that could be leaked in a security breach.
- Governance Model. Generative AI tools lack contextual awareness and real-time information. There is currently no implicit or explicit governance model for output validation.
- Hallucinations. Generative AI can sometimes produce incorrect responses in a highly convincing manner.
CFOs Must Prepare
CFOs cannot afford to stand on the sidelines as generative AI reshapes the finance function of the future and its partner functions, such as marketing and HR. Embracing this technology is crucial to maintaining a cutting-edge finance organization. To prepare, CFOs should take several steps.
Create proofs of concept using available use cases. Initiate adoption with use cases whose barriers to entry are low, such as investor relations and contract drafting. Evaluate and continuously refine the approach for optimal results. Finance personnel will likely find that applying the new technology in real use cases is the best way to climb the learning curve. This iterative approach is essential for cutting through the hype surrounding generative AI and developing a nuanced understanding of the technology’s practical applications and concrete value in the finance function.
Identify and train internal talent. Assess existing talent, identify skill gaps, provide training opportunities, and recruit individuals who are equipped to handle future use cases as they emerge. Ensure that finance personnel understand how generative AI can complement their work and unlock their potential by automating routine tasks, accelerating business insights, and improving operational efficiency. At the level of the individual analyst, the value proposition includes fewer repetitive tasks and keyboard strokes and more time for business collaboration.
Develop traditional and generative AI capabilities in-house. Evaluate whether the optimal approach is creating a center of excellence or embedding AI capabilities into technology teams.
Collaborate with IT. Build a strong partnership with IT to drive successful implementation. IT teams will play a pivotal role in prioritizing generative AI investments and addressing data security concerns surrounding the use of AI in finance function applications.
Champion generative AI. Many of the most important current opportunities reside outside of the finance function. CFOs should work with their C-suite peers to encourage creative thinking around potential use cases that promote cost efficiency and effectiveness. CFOs can also collaborate with financial planning and analysis and business partners to allocate investments to generative AI and incorporate generative AI-influenced cost targets into the business plan.
Generative AI has arrived and is evolving at an unprecedented pace. It currently excels in text generation and is swiftly honing its skills in numeric analysis. Finance leaders must closely monitor AI’s evolution, gain hands-on experience, and develop their organization’s capabilities. Given the comparatively low entry barriers, there is no need to wait for further advancements before initiating adoption. CFOs should embrace this technology immediately, remove any obstacles to adoption in their departments, and encourage their teams to take advantage of generative AI across the finance function.
The authors thank Deep Narayan, Francesca Gradara, Udit Mehra, and Kiran Wali for their contributions to this article.
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