Still think that generative artificial intelligence (AI) is just media buzz? That business leaders aren’t yet taking it seriously? Take a look at these results from a KPMG LLP survey conducted in June 2023 of senior executives representing multiple vertical industries: 

• 81% expected generative AI to have a high impact on their organization
• 74% expected generative AI to have the greatest impact in the next 12 to 18 months
• 81% anticipated a positive impact on the workforce from generative AI

In fact, generative AI is moving at warp speed in providing text, video, pictures and voice capabilities to help organizations achieve new levels in analysis, performance and process optimization faster than ever before. 

Opportunities for finance

We believe that chief financial officers (CFOs) and finance professionals are the natural leaders in adopting generative AI technology. Finance touches almost every part of the organization, from back-office accounting to procurement and sales. CFOs measure the pulse of the enterprise while monitoring expenditures, assets, transactions, cash flow and revenues. They are the stewards of vital business data, and they often build the strategy that considers the opportunities, risks and trade-offs of adopting new technology for the enterprise.

Equally important, generative AI is well-suited for supporting a number of tasks and requirements specific to finance. Here are just a few examples:

• Generative AI can reduce the time and labor required to create materials such as recurring financial reports, business reviews, management reports, investor calls and board meetings. It can bring data into templates and models and then generate new insights into financial and business trends, potential risks and opportunities. This means that executives and staff members can develop financial reports and make forecasts across multiple business levels without having to dig through old reports and manually integrate data. 
• For improved contract lifecycle management, generative AI can identify contracts with specific clauses or revenue recognition accounting treatments and review risky or undesirable contracts based on deviation from standard terms. It can generate new contracts automatically with preferred terms and standard language, optimize quote-to-cash and analyze large datasets to identify patterns and trends for accounts receivable aging and delinquency rates. 
• For enhanced benchmarking and peer analysis, generative AI applications can be used to analyze quarterly earnings reports and other public documents to extract competitor insights and understand key trends. It can also support the rapid reconciliation of invoices with automatic data extraction and entry of detailed line-item information.
• Generative AI can serve as a powerful research tool, able to find and synthesize data from publicly available material to generate insights on markets, competitors and customers. Analyses can be tailored to geographies or customer personas. Generative AI can also generate marketing and sales content for new products and deliver updates about generally accepted accounting principles, International Financial Reporting Standards and other finance-related regulations, including key takeaways applicable to different business functions.
• Generative AI also shows promise as an effective tool for detecting errors and spotting potential fraud. It can quickly compare new data or activity with past patterns to anomalies. The same technique can improve the quality of financials by continuously monitoring transaction data.

Next steps for CFOs

As with any new technology, adopting generative AI involves a careful assessment of risk, reward, business goals and investment appetite. A new generative AI for finance report shares next steps for CFOs starting on their generative AI journey. Steps can include: 

• Start with low-hanging fruit and low-risk opportunities to better learn what works for specific business challenges. 
• Integrate experiences and lessons learned to develop and refine long-term strategies. 
• Create support structures and a training approach in alignment with needs and resources. 
• Develop controls that address ethical and bias concerns.
• Partner with vendors with proven experience in generative AI to help with the design and deployment of solutions. 

Read this “Why finance should lead the adoption of generative AI” report for a deeper look at generative AI for finance. 

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