Artificial intelligence is rapidly emerging as an integral tool for organizations across industries, and it’s set to dramatically transform a variety of business functions. The office of the CFO is no exception. 

About half (51%) of CFOs are investing in AI this year, compared to just 15% in August 2023, according to SAP Concur’s 2024 CFO Insights survey. But 58% of respondents said they understand very little about using the technology in finance. Just 4% said they had strong knowledge.

We’re predicting that 2024 will be a pivotal year for AI adoption, as the technology begins to lose its novelty and becomes a real game-changer in the business world. 

Spend management in particular is one area where CFOs can easily implement AI to improve financial outcomes and operational efficiency. 

To be sure, the use of AI in spend management isn’t new — SAP Concur solutions have had AI at their core for about a decade. Still, it’s clear that recent advancements have thrust AI technologies to the forefront in this area and many others.

Potential benefits

For those CFOs who are still on the fence about AI adoption in spend management, here are a few potential benefits to consider:

• Minimizing expense policy violations. Employees are often uncertain when it comes to expense-related policies at their organization. A 2022 SAP Concur pulse survey found that 43% of business travelers had made at least one travel request in 12 months that they weren’t sure aligned with company policy. Generative AI can help encourage compliance. For example, investing in large language model (LLM) assistants — intelligent chatbots, essentially — can help to answer employees’ travel and expense questions instantly. These chatbots can also build cost estimates based on flight and lodging preferences while working within policy. With AI, employees can also be alerted in real time to potential spend policy violations or if they are close to exceeding daily per diems (i.e., travel allowance) to prevent overspending.
• Improving the employee experience. Some mobile applications have been equipped with machine learning (ML) to recognize prices and items on a paper receipt, automatically filling in expense forms for an employee. These same technologies can itemize receipts from hotels with AI assistance. This can be a huge time saver for employees, freeing them up to focus on other tasks. And it creates a better employee experience while enabling greater visibility into all spend, which managers can then access to inform strategic decision-making.
• Digging deeper into tax calculations. AI can sift through substantial amounts of data in seconds, streamlining auditing approvals and tax calculations in the employee expense process. It’s capable of delivering data-backed recommendations — including when it comes to patterns humans may never notice — ensuring tax compliance with T&E spending at the country level. AI and ML can also immediately find anomalies, such as duplicate charges, to remove redundancies. Not only will this directly cut costs, but it will eliminate hours of labor and reduce errors that come with manual processes. It can also help protect businesses from audits and penalties, including IRS-based, accuracy-related regulations.

Data privacy risks

Of course, it’s no secret that AI poses risks as well. A sloppy rollout can open the door to problems such as data privacy issues. Given the sensitive nature of companies’ financial data, spend management is an area where the risk level is particularly high.

Generally speaking, companies today are experiencing heightened scrutiny into how they collect, process and use employee and customer information. T&E platforms in particular manage materials like receipts, flight booking confirmations, or emails containing personal and even sensitive details like addresses and birthdays.

AI-enabled tools are critical to the success of spend management tactics, but only CFOs who can mitigate the potential risks will see success. It’s imperative to select a solution provider with strong data privacy protection protocols that can ensure data will be protected and processed responsibly.