The rapidly increasing adoption of artificial intelligence (AI) technologies within finance functions was underscored by a recent survey from Gartner. Polling the views of 121 finance leaders globally, the survey revealed a noteworthy rise in AI utilisation. In 2024, 58% of respondents reported using AI technologies, a substantial increase from the 37% recorded in 2023. Notably, only 19% indicated no plans for AI implementation, highlighting a clear trend towards AI integration.
Commenting on the findings, Marco Steecker, Senior Director of research in Gartner's finance practice, observed a narrowing gap between finance and other administrative functions such as HR, legal, and procurement, in terms of AI adoption. "In the previous year’s survey, other administrative functions were twice as likely to be using or scaling out AI solutions compared to the finance function. This year the gap is almost non-existent," Steecker noted in a press release.
The burgeoning interest in AI within finance is also prominently reflected in the treasury sector. Paul Bramwell, Enterprise Treasury Lead at Trovata, praised the broader adoption of AI within finance teams while emphasising the significant benefits within the treasury function itself. He highlighted the role of treasury groups as data gateways to the banking world, which allows for deep insights into spend, liquidity trends, fraud detection, and tracking specific transaction types and lines of business.
"With the advent of AI, it’s becoming easier to analyse significant volumes of data to create forecast models, plan for liquidity events, reduce fees, and improve either debt costs or investment returns," Bramwell stated. He also pointed out the advantages of generative AI in querying large datasets, simplifying the process for users who may not have advanced technical expertise.
The survey pinpointed four main use cases for AI in finance:
- Intelligent process automation (44% adoption).
- Anomaly and error detection (39% adoption).
- Analytics (28% adoption).
- Operational assistance and augmentation (27% adoption).
James Kelly, Senior Vice President of Treasury, Risk Management, and Insurance at Pearson and Co-Founder of training company Your Treasury, noted that the highlighted use cases align with proven AI applications. He observed that automation in accounts payable (AP) and accounts receivable (AR) is becoming more prevalent, reaping benefits in fraud detection, automation, and forecasting, which in turn aids treasury operations.
However, Kelly cautioned that implementing AI-driven analytics can be challenging due to data-related issues. He cited the need for data cleaning and preparation, and the high sensitivity of the data handled by treasurers, which often necessitates private, isolated models to ensure privacy, rather than off-the-shelf solutions like ChatGPT.
Despite the evident interest in AI, several challenges to its adoption remain. Gartner's research pointed out obstacles such as inadequate data quality and availability, low levels of data literacy and technical skills among finance leaders, and an unclear understanding of the roles and skills needed for AI implementation. The development of AI skills within existing employees is also progressing slowly.
To address data quality issues, Gartner experts recommend transitioning from a ‘single version of the truth’ data management policy to a ‘sufficient versions of the truth’ approach. This strategy balances the need for high-quality data with the need to support decision-making processes effectively.
In summary, while there are challenges to AI adoption within finance, the increasing use of these technologies is consistent with the evolving needs and ambitions of finance professionals. "Once the case is proven and the issues ironed out, finance and treasury will happily adopt," Kelly concluded, reflecting optimism about the future of AI in the finance function.
Source: Noah Wire Services