Insight & Analysis

New survey highlights AI adoption in finance

Published: Sep 2024

According to a new Gartner survey, 58% of finance functions are using AI, up from 37% a year ago – so what does this mean for treasury teams?

Digital AI brain

A recent survey by Gartner has illustrated the growing adoption of AI technologies within the finance function. According to the survey, which polled the views of 121 finance leaders, the use of finance AI has increased significantly in the last year, with 58% using the technology in 2024, up from 37% in 2023. Only 19% of respondents said that no AI implementation was planned.

Marco Steecker, Senior Director, research in the Gartner finance practice, noted in a press release that in the previous year’s survey, “other administrative functions (such as HR, legal, and procurement) 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.”

Benefits for treasury

The growing adoption of AI in finance is reflected in the continuing interest in this topic in the treasury arena. “Whilst it’s great to see the broader adoption of AI within finance teams, there are also significant benefits within the treasury function,” comments Paul Bramwell, Enterprise Treasury Lead at Trovata.

Bramwell notes the treasury group often serves as the gateway to the banking world, collecting significant volumes of data which may be mined for insights into spend, liquidity trends, fraud and for tracking specific transaction types and lines of business.

“For years, treasurers have been told to do more with less which normally translates to less headcount and more automation,” he reflects. “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.” He adds that generative AI “also makes it significantly easier to query large data sets without having to be a technology expert.”

Use cases for AI

The Gartner survey identified four main use cases for AI in finance:

  • Intelligent process automation (used by 44% of finance functions).

  • Anomaly and error detection (used by 39% of finance functions).

  • Analytics (used by 28% of finance functions).

  • Operational assistance and augmentation (used by 27% of finance functions).

“The use cases outlined as being the most common have a reasonable track record and so it is no surprise to see them highlighted,” comments James Kelly, SVP Treasury, Risk Management and Insurance at Pearson, and Co-Founder of training company Your Treasury. “We are seeing automation in AP and AR becoming increasingly common, whether for fraud detection, automation or forecasting. This can then be leveraged in treasury.”

Nevertheless, Kelly notes that analytics tend to be more difficult because of challenges around data. “Where this can be imported from a TMS and/or an ERP, this is simple – but for many teams, data is not in a suitable state and so needs cleaning and preparing, which requires either external support or strong data skills within a team.”

In addition, he notes that treasurers are hindered by the sensitivity of the data they handle, meaning tools like ChatGPT “cannot be used off the shelf, but instead require private isolated models to ensure privacy.”

Overcoming challenges to adoption

While Gartner’s research shows the continuing interest in AI, it also highlighted some of the challenges finance leaders may face in adopting the technology. These included inadequate data quality/availability, and low levels of data literacy and technical skills. Meanwhile, plans to harness AI talent can be held back by a lack of understanding in the roles and skills needed for AI implementation, as well as slow progress in developing AI skills within existing employees.

Where data quality is concerned, Gartner experts recommend moving from a ‘single version of the truth’ data management policy in favour of a ‘sufficient versions of the truth’ approach that balances the need for data quality with the need to support decision-making.

While there may be challenges to overcome, it’s clear that AI continues to attract interest from the treasury world. “We know treasurers are keen to adopt proven technology to aid their teams, and so the results of this survey showing that finance is catching up fast with other teams is consistent with that,” Kelly concludes. “Once the case is proven and the issues ironed out, finance and treasury will happily adopt.”

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