A report by Crisil Coalition Greenwich has found that corporate treasury departments are not getting the productivity boost they anticipated from their investments in AI. Despite around half of large corporates globally committing resources to deploy AI in their treasury departments, the report suggests they have made limited progress towards implementing the technology into workflows in a way that can meaningfully impact performance.
The results from Asia-based respondents suggest large corporates in that region have been even slower to deploy AI in their treasury departments than their counterparts in the US or Europe.
Almost half (46%) of the US-based treasurers surveyed were running initial pilots, researching use cases or expanding AI across multiple treasury functions. The equivalent figure for Europe was 54%, with a further 8% of European treasurers using AI in areas such as forecasting or fraud detection.
In contrast, 61% of Asian treasurers said they were not using AI at all.
Christof Nelischer, former Group Treasurer of S4 Capital Group observes AI is increasingly being explored within treasury as a means to enhance operational performance with the overarching objective of creating a more efficient, data‑driven treasury function capable of responding swiftly to market dynamics.
“Among organisations that have begun integrating AI, automation remains the most common entry point, largely because it offers quick wins by reducing manual workload and improving process reliability,” he explains.
However, a central limitation persists: AI systems depend on high quality data and many companies are not yet able to provide the volume, consistency or cleanliness of data required to unlock the full value of the technology.
“Without strong data foundations, even the most advanced tools cannot deliver meaningful insights or reliable outputs,” says Nelischer. “Implementing AI does not compensate for structural data weaknesses – rather, it exposes them.”
It is also essential to remember that quantitative analysis is only one aspect of treasury work. Treasury professionals routinely navigate competing priorities, assess risk under uncertainty and make judgement calls that draw on experience as much as on data.
AI may offer suggestions or highlight patterns, but it cannot replicate the contextual understanding or strategic intuition that underpins effective treasury leadership. “In this sense, AI should be seen as a complement to human expertise, not a replacement,” adds Nelischer.
The report authors agree the central mistake companies are making with their AI investments in treasury and management systems, and other departments is allocating resources to solutions without first building the data infrastructure needed to effectively operate those solutions.
Data fragmentation presents an inherent barrier to AI adoption – often limiting companies to isolated applications, such as intelligent process automation in a specific workflow – and is one of the primary reasons treasury department staff globally still spend nearly a third of their time working in spreadsheets.
From an Asian treasury perspective, one of the most encouraging findings of the report was that Asian corporate treasurers were less likely to be using spreadsheets than their US or European peers and have been more proactive in implementing internally developed specialised applications.
