February 18, 2026 — Corporate treasury departments aren’t getting the productivity boost they anticipated from their investments in artificial intelligence (AI). This experience might have broader lessons for companies around the world wondering why the return on investment (ROI) on AI is falling short of expectations, at least for now.
Roughly half the large global companies participating in a new study from Crisil Coalition Greenwich have deployed some form of AI in their treasury departments. These companies have made only limited progress, often in the form of relatively basic process automation. Fewer than 1 in 10 have implemented AI into daily workflows in areas like forecasting and fraud detection, and only a relative handful claim to be in the process of deploying AI at scale across multiple treasury functions.
Why has progress with AI been so slow?
The central mistake companies are making with their AI investments in treasury and other departments is allocating resources to AI solutions without first building the data infrastructure needed to effectively operate those solutions. AI only thrives if it is fueled by large, integrated and clean datasets.
Many companies run on outdated legacy technology platforms composed of siloed systems that don’t interact well and store data in multiple locations, often in varying and incompatible formats. This data fragmentation presents an inherent barrier to AI adoption.
“Companies are skipping the foundational work of building comprehensive, organization-wide data management systems and jumping ahead to the sexier job of building and buying AI tools,” says Dr. Tobias Miarka, Global Head of Corporate Banking at Crisil Coalition Greenwich. “This, we believe, is the fundamental reason companies are failing to achieve expected results.”
Ignoring the Hype
This mistake is understandable. With AI hype intensifying, CEOs are under pressure to capitalize on the revolutionary technology. As a result, they are green-lighting investments in AI. As they do so, they know that boards and investors are clamoring for deals with major AI providers and headlines about the arrival of cutting-edge AI solutions. These audiences are unlikely to be impressed with an announcement that the company is directing a big part of those AI investments toward rebuilding internal data management architecture.
But that is exactly what’s required.
“Without the ability to produce seamless, timely and accurate data, companies will never be able to integrate AI at scale in treasury or anywhere else,” says Dr. Tobias Miarka.
About 60% of large corporates around the world expect to increase their investments in AI. For companies that take this step without addressing foundational issues of data management and governance, it could be a case of throwing good money after bad. But hopefully, that won’t be the case for most companies.
As Dr. Tobias Miarka concludes, “Companies can unlock anticipated ROI on AI in treasury departments and across organizations if they are willing to roll up their sleeves and tackle the hard work of revamping legacy systems and establishing processes for sound data governance.”
A new Crisil Coalition Greenwich report. AI in corporate treasury: What causes slow adoption, preventing full potential? presents the complete results of the 2025 study on AI use in corporate treasury departments and presents five key strategic priorities for senior leadership, chief data officers, corporate treasurers, and other executives as they work to maximize ROI on AI.