Breaking the Ice: Why Global Corporate Treasuries Are Lagging in AI Adoption
A new industry survey has cast a spotlight on a surprising trend: even as organizations worldwide pour resources into artificial intelligence (AI), corporate treasury teams remain noticeably slow to adopt AI tools for core financial functions. This isn’t just a curiosity — it’s a symptom of deeper structural and strategic hurdles within one of the most financially critical parts of any business. (Tech in Asia)
📉 The Adoption Gap: AI Is Everywhere — Except in Treasury
Despite the era of AI acceleration, treasury departments — the units responsible for managing a company’s cash, liquidity, foreign exchange exposure, financing, risk, and banking relationships — are still on the sidelines when it comes to AI deployment. A recent survey by research firm Crisil Coalition Greenwich reveals that fewer than 10% of corporate treasury teams currently use AI for core functions, such as financial forecasting and fraud detection. Worse still, around half of these teams haven’t started using AI at all. (Bloomberg Law)
This disparity exists even as broader organizational AI spending grows, with many companies publicly committing to artificial intelligence — not just for efficiency but also strategic advantage across finance, operations, compliance, and customer engagement.
🧠 Why Treasury Is Behind: Structural and Skills Barriers
So what’s holding treasury back? The survey and related industry reporting point to a few key barriers:
- Lack of in-house expertise: Treasury teams often lack personnel with AI knowledge and data science skills, making it difficult to evaluate, deploy, and govern advanced technologies effectively. (Bloomberg Law)
- Messy financial data: Data quality, accessibility, and integration issues — common in legacy finance systems — undermine the accuracy and reliability of AI models fed into forecasting or risk tools. (AInvest)
- Integration hurdles: AI usually doesn’t plug cleanly into existing treasury systems and processes, creating compatibility challenges that many organizations aren’t prepared to manage. (Bloomberg Law)
Industry surveys from other regions and sectors reinforce the theme of an “AI readiness gap” — where enthusiasm for AI exists, but operational preparedness in talent, systems, governance, and risk management lags significantly. (PR Newswire)
🔮 What This Means for Finance Leaders
For corporate CFOs and treasurers, this is both a wake-up call and an opportunity:
- Competitive resilience: Organizations that overcome these barriers may unlock stronger risk management, sharper forecasting, and more accurate fraud detection — capabilities that are increasingly strategic in a volatile economic environment.
- Strategic investment: Treasury teams that invest in data quality, talent, and integration frameworks could leapfrog peers into value-generating AI use cases.
- Risk governance: As AI becomes more prevalent, robust oversight mechanisms to balance innovation with compliance and data integrity will be essential — especially in finance functions with regulatory oversight.
📚 Glossary of Key Terms
- AI (Artificial Intelligence): A set of computer technologies that simulate human-like decision-making, pattern recognition, and predictive analysis.
- Treasury Function: A corporate unit responsible for cash management, liquidity planning, risk exposure, and banking relationships within an enterprise.
- Forecasting: The process of predicting future financial performance using historical data and analytical models.
- Data Integration: Combining data from multiple sources into a unified view, critical for accurate analytics and AI application.
🧾 Source
https://www.techinasia.com/news/global-corporate-treasuries-slow-to-adopt-ai-survey
