Corporate treasuries are on the verge of an AI-driven transformation that may redefine their core operations. AI now aids decision-making, cash forecasting, and liquidity optimization. A recent report by Citi reveals that while 82% of treasury units are experimenting with AI, only a small number have scaled its use extensively. However, projections suggest AI will become an integral component by 2030, evolving treasuries into intelligent financial hubs. Despite being in nascent stages, AI is anticipated to redefine treasuries’ role in business operations significantly.
Earlier discussions surrounding AI in treasury functions highlighted potential benefits but faced scrutiny due to data quality issues. AI adoption has been progressing, albeit slowly, due to fragmented data systems and the absence of standardization. Traditional treasury processes have relied heavily on manual input, making the transition to AI-powered platforms a significant logistical challenge. Nonetheless, advancements like Bank of America’s CashPro indicate that AI-powered platforms offer improved data visibility and reliability for corporate treasuries.
How Are Treasuries Adapting to AI?
Corporate treasuries are increasingly investing in digital transformation, with nearly 60% reporting the identification of practical AI applications. Liquidity forecasting and data reconciliation emerge as prominent areas. However, data integrity remains a major challenge, underscoring the necessity of comprehensive data systems. According to Citi, without reliable data, “AI will only replicate human errors at greater speed.” Establishing centralized databases and ensuring data accuracy are critical steps for viable AI adoption in corporate treasuries.
What Do Treasury Leaders Say About AI Integration?
Treasury professionals recognize the necessity of adapting to AI.
“The potential productivity gains from AI are too significant to ignore,” said Ron Chakravarti, Citi’s Global Head of Client Advisory Group. “Generative AI is the getting-things-done tool for treasury.”
Meanwhile, Joseph Neu of NeuGroup emphasizes trust in data quality for successful AI integration.
“There must be 100% trust in the numbers. Generative AI has been slow to deliver at this level of trust,”
according to Neu. These leaders indicate a focus on collaboration, data accuracy, and cautious implementation as part of their AI strategies.
In parallel, some firms are experimenting with agentic AI to push the boundaries of automated processes. Systems capable of immediately recommending or executing internal transfers are in pilot stages. These prototypes, closely monitored by human oversight, illustrate the potential of AI to enhance treasury efficiency. Such systems align with Citi’s outlined maturity model for AI, which underscores gradual transformation before attaining full autonomy.
The outlook for corporate treasury involves not just technological upgrades but also an expanding scope. Treasuries are increasingly seen as strategic contributors to enterprise financial planning. Citi’s findings reflect treasurers’ expanding roles as they manage liquidity, payments infrastructure, and data resilience in a rapidly digitizing world. The integration of technology and strategic planning is advancing treasuries beyond their traditional roles.
A comprehensive approach is recommended, starting with small pilot projects that demonstrate immediate value. Citi’s report highlights a need for human validation and outcome measurement to preserve credibility as treasuries gradually integrate AI. However, quick, unchecked advancements might undermine progress, indicating a cautious yet deliberate approach remains crucial for treasuries exploring this transition.
