The AI Revolution in Treasury Management
Corporate treasury departments worldwide are accelerating their adoption of artificial intelligence, transforming from traditional financial stewards into strategic partners powered by data intelligence. According to Citi’s comprehensive global survey of 75 corporate treasuries across multiple industries, nearly 60% of treasurers have identified at least one practical generative AI use case, while 40% plan to increase their AI investment within the next two years.
“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.” This sentiment reflects a broader shift in how financial leaders view technology’s role in their operations, moving from supportive tool to core capability.
The Four-Stage Maturity Model
Citi’s report outlines a detailed four-stage maturity model that begins with identification of use cases and progresses through exploration, transformation, and optimization. Each stage requires measurable results, structured data, and continuous human oversight. This structured approach helps organizations navigate the complex landscape of AI integration while maintaining control over the transformation process.
The most common applications currently in pilot include liquidity forecasting, reconciliation, and automated report generation. A smaller but growing number of treasuries are testing generative AI for more advanced tasks like variance analysis and narrative creation in management reports. These developments represent significant strategic manufacturing shifts in how financial data is processed and utilized.
Data Quality: The Critical Foundation
Despite the enthusiasm for AI capabilities, data quality remains the single biggest barrier to successful implementation. More than 70% of respondents cited fragmented or incomplete data as a major constraint. The report recommends building centralized data lakes, establishing API connections to enterprise resource planning systems, and defining clear ownership for data accuracy.
“Without those foundations,” Citi warns, “AI will only replicate human errors at greater speed.” This challenge reflects broader industry developments in data management that organizations must address to leverage advanced technologies effectively.
Building Trust in AI Systems
“Treasury is the ultimate guardian,” emphasized Joseph Neu, Founder and CEO of NeuGroup. “There must be 100% trust in the numbers. Generative AI has been slow to deliver at this level of trust.” Citi links this skepticism to a broader cultural challenge, noting that while AI can improve speed and precision, its credibility ultimately depends on governance, explainability, and clear audit trails.
This call for transparency is echoed by practitioners implementing these systems. “As a first step, we invested time to train the treasury team and trigger a change mindset,” explained Alexander Reijrink, Global Head of Corporate Finance and Risk Management at Philips. “This helps us in finding the most valuable use cases, wherever they come up.”
The Evolving Treasury Ecosystem
The transformation Citi envisions is already visible across the financial ecosystem. Treasury teams are moving away from manual spreadsheets toward platforms powered by predictive analytics and data intelligence. Bank of America’s CashPro, for example, gives treasurers real-time visibility into global cash positions and forecasts, illustrating how structured data enables faster, more confident decisions.
These changes parallel related innovations in other sectors where data-driven decision making is becoming standard practice. The convergence of technologies is creating new opportunities for efficiency and strategic insight.
Agentic AI and the Future of Automation
Experiments with agentic AI are testing the boundaries of automation in treasury functions. Some firms are building systems that can recommend or execute internal transfers while maintaining human review and full traceability. These prototypes resemble what Citi describes as the “transformation” stage of AI maturity, where models assist but do not yet act independently.
Citi’s own Treasury and Trade Solutions group is applying these concepts to real transactions. In its most recent quarterly update, the bank shared that it’s extending tokenization and programmable money capabilities to corporate clients, enabling instant cross-border liquidity and more automated cash management. These capabilities represent significant market trends toward more integrated financial systems.
The Expanding Role of Treasury
The shift toward AI-powered treasury isn’t just technical—it’s fundamentally changing the function’s strategic position. As AI and cyber risk converge, treasurers are becoming key participants in enterprise planning, overseeing not only liquidity but also payments infrastructure, data quality, and digital resilience.
Citi’s findings support this evolution, observing that treasurers who collaborate with technology and data teams early are better positioned to move from experimentation to transformation. This strategic positioning reflects how recent technology implementations are reshaping traditional roles across multiple business functions.
Balancing Innovation with Prudence
Despite the clear momentum, Citi stresses that full AI deployment should proceed in phases, anchored by human validation and measurable outcomes. The report notes that 61% of surveyed treasurers prefer to start with small pilots that demonstrate quick wins before scaling. This cautious approach reflects the understanding that premature automation without proper oversight could erode credibility rather than enhance it.
The journey toward AI maturity in treasury represents both a technological and cultural transformation. As organizations navigate this path, the combination of robust data foundations, human expertise, and measured implementation will determine which treasuries emerge as true strategic partners in the AI era.
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