Artificial intelligence is no longer an abstract future concept for corporate treasury—it is rapidly becoming an operational reality. While early conversations focused heavily on generative AI and its ability to draft cash position summaries and explain complex data in reports, a more consequential shift is now emerging: agentic AI, a software agent that operates semi-independently to execute tasks from initiation through completion.

According to Strategic Treasurer’s 2026 Treasury Technology Analyst Report, agentic AI represents a structural evolution in how treasury professionals interact with systems—moving from procedural commands to outcome-driven instructions. This shift has the potential to materially change treasury workflows, accelerate decision velocity, and redefine the role of human oversight.

From automation to autonomy

Treasury has long relied on automation to reduce manual effort—whether through straight-through processing, rules-based workflows, or robotic process automation (RPA). While effective, these approaches depend on rigid logic and predefined sequences. Agentic AI introduces a different model. As described in Strategic Treasurer’s 2026 Treasury Technology Analyst Report, agentic AI is designed to understand user intent, formulate multi-step action plans, and dynamically adapt based on context.

In practical terms, this allows treasury teams to move away from task-by-task commands toward goal-oriented execution. Instead of manually completing each step in a reconciliation or exception-management process, an agentic system can identify anomalies, determine appropriate actions, and resolve issues across connected systems—escalating only when human judgment is required.

This flexibility distinguishes agentic AI from traditional automation. It enables treasury teams to manage variability and judgment-intensive tasks that have historically resisted full automation, such as exception queues, audit trail retrieval, or conditional payment execution.

Early adoption signals momentum

Agentic AI is not theoretical. Strategic Treasurer’s AI in Treasury & Finance Survey report reveals that “22% of companies are already using agentic AI, and 18% of respondents indicated use within their department (treasury, finance).” This level of adoption exceeds earlier estimates and signals accelerating momentum.

The survey data highlights a clear progression from awareness to experimentation and deployment:

• 72% of respondents know what agentic AI is
• 32% have experimented with agentic AI
• 33% are testing agentic AI at the enterprise level
• 25% are testing it specifically within treasury and finance departments

These findings suggest that while broad deployment is still evolving, treasury teams are increasingly comfortable piloting agentic use cases—particularly where efficiency gains, control improvements, and risk reduction are measurable.

Where agentic AI is taking hold in treasury

Initial implementations tend to focus on well-defined workflows where intent is clear and automation delivers immediate value. The 2026 Treasury Technology Analyst Report cites early use cases such as:

• Generating forecast variance reports
• Navigating exception queues
• Retrieving specific audit trails across systems
• Executing payments for pre-approved invoices

As confidence grows, the scope is expected to expand, with agentic AI applied to strengthen cash forecasting accuracy, enhance working capital optimization, and refine FX hedging strategies—provided appropriate governance frameworks are in place.

Crucially, agentic AI does not replace corporate treasury judgment; it augments it. By handling routine execution and decision preparation, these systems enable treasury professionals to focus more intently on oversight, scenario evaluation, and strategic trade-offs.

Data readiness and governance remain decisive

Despite its promise, one thing is clear: agentic AI success depends heavily on accurate, well-structured historical data. As emphasized in Strategic Treasurer’s 2026 Treasury Technology Analyst Report, treasury teams must ensure data quality, accessibility, consistency, and system integration well before deploying advanced AI models.

Even organizations with years of transaction history may encounter limitations stemming from formatting issues, system silos, or access constraints. Moreover, agentic systems require clearly defined boundaries—approval thresholds, escalation logic, audit trails, and controls—to ensure trust, transparency, and regulatory compliance.

In short, autonomy without governance introduces risk. Treasury teams must strike a careful balance between innovation and control.

Implications for corporate treasury leadership

The rise of agentic AI signals more than a technology upgrade—it marks a shift in treasury’s operating model. As systems become capable of executing intent-based actions, the treasurer’s role will increasingly centre on policy design, risk calibration, and strategic oversight.

Teams that prepare early—by modernizing data infrastructure, clarifying decision rights, and investing in AI literacy—will be better positioned to capture value while maintaining control. Those that delay may find themselves constrained by legacy processes as peers move faster with leaner, more adaptive treasury operations.

Looking ahead

Agentic AI remains in its early stages, but the direction is clear. As embedded capabilities expand within treasury management systems, interaction will become more intuitive, execution faster, and decision cycles shorter.

As Strategic Treasurer’s 2026 Treasury Technology Analyst Report underscores, the question for treasury teams is no longer whether agentic AI will play a role—but how quickly and responsibly it can be integrated.

For corporate treasury, agentic AI represents not just a technological advance, but a strategic opportunity to transform treasury execution—redefining efficiency, streamlining payments, reducing errors, and enabling smarter, faster decision-making in the years ahead.

 

 ⃰ Disclosure: Strategic Treasurer owns CTMfile.