The advent of autonomous treasury has ignited a competitive push, complete with aggressive industry targets. Not all companies will want to proceed at the same speed.
The shift to an autonomous treasury is reshaping the world of corporate finance, driven by new strategies and technologies—from self-healing cash forecasts to AI-driven liquidity engines—that are replacing legacy systems and maximizing yield.
To fully realize the potential, corporate finance leaders are strategically investing in the key areas that will accelerate the transition. The next phase of autonomous treasury will be defined by three investment-focus areas, says Sayantan Chakraborty, head of Digital Payments at Fiserv. “Treasurers don’t lack visibility anymore; they lack widgets that can act on that visibility in real time,” he says. “The gap isn’t analytics. It’s execution.”
Although agentic AI can forecast cash positions and draft funding instructions, Chakraborty notes, current corporate infrastructure often runs in batch mode. The first essential missing link is comprehensive, real-time cash positioning, second, it’s combined with rule-based, just-in-time money movement across multiple payment rails—including instant and traditional—and third, integration of new features like tokenized deposits and programmable payments.
The technological journey still requires human expertise, however. And Chakraborty advises building around legacy ERP systems rather than waiting for a complete modernization.
“Think of it as an AI-powered autopilot added to an older cockpit,” he says. “Policies are enforced, actions are executed, and audit trails are preserved without forcing a full-core replacement on day one, under the watchful eyes of a trained cockpit and cabin crew.”
The era of multi-year, big-bang upgrades is over, Chakraborty argues. Instead, the best course is to implement a lightweight, 24/7 automation layer to handle real-time balances, rules, and payments.
As instant payment rails and real-time reporting become more widespread, Chakraborty predicts the current practice of pre-funding accounts before cut-offs will become obsolete. Instead, “agentic AI will push treasury from once-a-day instructions to continuous, just-in-time funding: as soon as execution matches intent across all rails.”
This shift will impact float, causing idle-balance float to decrease and driving banks to focus their earnings on 24/7 clearing services, intraday credit, and real-time liquidity.
Siemens, a leader in autonomous treasury, adopted J.P. Morgan’s programmable payment feature (formerly Onyx, now Kinexys) in late 2023. Siemens shifted to advanced programmable payments using the blockchain-based ledger, JPM Coin. This allows their bank accounts to autonomously manage cash and execute transactions based on pre-defined rules. Addressing the inefficiency of idle pre-funded balances, Siemens implemented a just-in-time mechanism. Funds are only moved into a specific account the moment a payment is due. If a balance drops below a set threshold, the system autonomously sweeps funds from a central cash pool, enabling Siemens to operate with near-zero balances in local accounts.
“In my experience, the biggest challenge is not technology, but the mindset shift in finance and treasury,” states Heiko Nix, global head of Cash Management and Payments, Siemens. “For almost every technical problem, there is a solution. But simplifying entrenched processes and changing how people think about treasury and its role takes significantly more time and effort. In practice, you do not need to convince everyone at once, what matters is building sufficient momentum across the organization to enable real transformation.”
A ‘Forward-Looking Control Tower’
AI creates a strategic opportunity, argues John Stevens, senior vice president, global head of Capital Markets, Financial Institutions & Working Capital at Kyriba.
“AI can transform working capital management from a retrospective reporting function into a forward-looking control tower,” he says. “Instead of focusing on past events, you can optimize for the future in real time. This is because tasks that previously required manual, analog effort, or demanded analysts to spend long hours consolidating reports, can now occur instantaneously. This real-time capability allows for significantly more sensible and timely decision-making.”
Companies still need to work closely with vendors to build AI safely, he cautions: “We don’t see a single out-of-the-box ‘autonomous’ product replacing the diversity of treasury needs.” The future will be “composable,” he predicts, although it is important to be precise about what this means.
While Kyriba App Studio serves as an extensibility layer for building bespoke integrations and workflows on the Kyriba platform, Stevens stresses that it is not an agent-building toolkit. The agentic AI layer is TAI, which provides Kyriba-developed agents with “a clear human in the loop posture.”
Using a third-party model doesn’t automatically make an AI tool less intelligent and using only in house-models doesn’t automatically make it more intelligent, he argues.
“In treasury, the deciding factor is whether the AI can be used safely and consistently in a regulated environment,” Stevens says. TAI isn’t positioned to avoid external LLMs. “We use a leading external model [Anthropic’s Claude] within a controlled, governed deployment. The difference is the wrapper around the model: strict limits on what data it can access, clear rules on what it’s allowed to do, and a full audit trail of activity.”
Practically, that means the AI can help generate insights—summaries, explanations, flag anomalies, scenario narratives—while anything that could affect payments, liquidity, or risk stays under platform controls, approvals, and policy-driven workflows.
“So it’s not a binary choice between open and sovereign,” he notes. “Some organizations will require sovereign options for policy or jurisdiction reasons, but most regulated treasuries are looking for governed AI: strong models, used in a way that is secure, auditable, and designed for real operational control.”
Redefining Corporate Finance
The potential benefits to treasury have ignited a competitive push for autonomy, complete with aggressive industry targets and a race for “fully autonomous” platforms.
HighRadius recently updated its agentic AI platform with the goal of achieving over 90% automation for the Office of the CFO by 2027. The initiative involves deploying AI agents across six product suites and 20 products within accounts receivable, payables, treasury, close, and consolidation. The release of 186 agentic AI agents, announced last February, moves HighRadius closer to the “fully autonomous platform vision” it first announced in 2019, with cash application and cash forecasting already demonstrating 90% touchless automation.
HighRadius prioritizes “measurable value creation,” which it validates with clients through mutually agreed success criteria (MASC). This value is delivered via automated agents, aiming for 90%-plus automation, and assisted agents, designed to triple user effectiveness.
CEO Sashi Narahari views agentic AI as an interim step toward HighRadius’s goal of ensuring that all its products are “fully autonomous”—defined as 90%-plus touchless end-to-end process—by 2027. Narahari stresses the critical nature of this goal, to the point that failing to achieve it would lead to the company’s demise.
What about mid-tier banks that may not want to jump to a comprehensive transformation? For them, Chakraborty advises that a single, reliable orchestration endpoint is better than many disparate APIs.
“Essential to this is a real time balance plus payment execution API,” he says “exposing positions, limits, and instant movement through a single, resilient interface. That’s what lets AI driven treasury systems act as agents, not just analysts.” Integrating such a process with tokenized deposit movement is also beneficial where possible, he adds.
That said, the journey toward the autonomous treasury, spearheaded by pioneering companies like Siemens and driven by the rapid evolution of agentic AI, is fundamentally redefining corporate finance.
The shift is not merely about incremental efficiency gains but is coming to be seen as a strategic imperative for maximizing yield, securing real-time liquidity, and moving beyond the constraints of legacy systems. Corporate treasurers who are embracing the transition are attracted by a promised tactical roadmap to a future-proofed role. For the financial institutions that serve them, autonomous treasury is an urgent call to align their offerings with a new era of continuous, intelligent, and just-in-time financial control.
