This article is written by TIS (Treasury Intelligence Solutions)
Imagine a global treasury team that spent months evaluating AI-powered cash forecasting tools. They researched, sat through countless demos, and eventually selected the tool that seemed the most promising for their needs. They integrated it with their ERP and handed it their historical data. The outputs they received were fast, detailed, and unfortunately, inaccurate and incomplete.
In this scenario, technology isn’t to blame. Instead, the problem is the data feeding the tool: mislabeled cash flows, inconsistent intercompany entries, and siloed inputs from business units operating on different timelines. What AI did is to merely amplify these existing problems at scale.
Situations like these play out in treasury departments worldwide. They bring into focus the message that ought to sit at the center of any AI adoption strategy: Your cash forecasting is only as good as the data underlying it.
AI is no longer a peripheral conversation in corporate treasury, as more finance teams are adopting AI at rates comparable to other functions. The volatility defining today’s financial environment, from geopolitical disruptions to climate-related supply chain shocks, has exposed the fragility of traditional forecasting frameworks. Models built on historical averages and predictable payment behavior perform less reliably when volatility is embedded rather than exceptional.
According to the 2025 AFP Treasury Benchmarking Survey, over 60% of treasury professionals say cash or liquidity forecasting is the most challenging task they face, and AI is well-poised to help address this challenge. Additionally, EuroFinance’s survey of global treasury professionals shows that 46% of respondents are actively evaluating AI solutions — compared to 32% evaluating treasury management systems.
Yet nearly 47% of treasurers acknowledge awareness of AI-driven forecasting but report no concrete plans to adopt it. For most of these teams, operational readiness is the primary blocker, as data quality, system integration, and governance infrastructure have not kept pace with the ambitions placed on the technology. Teams using AI-powered cash forecasting successfully enable better decision-making.
Studies show that effective cash flow forecasting makes a company 1.5 times less likely to experience a liquidity crisis, and 2.5 times as likely to be profitable than those that don’t. When the foundations are in place, AI delivers meaningful value across the treasury function in both the short- and long-term.
AI systems can identify patterns and correlations across both historical and real-time data that human analysts are likely to miss. When integrated across functions, AI provides a more comprehensive view of a company’s cash position, detecting anomalies and flagging shifts in payment behavior more quickly than manual methods. Forecasting accuracy relies heavily on the quality of the underlying data.
Even large, complex organizations with multiple ERP systems and currencies can use AI to pinpoint when cash enters and leaves the business. While traditional models require constant updates and operate on static assumptions, AI-based models learn from patterns and adjust forecasts dynamically.
AI can produce thousands of potential scenarios based on historical data and current market conditions, enabling treasury teams to stress-test their liquidity positions in ways that would be impossible to execute manually, especially given the scale of operations in larger enterprises. In the current environment of heightened uncertainty, this capability is especially valuable as it empowers stronger contingency strategies and more resilient liquidity buffers.
Treasury and finance teams typically operate across a fragmented landscape of tools: ERPs, TMS platforms, bank portals, reporting systems, etc. AI reduces the manual reconciliation burden, freeing teams to focus on analysis and decision-making rather than data assembly. About 30% of respondents to EuroFinance’s poll identified reducing manual effort as the area with the greatest AI potential.
Accurate, explainable forecasts backed by AI analysis give treasury teams the credibility to present confidently to leadership and challenge assumptions that might otherwise go unquestioned. Treasury professionals use AI to shift from reactive to proactive strategic cash flow decisions.
No matter how sophisticated your forecasting platform or how capable your team, the truth is, AI-enabled cash forecasting is only as good as your data.
In EuroFinance’s 2026 poll, 51% of respondents identified data quality and consistency as the single biggest constraint to improving forecasting accuracy. When asked specifically about the barriers to trusting AI-generated forecasts, 37% named poor underlying data as their top concern, far above any other factor.
This response makes sense, since accurate forecasting depends on inputs from multiple, often disparate systems. When those inputs are incomplete or poorly governed, AI amplifies these gaps. Without a foundation of clean, timely, consistent data, forecasting becomes an exercise in futility.
Additionally, generative AI’s tendency to produce plausible-sounding — but incorrect — outputs has made many treasury teams cautious about accepting AI forecasts at face value. Organizations that rely on AI without applying judgment to its outputs are worsening the trust problem they seek to overcome.
Data quality and trust issues are the dominant challenge, but not the only ones. Some of the other barriers treasury professionals must navigate include:
AI is capable of powerful analytics, but it can’t compensate for fundamental data problems. Where data gaps are minor and isolated, AI models can often flag anomalies and prompt human review. But for structural issues like missing categories, siloed inputs, or absent governance, the model will produce outputs that reflect and reinforce those weaknesses.
Rather than improve forecasting, the primary value of AI in poorly prepared environments is to expose the inefficiencies that had previously remained buried in siloed platforms and spreadsheets.
While there’s no such thing as “perfect data,” there is such a thing as investing sufficiently in your data foundation, so AI enhances cash forecasting. Doing so requires clear data ownership, structured cash flow data, and integration architecture that enables cross-system visibility.
Treasury professionals are increasingly adopting AI and recognize its value in cash forecasting. The speed and analytical depth that AI brings to treasury operations represent a genuine leap forward, especially in a world where volatility is the norm.
However, while AI enhances the fundamentals of good forecasting, it only does so when strong foundations are present — clean, high-quality, well-governed data. Data is an ongoing investment that determines whether AI amplifies your capabilities or your mistakes.
To reap the benefits, treasury leaders must make data quality a core pillar of any AI-enabled cash forecasting strategy.
Treasury Masterminds is a community of professionals working in treasury management or those interested in learning more about various topics related to treasury management, including cash management, foreign exchange management, and payments. To register and connect with Treasury professionals, click the button below.
This article is a contribution from our partner, Embat
Accounting with the best financial team is a critical factor of success for any business, so nothing is more important for the CFO than surrounding themselves with the right people, this is what will allow you to enhance your role and focus on value generation from a much more strategic than operational perspective.
Traditionally, the financial domain was characterised by the mastery and application of a series of technical knowledge, which when well used not only allows one to ‘explain the past’, but also to generate reliable information for business decision making thus minimising the risk of errors.
It is the advance of technology itself, which has allowed them to redefine their role, following the same line of evolution as that of the CFO themselves, that is, towards a vision much more focused on the company’s business and strategy and not merely focused on explaining what has happened and on regulatory compliance.
This reconversion of the area requires the need for professional profiles that not only have a solid “technical” knowledge, but also have the necessary skills to take advantage of the benefits offered by the new technologies available.
It is above all a change in the culture of the area that must accompany and support the evolution of the business itself, adopting new ways of working, based on collaboration, experimentation and continuous learning, while at the same time requiring a rethinking of the operating model, including the automation of tasks and the redefinition of internal processes.
Combining analytical rigor with business intuition, technical precision with the flexibility to adapt to changes in the environment, is essential when it comes to building a financial team, where technical expertise becomes a necessary (and mandatory), but not sufficient, skill.
It is therefore necessary for financial professionals to have a broader understanding of the business they manage, in order to be able to adapt as quickly as possible to changes, which are becoming faster and faster.
The evolution of the CFO in today’s business strategy
Discover the evolution of the CFO and their relevance in today’s business landscape.
Being able to translate and being able to communicate complex information in such a way that it can be used for decision making by the rest of the organisation, which generally speaks a different ‘language’ and does not usually have the same technical knowledge, requires the development and good application of what are defined as ‘soft skills’.
Empathy, proactive attitude, autonomy, thinking critically about how things are done, adapting to new scenarios, interacting with interdepartmental teams, are critical and essential skills that any finance team must have in order to turn data into analysis, business opportunities into results and strategy into profitability for the company.
This is where the CFO takes on a central role, in the sense of being able to integrate the different skills of the financial team, identifying both their strengths and weaknesses, thus ensuring that all members of the team work towards the same common goal.
Therefore, it becomes relevant to promote diversity within the financial team not only in terms of the incorporation of people with different professional experiences, but also with other ways of thinking and approach, in order to encourage the generation of innovative ideas that can become creative solutions to the problems that arise.
A diverse finance team can also be better prepared to meet the challenges of a globalized marketplace. Companies operating in multiple countries need teams that understand the particularities of each region, both from a technical and cultural perspective.
On the other hand, it is necessary to promote a culture of open and ‘bidirectional’ communication between the CFO and the members of their team, where the contributions of each one of them are valued, something that is relevant to strengthen the group and thus tend to achieve the proposed objectives.
Trust is another essential requirement for building a cohesive team, especially when confidential and strategic information must often be managed, which is why it is essential to guarantee its integrity. Likewise, it must be bidirectional, thus generating a climate of security that reinforces open communication, as well as the responsible assumption of risks and the ability to learn not only from the successes achieved, but also from the failures.
Thus, forming a financial team is a process that requires a certain “science” in the selection of the best talents with the necessary technical knowledge for the development of their functions, as well as the purest “art”, since it must be complemented with the ability to adapt, innovate and collaborate closely with other areas of the company.
In this way, a balanced integration between art and science is what really determines the difference between a financial team that is oriented to the administration of resources, and another that is dedicated to the generation of value through continuous improvement.
Treasury Masterminds is a community of professionals working in treasury management or those interested in learning more about various topics related to treasury management, including cash management, foreign exchange management, and payments. To register and connect with Treasury professionals, click the button below.
This article is written by Nadia Callaghan
Treasury has quietly become one of the most powerful levers for improving financial performance. In many organisations, it is still treated as a protective cost-centre function, focused on liquidity, compliance and risk management. Yet the businesses that outperform today are those that recognise treasury as a value creator. From my experience, optimising finance returns is not only about taking more risk; it is also about tightening the fundamentals that determine how efficiently money moves through the organisation.
The first and most immediate source of value comes from strengthening collections. Cashflow improves dramatically when organisations collect what they are owed, when they are owed, without leakage or delay. Accounts receivable often suffers from fragmented ownership, inconsistent follow‑up and manual allocation processes that slow everything down. Commission collections can be even more complex, especially in industries where revenue is shared across multiple parties. When treasury leads a disciplined approach to collections, supported by real‑time ageing visibility, automated reminders and clear accountability, liquidity improves almost instantly. Faster allocation of incoming cash reduces unapplied balances, improves forecasting accuracy and frees capital that would otherwise sit idle.
Cost discipline is the second major lever. Operating expenses are rarely fixed; they simply feel that way because they are not reviewed often enough. Treasury can materially improve returns by treating bank fees and partner contracts as commercial agreements rather than administrative necessities. Regular fee reviews reveal charges for services no longer used, pricing tiers based on outdated volumes and FX or payment fees that are significantly above market rates. Debt‑side partner contracts often contain renewal clauses, utilisation fees or covenant‑related costs that can be renegotiated when treasury brings data to the conversation. Organisations that review these agreements quarterly typically achieve meaningful cost reductions without changing their operating model.
Once collections tighten and costs come under control, surplus liquidity becomes visible. Idle cash is one of the most common drags on returns, and yet it is also one of the easiest to fix. Treasury can deploy surplus funds into safe, yield‑generating instruments such as money market funds, notice accounts, term deposits or custody accounts for short‑term securities. These options provide daily liquidity, low risk and significantly better returns than traditional operating accounts. Even modest yields create meaningful profit when applied to large balances that previously sat dormant.
Centralising liquidity amplifies these gains. Cash pooling allows organisations to treat group‑wide cash as a single strategic asset rather than a collection of isolated balances. Physical pooling sweeps cash into a master account, reducing external borrowing and enabling centralised investment strategies. Notional pooling aggregates balances virtually, optimising interest without the complexity of intercompany loans. Both approaches reduce reliance on external debt, lower interest expense and increase the organisation’s ability to generate returns from its own liquidity.
The final lever is automation, which is often underestimated in its impact. Manual treasury processes consume time, introduce errors and prevent teams from focusing on value‑creating activities. Automating bank reconciliations, AR allocation, commission matching, payment runs, forecasting and intercompany settlements reduces operating costs and strengthens control. Automation also improves data quality, which in turn improves decision‑making. Treasury teams that operate with clean, real‑time data can model scenarios more accurately, anticipate liquidity needs earlier and respond to market conditions faster.
Optimising returns is not a single initiative; it is a discipline. When treasury strengthens collections, reviews expenses with commercial rigour, deploys surplus cash intelligently, centralises liquidity and automates manual processes, it becomes a profit engine rather than a cost centre. The organisation benefits from improved cashflow, reduced leakage, lower operating costs and higher returns on liquidity. Treasury’s role shifts from safeguarding the business to powering it, creating a financial foundation that supports growth, resilience and strategic ambition.
Treasury Masterminds is a community of professionals working in treasury management or those interested in learning more about various topics related to treasury management, including cash management, foreign exchange management, and payments. To register and connect with Treasury professionals, click the button below.
This article is a contribution from our content partner, Kyriba
There is a quiet revolution happening inside the world’s most forward-looking finance organizations, and most treasury teams are watching it from the hallway. Artificial intelligence is no longer a pilot program, a buzzword in a vendor deck, or a problem for IT to solve. Treasury AI adoption is actively reshaping how cash is managed, how risk is quantified, and how financial leadership earns its seat at the strategic table.
The question is no longer whether AI will transform treasury. It already is. According to Kyriba’s 2026 CFO Survey, approximately 92% of global CFOs are integrating AI into some of their processes and decision-making. The only question is whether yours will be among those driving that transformation, or scrambling to catch up with those who did.
Let’s be honest about what the hesitation often looks like: concern about data security, skepticism about ROI, uncertainty about where to start, and (perhaps most dangerously) the comfort of the familiar. These are understandable instincts. Treasury has always been a function defined by precision and caution. But those same instincts, left unchecked, become an existential liability when the competitive landscape is moving at the speed of AI adoption.
Consider what’s at stake. While your team manually reconciles bank statements and consolidates cash positions across 40 entities, an AI-enabled competitor is receiving real-time global liquidity intelligence before the market opens. While your analysts are building this week’s FX exposure model in Excel, a peer institution’s system has already flagged a correlated risk in a currency pair you haven’t touched yet. The gap isn’t just operational efficiency; it is strategic foresight. And in treasury, the cost of delayed foresight is measured in basis points, counterparty risk, and missed working capital optimization, sometimes in the tens of millions.
Waiting for AI to be “proven” in treasury is like waiting for the internet to be proven in banking. The proof is already in, and the cost is being paid by those still waiting.
Strip away the hype and the financial case for treasury AI adoption is concrete and compounding. Cash flow forecasting, historically the most labor-intensive and least accurate function in the treasury toolkit, is being transformed by machine learning models that learn from ERP data, payment patterns, and external signals simultaneously. According to Kyriba customer data, organizations deploying AI forecasting are reporting 30-50% improvements in forecast accuracy, translating directly into lower precautionary cash buffers and higher yield on deployed liquidity.
On the risk side, AI is enabling dynamic FX hedging programs that adjust in near-real-time to exposure changes rather than quarterly rebalancing cycles, a structural advantage in volatile macro environments. For organizations with complex intercompany structures, AI-powered netting and pooling optimization is consistently surfacing working capital improvements that manual treasury operations simply cannot detect at the required speed or granularity.
Treasury teams deploying AI automation in cash positioning, payment processing, and reporting are reclaiming 15-25 hours per analyst per week, time that elite treasury organizations are reinvesting into capital structure strategy, M&A support, and board-level financial risk advisory. That is not incremental improvement. That is a fundamental repositioning of what treasury contributes to the enterprise.
The AI fears circulating in treasury circles deserve acknowledgment, but not accommodation. Concerns about model explainability are legitimate; the answer is to demand transparency from vendors and build internal AI literacy, not to abstain. Concerns about data security are valid; the answer is rigorous governance frameworks, not a blanket moratorium on adoption. Concerns about job displacement deserve a thoughtful response: treasury teams that adopt AI don’t shrink; they evolve. The analysts who once built cash reports become the strategists who interpret AI-generated intelligence for the CFO and the board.
What is worth examining honestly is whether vague discomfort is masquerading as prudent risk management. Every month a treasury organization delays AI adoption is a month of compounding disadvantage: in forecast quality, in working capital efficiency, in FX risk management, and ultimately in the credibility of finance leadership as a strategic partner to the business.
CFOs and treasurers are uniquely positioned to lead enterprise AI adoption, not just within finance, but as a model for the broader organization. Treasury sits at the intersection of data, risk, and strategy. The function already commands the systems, the governance instincts, and the cross-functional relationships needed to deploy AI responsibly and at scale. The organizations that seize this moment will not simply become more efficient treasury departments. They will become the intelligence engines of their enterprises, providing the real-time financial visibility and predictive risk insight that transforms how the C-suite makes decisions.
That is a future worth leaning into. The technology is mature enough to deliver. The business case is clear enough to defend. The only variable that remains is leadership conviction.
Imagine a treasury team that had fully embraced AI in cash forecasting, FX risk, liquidity optimization, and reporting. How much more would they know? How much faster would they move? And how much more would the business trust them with? If the honest answer unsettles you even slightly… what, specifically, is holding you back?
Treasury Masterminds is a community of professionals working in treasury management or those interested in learning more about various topics related to treasury management, including cash management, foreign exchange management, and payments. To register and connect with Treasury professionals, click the button below.
