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.
The Shift Towards AI in Treasury
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.
The Practical Benefits of AI in Treasury
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.
Improving Forecast Accuracy
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.
Predicting the Timing of Cash Flows
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.
Scenario and Stress Testing
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.
Reducing Manual Effort
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.
Enhancing Confidence at Board and CFO Level
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.
Why Poor Data Quality Is Treasury’s Biggest AI Problem
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.
Other Obstacles to AI Adoption in Treasury
Data quality and trust issues are the dominant challenge, but not the only ones. Some of the other barriers treasury professionals must navigate include:
- Lack of explainability: When AI produces a forecast without a traceable reasoning path, it widens the trust gap. Treasury professionals need to understand not just what the model concluded, but why, especially when those outputs inform funding and liquidity decisions.
- Audit and governance concerns: Without strong governance frameworks, AI-driven forecasting cannot meet the standards required for financial reporting and regulatory compliance. An accurate audit trail is essential.
- Skills gap within treasury: AI adoption requires treasury professionals who can interpret model outputs and evaluate assumptions. However, the skills gap in the treasury team is a significant barrier to trusting AI-generated cash forecasts.
- Integration with existing data systems: When treasury systems don’t talk to each other, AI cannot perform a complete analysis. Fragmented architecture makes data harder to collect and harder to trust.
Can AI Work With Imperfect Data?
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.
Strong Foundation, Stronger Forecast
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.
Ready to see what AI-powered cash forecasting looks like when it’s built on a strong data foundation? Book a demo today.
