Starting your AI journey within the treasury function

In the rapidly evolving landscape of business, the race to integrate artificial intelligence (AI) into the work of the finance team has become a strategic priority for Chief Financial Officers (CFOs) and Treasurers with established financial systems. Many treasury departments struggle with outdated cash forecasting processes that remain largely manual and reactive. Such inefficiency often results in late discovery of depleted cash reserves, which then forces emergency borrowing at premium rates, and which is not only costly but also damaging to credibility with boards of directors, investors, and the market. 

AI presents a transformative solution to those challenges by offering Treasurers and CFOs powerful tools to shift from reactive cash management to proactive, data-driven financial forecasting. 

When we surveyed a group of executives whose organizations had more than 20% in net income profitability in the past year, we found that 53% ranked finance as a major focus of AI strategic focus and investment—16 percentage points higher than the average and just behind customer insight and service. The executives of those highly profitable companies were also more than twice as likely to be using AI tools to analyze enterprise risk—another finance function activity. 

The same holds true when measuring growth: 47% of the fastest-growing companies said finance is a major focus of their AI investments compared with 37% overall. 

This article explores high-impact AI applications for Treasury and draws from our extensive research and implementation experience. We importantly point out that although AI and automation are ideally suited for accelerating task completion and although agentic AI further enhances that acceleration by making simple business decisions autonomously, the technologies remain only tools. Their effectiveness, like the effectiveness of any tool, ultimately depends on their strategic application within an organization’s business functions to maximize the value-add.

To that end, start by taking the following three steps:

1. Identify the workflows that would benefit most from AI. Typically, they will be processes that are labor-intensive, high volume, and transactional—often, remnants of outdated systems or legacy procedures. Such tasks tend to consume valuable time and resources that could otherwise be directed toward mission-critical strategic work. Good examples of such a process are transaction reconciliation and payment processing, where AI-driven automation facilitates the elimination of manual data entry, automates complex reconciliations, and creates intelligent verification systems that reduce processing time, minimize errors, and flag potential fraud patterns. 
2. Enable quick wins by harvesting low-hanging fruit such as data aggregation inefficiencies — particularly if your treasury team spends excessive time manually consolidating historical and banking data. That consolidation process can almost always be improved with AI by enabling the automatic integration of multi-country, multibank accounts into a unified forecasting platform, thereby achieving immediate efficiency gains.
3. Use AI to improve forecast accuracy—especially in cases in which precision falls below 95%. AI and advanced non-linear modeling techniques enable the automation of validation processes such as outlier evaluation and incorporate real-time data streams to significantly increase forecasting accuracy. The AI approach to forecast accuracy transforms traditional forecasting into a dynamic and precise process that delivers consistently reliable results via continuously refreshed reports such as daily profit-and-loss statements.

It is also important not to overlook two key change management steps before implementing any automation: first, define success criteria, and second, clearly understand the limitations of the automation processes that will necessitate human intervention, such as exceptions that have to be handled by humans.

To bring the foregoing recommendations to life, we consider three specific case studies in which AI and automation yielded excellent results. The studies involve cash flow forecasting, management of accounts receivable, and the use of AI to automatically extract suppliers from bank transactions with a subsequent supplier categorization.

CASE STUDY #1 
CASH FLOW FORECASTING 

As mentioned in the above framework, siloed data and inaccurate forecasts usually raise significant challenges for treasury departments, which was precisely the situation faced by a domestic healthcare company whose cash flow forecasting process was erratic and often unreliable due to fragmented data across business units. 

By implementing AI solutions that aligned with our approach—including clustering for pattern identification, regression models for prediction, and real-time anomaly detection—the company positively transformed its treasury operations. And as a result, forecast accuracy improved from 80% to nearly 90%, idle cash was reduced by more than 50% through automated bank integrations, and forecasting productivity increased by approximately 70% through statistical modeling techniques.

CASE STUDY #2 
TREASURY AND ACCOUNTS RECEIVABLE (AR) COLLECTIONS

A global technology company was experiencing some of the issues described in the above framework: the company’s AR collections were growing each year while its collection process remained manual and time-consuming. The company’s treasury analysts were spending excessive hours in tracking invoicing data and managing customer outreach, with limited visibility into customer risk profiles.

By implementing the AI approaches outlined in our framework—a classification model to assess 
default propensity, generative AI to incorporate external variables through web scraping, and natural language processing for automated document processing and communication—the company transformed its AR operations. 

The results aligned with our pro forma outcomes: past-due invoices reduced by 15%, 
and collections productivity increased by 25%.

CASE STUDY #3 
AUTOMATED SUPPLIER EXTRACTION AND CATEGORIZATION

A business-to-business/business-to-consumer (B2B/B2C) retailer was struggling with transaction processing inefficiencies. The company’s treasury team was performing manual processing of bank transactions to extract supplier names, followed by time-consuming manual categorization of the suppliers into classifications such as payroll, capital expenditures, and operational expenses. That kind of labor-intensive workflow is an ideal candidate for AI enhancement. We implemented a generative AI solution that automatically extracted supplier information from transaction data and deployed an intelligent classification system that categorized suppliers with minimal human intervention—and we did so with 96% accuracy, exceeding human performance.

Adopt an effective implementation approach

The foregoing case studies exemplify effective AI implementation in treasury operations: pragmatic, focused on specific use cases, and designed to deliver substantial return on investment in the near term while building foundations for sustainable long-term growth.
Rather than pursuing sweeping, enterprise-wide transformations, employ a strategic approach: begin with high-impact, targeted use cases; demonstrate clear value; and then scale methodically. 

The following framework illustrates our disciplined methodology for treasury AI implementation by comprising three essential phases: comprehensive assessment, scale determination, and prioritized use case piloting—each of them supported by purpose-built artifacts that guide decision-making and implementation:

CONCLUSION

As the framework and case studies illustrate, AI implementation in Treasury isn’t merely about technology adoption; it’s also about strategic transformation that delivers measurable results. The pragmatic approach outlined—from identifying high-impact workflows to enabling quick wins through data integration, to enhancing forecast accuracy—provides a clear road map for treasurers seeking to evolve beyond manual, reactive processes.

The results speak for themselves: improved forecast accuracy from 80 to greater than 90%, reduced idle cash by 50%, and decreased past-due invoices by 10 to 20% — tangible outcomes that directly and positively affect an organization’s financial performance and  
strategic positioning.

Treasury teams must not just adapt to technological change but also use it as a source of competitive advantage. Organizations just beginning this journey should develop a comprehensive strategy before implementation.

AlixPartners’ “Practical AI for CFOs” guide offers treasury-function-specific insights for building an effective AI approach from the ground up to help transform an organization’s treasury function from a transactional cost center to a strategic value driver in today’s data-driven financial landscape.

The future of treasury belongs to those who can harness the power of AI while maintaining strategic long-term focus.

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