To get started:

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 Typically, they will be processes that are labor-intensive, high volume, and transactional—often, remnants of outdated systems or legacy procedures. These tasks tend to consume valuable time and resources that could otherwise be directed toward mission-critical strategic work. Good examples of such processes are transaction reconciliation and payment processing, where AI-driven automation can facilitate the elimination of manual data entry; automate complex reconciliations; and create intelligent verification systems that reduce processing time, minimize errors, and flag potential fraud.
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. AI can almost always improve such a consolidation process by automating the integration of multi-country, multibank accounts into a unified forecasting platform, thereby achieving immediate efficiency gains.
Use AI to improve forecast accuracy, especially if forecast precision currently falls below 95 percent. AI and advanced non-linear modeling techniques enable the automation of validation processes, such as outlier evaluation, and incorporate real-time data streams that can significantly increase forecasting accuracy. The AI approach to forecasting transforms traditional methods into a dynamic and precise process that delivers consistently reliable results via continuously refreshed reports such as daily profit-and-loss statements.

Two areas that many companies overlook in deploying AI: First, laying the groundwork for implementation. One key element is to define success before you start. Specific, realistic, and ambitious goals will focus your efforts; achieving them will make the case for further work. And second, making sure you clearly understand—in advance—the situations in which your automation tools will necessitate human intervention, such as exceptions in reconciliations. It is better to be surprised to discover what AI can do than to be surprised by what it cannot do.

Making AI an Effective Long-Term Investment

Treasury groups that take a disciplined, thoughtful approach to AI implementation have the opportunity to dramatically improve forecast accuracy, reduce idle cash, and minimize past-due invoices. Such tangible outcomes can directly and positively affect the company's financial performance and strategic positioning, potentially improving the treasury function's standing with senior leadership and the board. Figure 3 illustrates the ways in which a treasury team's use of AI can mature over time.

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