Artificial intelligence is transitioning from conference topic to practical treasury tool. This chapter examines real-world applications emerging across cash flow forecasting, intercompany lending, fraud detection, and working capital optimisation.

Contributors from BNP Paribas, PwC, SAP, and EACT discuss the data foundations required for AI success, the distinction between machine learning and generative AI, and why orchestration matters more than individual algorithms. The analysis covers intelligent workflow design, agent-based models, and the governance frameworks necessary to build trust in AI outputs. Crucially, the chapter addresses how smaller treasury teams can adopt AI incrementally without wholesale transformation.

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