Enterprise AI applied to specific treasury problems.
The clearest way to understand what responsible enterprise AI looks like in treasury is to see it applied to specific problems. What follows are worked examples — drawn from the book and from practical prototype work — of how enterprise AI can support treasury operations while preserving the judgment, controls, and trust the profession requires.
Each case study reflects the same principles: multi-horizon reasoning, explainable outputs, production-safe integration with SAP, and the treasury professional firmly in the loop.
Case Study 01
Enterprise AI for the daily, tactical, and strategic horizons of treasury cash forecasting.
Cash forecasting is often the first place organizations look when they consider enterprise AI for treasury — and for good reason. It's data-rich, decision-adjacent, and touches every downstream treasury process. But it's also a place where the wrong AI approach can quietly erode the professional judgment that makes forecasts meaningful.
This case study — described in depth in Enterprise AI for Treasury and demonstrated through a working prototype — shows what a responsible approach looks like: T+1 daily cash position, multi-horizon predictions from T+7 to T+90 and beyond, forecast accuracy tracking, and explainable AI that helps treasury professionals understand what's driving the forecast.
Detailed write-up in preparation.
Case Study 02
Enterprise AI for identifying, quantifying, and supporting decisions on FX exposure and hedge effectiveness.
FX exposure and hedge management sit close to the strategic heart of corporate treasury. Every decision has consequences on the balance sheet, on cost of hedging, and on the organization's tolerance for volatility. Enterprise AI in this domain must therefore be exceptionally disciplined about explainability, accountability, and where the human professional makes the call.
This case study — also explored in Enterprise AI for Treasury — walks through how a multi-agent system can support exposure identification, quantification, and hedge recommendations while keeping the treasury professional firmly in charge of every consequential decision.
Detailed write-up in preparation.
Additional case studies drawn from the treasury domains covered in the book — liquidity management, payments, fraud detection, working capital — are in preparation.
If a specific treasury problem is on your mind and you'd welcome a conversation on how enterprise AI might apply, please get in touch.
If one of these case studies mirrors a challenge you're working on, or you'd like a second opinion on an approach you're considering, I'd be glad to hear from you.