How do you choose an AI treasury platform?

Judge an AI treasury platform on five criteria: whether AI is native or bolted on, whether the model learns from corrections, whether every output traces to source data, integration coverage for your banks and ERP, and time to value. Ask for variance data by horizon, not one headline accuracy claim.

The five tests that separate real AI from marketing AI

1. Architecture. Is AI native to the platform, or added to a system designed 15 years ago? Can it run without a rules engine underneath?
2. Learning. Does it improve from your corrections automatically, and retain that knowledge without manual retraining?
3. Data foundation. Is your data consolidated and clean, or fragmented across systems the AI cannot reach?
4. Explainability. Does it show why, and trace every prediction to source transactions?
5. Precision. Does it deliver reproducible outcomes, or probabilistic best guesses? Treasury needs arithmetic executed in code, not synthesised by a language model.

Questions that get past the demo

• Show me variance reduction against actuals, by forecast horizon and cash category.
• Which of my banks and ERPs do you connect to in production today, not on the roadmap?
• Can my team override the model, and does it learn from the override?
• Does my data train models used by other customers?
• What happens to accuracy after an acquisition or a new entity?
• Can you run the demo on my data rather than yours?

Red flags

AI that lives mainly in the pitch deck, features that feel bolted on, a platform still dependent on hard-coded rules, no feedback loop from corrections, accuracy claims with no horizon attached, and a rollout measured in quarters. Your TMS has AI on its website. It doesn't have AI in the product. explains how to spot them.

Weight time to value properly

Most buyers underweight it. A 4-week deployment and a 24-month deployment are different categories of decision, not two points on one scale. Compare implementation timelines across the 8 best treasury platforms for cash forecasting before you shortlist.

Benchmark yourself first

Run a Forecasting Health-Check to benchmark your current variance before you evaluate anyone. Without that number, every vendor claim is unfalsifiable. Then see what customers achieved in the case studies, or talk to the team.

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