How does AI influence treasury?
Treasury teams have no shortage of data. Bank balances, payment activity, invoices, receivables, orders, shipments, market data, forecasts, and business-unit plans all contain information that could influence the next liquidity or cash decision.
The problem is that this information rarely arrives together.
It is distributed across banks, ERP systems, treasury management systems (TMS), payment platforms, business processes, spreadsheets, and external partners. That fragmentation makes it harder for treasury to understand not just what has happened, but what it means and what should happen next.
This is where AI in treasury management has significant potential. But getting value from AI starts with something less glamorous: connecting the information that gives AI the context to be useful. Because as we all know: AI is only as useful as the context behind it.
To get value from AI you must begin with something less glamorous: connecting the information that gives AI the context...
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