FINANCE
CASH
is the one figure a Chief Financial Officer can’ t afford to get wrong. Cash or liquidity forecasting is the most challenging task treasury professionals face, according to 62 % of respondents in the 2025 AFP Treasury Benchmarking Survey. Yet most businesses still work from a number that is already out of date before it comes close to a spreadsheet. Traditional 13-week cash flow forecasts – the standard treasury instrument for eons – are rebuilt manually most weeks. Analysts pull receivables data from the enterprise resource planning system, then payables schedules from finance and bank balances from the previous day’ s statements. The result? The numbers reflect the business as it stood a day earlier, not as it stands at that moment. Cash flow forecasting is the single biggest liquidity management challenge for 2026, according to more than a third of Chief Financial Officers and treasurers surveyed by J. P. Morgan across Asia Pacific.
73 %
of practioners find cash management and forecasting rank as the top departmental priority
Source: AFP
The( lack of) accuracy problem Manual forecasting has three structural weaknesses: it’ s stale, it’ s fragmented across disconnected systems and it’ s difficult to interrogate when a number moves without explanation. The same AFP survey found cash management and forecasting rank as the top departmental priority for 73 % of practitioners, regardless of rank. While the most mature treasury departments automate more than half of the processes behind building a liquidity forecast, most departments fall well short of that. And a forecast that can’ t be trusted forces finance teams to hold larger cash buffers than they really need. It also slows decisions on hedging, investment and credit facility drawdowns.
From static report to live system As with just about every other aspect of business, artificial intelligence is changing the shape of the problem. AI-powered forecasting agents now sit on top of existing treasury and ERP systems, pulling live data from banks, ledgers and subledgers into a single, continuously updated model.
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