Corporate treasury teams—which manage a company’s cash, payments, investments and foreign exchange exposure—currently spend 60-70 per cent of their time on manual and low-value work, the report said.
In companies that rely heavily on spreadsheets, the difference between forecast and actual cash positions can exceed 20 per cent, it added.
The report, ‘An Agentic AI Adoption Playbook for CFOs and Treasurers’, said agentic AI models can achieve up to 90 per cent accuracy in 30-, 60- and 90-day cash forecasts.
Treasury teams still rely on spreadsheets
According to the report, spreadsheets remain widely used for treasury work despite investments in specialised technology.
A mature treasury team may use 50-100 linked spreadsheets for tasks such as tracking cash, foreign exchange exposure, investments and regulatory reporting. More than half of companies globally also continue to use manual reconciliation processes, the report said.
“Many treasury teams continue to rely heavily on spreadsheet-based processes at a time when organisations are seeking greater visibility, agility and control,” Hemal Shah, Partner, Risk Consulting, EY India, said.
“Agentic AI presents an opportunity to move treasury from a reactive function to a predictive and intelligent operating model. However, realising this potential will require strong data foundations, robust governance and clearly defined workflows,” Shah added.
Data remains a hurdle
The report said companies need reliable, governed data systems before they can scale AI in treasury operations.
Organisations that have introduced workflow automation and digital breaks are achieving 80 per cent – 90 per cent auto-match rates in reconciliation, according to the EY India analysis.
The report also identified the absence of reliable and governed data architecture as a common reason for AI projects not scaling in treasury.
EY recommended creating a treasury data lake that brings together data from enterprise resource planning systems, banking platforms, contracts, emails and market information.
Where else could AI be used
The report identified cash forecasting, reconciliation and checks related to customer identity and anti-money laundering rules as areas where companies could start using agentic AI.
Cash forecasting has the greatest potential business impact among these areas, according to the report. AI agents could also handle 70 per cent – 80 per cent of routine cases involving such checks, with an audit trail, it said.
EY also recommended setting up a Treasury Centre of Excellence to manage the company’s data systems, workflows and data governance.
The report is based on EY India’s work with treasury teams in the manufacturing, financial services and infrastructure sectors.
