India, Sep 03: According to EY India’s latest report, ‘An Agentic AI Adoption Playbook for CFOs and Treasurers’, treasury functions continue to spend 60%-70% of their bandwidth on manual and low-value activities, limiting their ability to focus on strategic priorities. Forecast variance in spreadsheet-led treasury environments often exceeds 20%, highlighting the challenges of managing liquidity using fragmented systems and legacy processes. The report finds that agentic AI-enabled treasury models can improve forecast accuracy to as much as 90% across 30-, 60- and 90-day liquidity horizons, helping organizations make faster and more informed decisions.
Spreadsheets continue to dominate treasury operations
Despite significant investment in treasury technologies, spreadsheets continue to underpin critical treasury activities across many organizations. A mature treasury function may manage between 50 and 100 interconnected spreadsheets covering cash positioning, foreign exchange exposure, investments and regulatory reporting.
More than 50% of corporates globally continue to rely on manual reconciliation processes, creating inefficiencies and increasing operational risk. According to the EY report, this presents a significant opportunity for organizations to modernize treasury operations through workflow automation, trusted data foundations and agentic AI.
Commenting on the findings, Hemal Shah, Partner, Risk Consulting, EY India, said:
“Many treasury teams continue to rely heavily on spreadsheet-based processes at a time when organizations are seeking greater visibility, agility and control. Agentic AI presents an opportunity to move treasury from a reactive function to a predictive and intelligent operating model. However, realizing this potential will require strong data foundations, robust governance and clearly defined workflows.”
A clear case for treasury transformation
The report identifies workflow transformation as the critical first step in successful agentic AI adoption. Organizations that implement digital breaks and workflow automation are already realizing measurable outcomes, including 80%-90% auto-match rates in reconciliation processes, as per EY India analysis. One of the most common reasons AI initiatives fail to scale in treasury functions is the absence of a reliable and governed data architecture.
EY’s recommended approach centers on building a treasury data-lake that serves as a single source of truth by bringing together structured and unstructured data from ERP systems, banking platforms, contracts, emails and market information.
High-impact use cases emerge for early adoption
The EY report identifies cash forecasting, reconciliation and KYC/AML exception handling as the most promising starting points for agentic AI adoption. Among these, cash forecasting offers the greatest potential business impact, with AI-enabled models capable of significantly improving forecasting accuracy and liquidity visibility. The report also finds that AI agents can manage 70%-80% of routine KYC/AML exception cases with full auditability, enabling treasury and risk teams to focus on higher-value activities.
The role of the Treasury Center of Excellence
To support long-term transformation, the report advocates the establishment of a Treasury Center of Excellence, responsible for managing datalake pipelines, workflow libraries and data governance frameworks
As treasury functions become increasingly data-driven and interconnected, organizations have an opportunity to reimagine how liquidity, risk and operational efficiency are managed. The report suggests that companies that combine workflow automation, trusted data foundations and strong governance frameworks will be best positioned to realize the benefits of agentic AI and build more resilient treasury operations