Treasury Operations Stuck in Spreadsheets as Agentic AI Offers 90% Forecast Accuracy: EY India

Spreadsheets still eat up 70% of treasury teams' time as agentic AI promises sharper forecasts

Treasury teams spend much time on manual tasks, hindering strategic work. Spreadsheet use causes forecast variances, impacting liquidity management significantly. Agentic AI can improve cash forecast accuracy to ninety percent. Workflow automation and AI offer scope for transformation and efficiency gains. Companies need strong data foundations and governance for successful AI adoption. Treasury teams are still spending as much as 60%-70% of their time on manual and low-value tasks, leaving little room for strategic work, according to a new report by EY India. The report, An Agentic AI Adoption Playbook for CFOs and Treasurers, said spreadsheet-driven treasury operations can see forecast variances of more than 20%, making it harder for companies to manage liquidity effectively. Agentic AI could change that. EY said AI-enabled treasury models can improve cash forecast accuracy to as much as 90% across 30-, 60- and 90-day liquidity horizons, allowing companies to make faster decisions around cash and liquidity. Despite growing investments in treasury technology, spreadsheets remain deeply embedded in day-to-day operations. A mature treasury function can have 50-100 interconnected spreadsheets covering cash positions, foreign exchange exposure, investments and regulatory reporting, the report said. More than half of corporates globally also continue to rely on manual reconciliation, according to EY. This creates inefficiencies and increases operational risks, while also highlighting the scope for workflow automation and AI-led transformation. "Many treasury teams continue to rely heavily on spreadsheet-based processes at a time when organizations are seeking greater visibility, agility and control," Hemal Shah, partner, risk consulting, EY India, said. Agentic AI can help move treasury operations from a reactive to a more predictive model, Shah said, but companies will need strong data foundations, governance and clearly defined workflows to make it work. EY said workflow transformation should be the first step for companies looking to deploy agentic AI in treasury. Organisations that have already introduced digital breaks and workflow automation are seeing 80%-90% auto-match rates in reconciliation, according to EY India analysis. The report identified cash forecasting, reconciliation and KYC/AML exception handling as some of the most promising early use cases for agentic AI. Cash forecasting could deliver the biggest business impact, EY said, while AI agents could also handle 70%-80% of routine KYC/AML exception cases with full auditability. This could allow treasury and risk teams to focus on more complex tasks. But getting there will require companies to first fix their data architecture. EY recommended building a treasury data lake that acts as a single source of truth by bringing together data from ERP systems, banking platforms, contracts, emails and market information. The report also recommended setting up a Treasury Center of Excellence to oversee data pipelines, workflow libraries and governance frameworks.

EY India Report: Agentic AI to Boost Treasury Forecast Accuracy up to 90%

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 (CoE), 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