How agentic AI is transforming treasury beyond spreadsheets 

CFOs and treasurers can build AI-ready treasury functions through strong data foundations, governance and trust. 

Finance and treasury functions have spent the last few decades running on spreadsheets. For much of that time, the spreadsheet was among the best tools available. That is no longer the case. The EY white paper, ‘From spreadsheet heroics to autonomous treasury: An agentic AI adoption playbook for CFOs and treasurers,’ reveals that a large share of bandwidth among treasury analysts and managers goes toward aggregating data across systems, manually validating figures against counterparty thresholds and producing reports that are frequently outdated before they reach decision-makers. A typical mature treasury function runs between 50 and 100 interconnected spreadsheets covering cash positioning, foreign exchange (FX) exposure, investment tracking, debt management and regulatory reporting. Each one is a silo with its own version control and associated risk of error. This spreadsheet dependency is not a cosmetic problem. It is a structural barrier to treasury digital transformation.

How treasury workflows change with agentic AI

The shift to agentic AI in treasury does not happen in one step. It begins at the workflow level, where digital processes replace manual handoffs with system-driven execution. At scale, these changes create smart treasury workflows where routine coordination runs without human intervention.

In FX risk management automation and interest rate risk management, agents ingest real-time exposures alongside live market data, run multiple hedge scenarios in minutes and auto-generate IFRS-compliant documentation with a full audit trail. In AI-powered cash flow planning, agents aggregate multi-bank, multi-currency balances in real time and flag funding gaps 30 to 60 days ahead with measurable forecasting accuracy.

What this means for treasury professionals

Enhancing treasury professionals’ capabilities involves giving them quicker access to information, clearer insights and decision support that highlights the right questions at the right time.

This rebalancing has four measurable consequences:

Summary 

The business case for treasury digital transformation is rarely the primary obstacle. The more significant barriers are gaps in treasury data governance, fragmented data infrastructure and the organizational tendency to scale before the foundational work is in place. CFOs and treasurers who address the data layer first, govern early pilots with appropriate rigor and expand only after outcomes have been validated will build treasury functions capable of making better-informed decisions with greater efficiency. A structured approach that incorporates maturity assessment frameworks, governance design and CoE operating models can help organizations accelerate adoption while strengthening decision-making, operational efficiency and long-term value creation.