The adoption of AI in enterprise treasury management is enabling organizations to move away from manual spreadsheets toward automated, real-time data pipelines.

Corporate finance departments are under increasing pressure from market volatility, regulatory requirements, and the growing demand for digital finance transformation. Recently, Ashish Kumar, Head of Infosys Oracle Sales for North America, and CM Grover, CEO of IBS FinTech, discussed the evolving challenges facing corporate treasury teams.

IBS FinTech, with 19 years of experience and ranked among the top five globally by IDC, highlights a critical gap within many CFO offices: treasury management is still often handled through Excel spreadsheets, despite advancements in AI across other business functions.

Treasury teams are responsible for managing cash flow, liquidity, and financial risk. Organizations engaged in imports and exports face foreign exchange exposure, along with commodity-related risks. Companies with surplus cash must also strategically invest to generate returns.

However, a major challenge for many enterprises is the lack of real-time data integration. Treasury teams frequently execute trades on platforms such as Bloomberg, Reuters, or 360T, then manually input the transaction data into spreadsheets before posting entries into enterprise resource planning (ERP) systems. This manual workflow creates inefficiencies and increases the risk of errors.

Successful AI implementation in treasury operations depends on eliminating these manual bottlenecks. While many leaders view AI as a quick solution, its effectiveness relies on a strong foundation of digitized and automated data. As Grover emphasized, AI cannot be implemented in treasury without first building a structured, automated data environment.

Integrating treasury management systems (TMS) with existing ERP platforms is essential to establishing this foundation. IBS FinTech built its backend on Oracle databases and now integrates with Oracle Cloud, NetSuite, and Fusion applications to ensure seamless data flow.

A connected financial ecosystem requires direct communication between treasury management systems, ERP platforms, trading systems, and banking partners. Such integration provides executives with accurate, real-time visibility into liquidity, risk exposure, and compliance.

With geopolitical and economic uncertainties expected to increase global volatility across commodities, equities, and foreign exchange markets, automation and real-time data systems are becoming strategic necessities.

Kumar emphasized that modernizing treasury management through AI and ERP integration strengthens financial resilience. Enterprise leaders should carefully evaluate their current data workflows. If treasury processes still depend on manual data transfers between trading platforms and ERP systems, AI initiatives are likely to fail due to poor data quality.

Direct system integrations that enable real-time, error-free data flow are the essential groundwork for successful AI adoption in enterprise treasury management.