Many corporate treasury teams still manage core liquidity functions on Excel despite two decades of digital finance investment. That operational gap is limiting how quickly enterprises can deploy artificial intelligence across mission-critical cash and risk systems.
Ashish Kumar, head of Infosys Oracle Sales for North America, and CM Grover, Chief Executive Officer of IBS FinTech, recently outlined the issue in a discussion on enterprise finance transformation. IBS FinTech, which has operated for 19 years and ranks among the top five treasury technology providers globally according to IDC, said many chief financial officers still rely on spreadsheet-based workflows. Grover described this as a structural bottleneck inside the CFO’s office.
Why Data Integration Determines AI Success?
Treasury teams manage liquidity, foreign exchange exposure, commodity risk, and short-term investments. Yet in many enterprises, traders execute transactions on platforms such as Bloomberg or Reuters, then manually re-enter data into spreadsheets before posting accounting entries into enterprise resource planning (ERP) systems. Can artificial intelligence deliver predictive insights if the underlying data is fragmented and manually keyed?
Grover argued that AI cannot function without digitized, automated datasets feeding it in real time.
“It is not by talking you can do AI in treasury,” he said, adding that companies must first create structured and automated data pipelines.
IBS FinTech built its systems on Oracle databases and integrates with Oracle Cloud, NetSuite, and Fusion to connect treasury platforms directly with ERP systems, trading venues, and banks.
Kumar added that modernizing treasury architecture strengthens financial resilience as geopolitical and macroeconomic volatility intensifies across commodities, foreign exchange, and equities. Executives are increasingly auditing internal data workflows to identify manual breakpoints that could compromise compliance monitoring or liquidity management.
The next phase of enterprise treasury digitization will hinge on whether firms replace spreadsheet-based controls with integrated systems capable of real-time reporting, automated reconciliation, and audit visibility. Adoption metrics across large multinational CFO offices will serve as the clearest indicator of how quickly AI-native treasury models move from pilot projects to core infrastructure.
