As market disturbances intensify, AI adoption in finance accelerates, transforming cash flow forecasting, supplier management, and treasury automation to boost agility and resilience in uncertain times.
For decades, CFOs and treasurers have depended on traditional tools like spreadsheets, manual reconciliations, and financial judgement to manage working capital, the vital cycle of receivables, payables, and inventory that maintains business solvency. However, in todayâ€...
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™s fast-evolving and volatile markets, these legacy approaches are increasingly inadequate. Businesses now face fluctuations in interest rates, supply chain disruptions, and unpredictable demand shifts, which require more agile and data-driven cash management strategies.
Artificial intelligence (AI) is emerging as a transformative force in finance, promising to revolutionise working capital management. According to the 2025/2026 Growth Corporates Working Capital Index, a joint report from Visa and PYMNTS Intelligence, 43% of surveyed companies are planning to adopt AI solutions to support growth initiatives within the next few years. This shift goes beyond simple forecasting enhancements; AI is reshaping the entire working capital cycle, enabling CFOs and treasurers to respond dynamically to real-time changes and anticipate potential disruptions.
AI integrates diverse data sources, such as ERP systems, bank statements, market sentiment, and customer behaviour, to produce continuously updated cash flow forecasts. Unlike traditional static models that rely solely on historical data and fixed assumptions, AI-driven models adapt in real time, recalculating liquidity positions as new information emerges. This dynamic ability is crucial in navigating cash volatility. For instance, if a key customer delays payment or raw material costs spike unexpectedly, AI tools can instantly revise forecasts and recommend strategic responses such as adjusting payment terms or accessing credit facilities.
Sudipto Das, Vice President of Engineering at Convera, highlights how AI empowers finance leaders to pivot swiftly by offering unprecedented flexibility, a significant improvement over slower, manual processes. AI also enhances treasury functions beyond forecasting; agentic AI systems are capable of autonomously initiating payments, performing bank reconciliations, and assessing credit lines within predefined rules, automating routine tasks and reducing error rates. This automation enables treasury teams to redirect focus from administrative duties to strategic decision-making.
Supplier management is another area benefiting from AI advancements. Large corporations managing thousands of suppliers face complex, labour-intensive verification and compliance processes. Generative AI now automates much of this work by analysing supplier documents, cross-referencing with external data, and generating standardised summaries for review. These AI systems can also monitor supplier risk in real time, alerting finance teams if a supplier’s creditworthiness declines or if they appear on sanctions lists, thereby mitigating supply chain risks proactively.
Despite these clear advantages, challenges remain. The effectiveness of AI depends heavily on data quality and integration. Many organisations contend with fragmented systems across geographies or departments, limiting the holistic view necessary for maximising AI’s potential. Vishal Arora, Head of Generative AI and ML for Payments at AWS, characterises AI as an evolution rather than a revolution, emphasizing the need for careful experimentation, robust data infrastructure, and disciplined automation strategies.
In broader industry research, AI’s impact on financial operations is further reinforced. JPMorgan has documented AI’s capacity to integrate real-time data across ERP, CRM, and market feeds, dynamically modelling cash flow and enabling more accurate scenario analyses and stress testing. Academic studies also highlight pioneering AI frameworks that extend beyond forecasting into the automation of end-to-end financial processes, including budget planning, payment transfers, and financial reporting. These cutting-edge AI agents demonstrate significant improvements in processing times and error reduction, underlining AI’s potential to streamline complex treasury workflows.
Additionally, AI-powered platforms capable of processing multimodal financial data, from numerical to textual information, are achieving remarkable returns in trading and risk prediction, showing that AI’s benefits in finance extend well beyond liquidity management. The integration of large language models and multi-agent orchestration further enhances decision-making accuracy and responsiveness to market conditions.
As CFOs and treasurers look towards 2026 and beyond, the adoption of AI-driven solutions is expected to be a defining factor in managing cash volatility and driving competitive advantage. The growing capabilities of AI empower finance teams with agility, precision, and automation, transitioning working capital management from a reactive chore into a proactive strategic function. Companies embracing these technologies are poised not only to improve operational efficiency but also to strengthen resilience amid global economic uncertainties, ultimately enabling sustained growth and financial health in a rapidly changing world.
Source: Noah Wire Services
