A new report from FourKites and ABI Research finds that while working capital optimization tops corporate priorities, most organizations deploy AI in the wrong places, focusing on forecasting instead of the disruptions that drain cash from operations. The findings reveal a widening execution gap between firms using AI for insight and those using it for action.
AI Investments Skew Toward Forecasting
Although 28% of executives cite working capital optimization as their top investment goal, only 37% use AI for risk management, the function most directly tied to cash preservation. The study, “The Execution Gap: What Supply Chain Leaders Are Saying About Technology,” highlights that detention fees, emergency freight, and unplanned safety stock remain major drains on liquidity precisely because most organizations use AI to analyze rather than intervene.
“Executives want working capital improvements, yet they deploy AI for demand forecasting instead of disruption prevention,” said Mathew Elenjickal, founder and CEO of FourKites, in an official statement. “The 27% of companies that use AI for autonomous execution can avoid detention fees before they occur, cut expedited freight by managing exceptions in real time, and reduce safety stock through more reliable operations.”
The report shows a clear divide between insight-driven and execution-driven strategies. While 44% of companies use AI for demand forecasting and 41% for inventory management, few apply it to the unpredictable disruptions that actually immobilize working capital. Only 27% allow AI to make autonomous decisions, while more than half restrict it to decision support, limiting its potential to act on emerging issues before they turn into balance sheet losses.
Legacy Systems Still Block Real-Time AI Value
Integration hurdles continue to outpace data quality concerns as the main constraint to AI’s operational impact. Nearly half of respondents (46%) cite legacy system integration and tool fit as their primary obstacles. This finding aligns with other recent supply chain technology research showing that disconnected data flows and outdated ERP systems are slowing AI adoption at scale.
The FourKites, ABI study also found that the 156 organizations that “strongly agree” with autonomous decision-making outperform their peers in working capital efficiency. By connecting AI agents to live operational data, these firms are automatically preventing detention costs, rebalancing freight proactively, and reducing emergency shipments, achievements that translate directly into working capital gains.
AI’s Next Benchmark
The next wave of AI adoption in supply chains won’t be judged by accuracy in forecasting or anomaly detection, it will be measured by how effectively AI systems preserve liquidity. As working capital grows tighter amid fluctuating demand and tariff volatility, AI that can autonomously execute, rerouting freight, adjusting supplier orders, or reallocating inventory in real time, will function as a new form of financial control. In effect, supply chains are entering an era where cash management and AI strategy are inseparable disciplines.
