FourKites®, the leader in AI-driven supply chain transformation, today released research in partnership with ABI Research highlighting a critical disconnect in enterprise AI deployment. While 28% of supply chain executives say working capital optimization is their top investment driver, only 37% use AI for risk management, the very function that prevents costly disruptions, detention fees, and expedited freight.
The report, “The Execution Gap: What Supply Chain Leaders Are Saying About Technology,” surveyed 490 supply chain professionals across manufacturing, retail, and logistics. It revealed that companies are prioritizing AI in predictable areas like demand forecasting (44%) and inventory management (41%), while avoiding the unpredictable disruptions that actually lock up cash.
“Executives want working capital improvements, yet they deploy AI for forecasting instead of disruption prevention. They’re analyzing problems instead of preventing them,” said Mathew Elenjickal, CEO of FourKites. “The 27% of organizations willing to let AI act autonomously can prevent detention fees, eliminate expedited freight, and reduce safety stock—direct hits to the balance sheet through AI that acts, not just analyzes.”
Key Findings:
- Working capital optimization leads investment priorities at 27.6%, nearly double competitive advantage (14.9%) and triple sustainability (8.4%), yet AI deployment is misaligned.
- Only 27% allow AI to take autonomous action; 52% restrict it to decision support, preventing real-time operational fixes.
- Integration challenges outweigh data quality concerns, with 46% citing legacy system fit as a top workflow barrier.
Autonomous AI Drives Results
Among the 156 respondents who fully embrace autonomous AI, working capital improvements are tangible. These organizations use AI agents to automatically prevent detention fees, manage exceptions proactively, and eliminate emergency freight—converting insight into immediate financial impact. Competitors, by contrast, remain hesitant, limiting AI to forecasting and analysis rather than action.
Ryan Wiggin, Senior Analyst at ABI Research, emphasized, “Success requires data interoperability, defined processes for action, and organizational readiness. Without these, AI investments fail to meet strategic goals like working capital optimization.”
The research underscores a growing imperative for supply chains: to fully capture AI’s potential, companies must go beyond predictive analytics and empower autonomous systems to act in real time. Those that do are already gaining a measurable competitive and financial edge.
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