Even as growing geopolitical volatility fuels new disruptions and drives interest in the potential of AI-orchestrated supply chains, most organizations need to continue taking an incremental approach, as gaps in data readiness, the need for employee upskilling, and fragmented vendor landscapes constrain progress on technology deployment and adoption in the near term.

“Persistent volatility is driving interest in evaluating AI‑orchestrated capabilities, but investment remains constrained by foundational readiness,” said Caleb Thomson, Senior Director Analyst in Gartner’s Supply Chain practice. “Even among leading supply chain organizations that have demonstrated success with performance gains and ROI on their AI investments, few have truly embedded AI into their core operations.”

• Fragmented vendor landscape: Supply chain orchestration is built across multiple planning, visibility and analytics tools, and not delivered through a single “one‑stop” vendor platform.
• Data gaps constrain adaptability: Orchestration depends on data quality, yet many organizations struggle with foundational master data alignment that technology alone cannot fix.
• Inconsistent partner data: Data quality challenges extend across the supply network, as information from trading partners can often be incomplete or unreliable.
• Human expertise remains essential: Achieving greater decision autonomy requires sustained upskilling and gradual adoption, with AI augmenting—not replacing—human judgment.
• Process maturity is foundational: Clear processes, aligned roles and standardized data models are critical to enabling orchestration and effective decision governance.

“Today’s technical and organizational feasibility challenges should not be reasons to delay pursuing the underlying capabilities needed for AI-powered supply chain orchestration,” said Thomson. “Gartner research shows that the business value will be transformative, as it lays the groundwork for future agentic orchestration across the end-to-end supply chain network.”