Just 23% of supply chain organizations have a formal AI strategy in place, according to new survey data from Gartner. While most companies are experimenting with AI, few are building the governance structures or funding models needed to scale these tools across the enterprise. The findings suggest that AI adoption in the supply chain remains largely tactical, prioritizing fast ROI over foundational change.
AI Use Is Widespread, But Planning Lags
Gartner’s research highlights a growing gap between AI’s potential and how it is currently deployed. Most chief supply chain officers (CSCOs) are investing in AI through isolated, short-term initiatives rather than through coordinated strategies. This project-by-project approach may yield quick efficiency gains but often fails to support long-term scalability, data harmonization, or cross-functional impact.
“CSCOs are under pressure to show early returns, but that urgency can undermine future flexibility,” said Benjamin Jury, senior principal analyst in Gartner’s Supply Chain practice in an official statement. He warned that without formal AI governance and investment roadmaps, companies risk building fragmented systems that are costly to maintain and difficult to evolve.
Efficiency Over Innovation
The survey also revealed that most organizations evaluate AI based on bottom-line improvements, cost reduction, faster decisions, and efficiency, rather than innovation, revenue growth, or business model evolution. That emphasis may limit AI’s strategic role, keeping it in a supporting function rather than as a lever for competitive differentiation.
This narrow focus may reflect organizational maturity. Companies still early in their digital transformation tend to emphasize operational efficiency. But Gartner’s previous studies have shown that advanced adopters of AI often realize greater returns when they apply it to more ambitious use cases, such as intelligent planning, autonomous supply networks, or predictive risk management.
Tactical AI May Weaken Long-Term Resilience
Overreliance on short-term AI projects may weaken supply chains in the long run. Point solutions often lack interoperability and fail to integrate with broader planning and execution systems.
Without a cohesive strategy, companies could end up with a patchwork of tools that are difficult to govern, audit, or reconfigure when market conditions change. As AI systems take on more autonomous decision-making, fragmented deployment could also heighten compliance and accountability risks, issues already flagged by audit and risk committees in recent regulatory discussions.
