According to a study by Gartner, AI is currently being used only to a limited extent to drive change in corporate processes and workflows. At the Gartner Supply Chain Symposium/XPO in Orlando on May 4–6, it was noted that the reason for this is a number of challenges that hinder and limit the opportunities for implementing AI.
Gartner’s study on AI strategies in the supply chain included 140 senior supply chain decision-makers. The results showed that only 17 percent of the participating companies are currently striving to transform their supply chain processes and workflows, and that the remaining 83 percent are either implementing specific use cases and pilot projects in a phased manner or gradually scaling up their use of AI by integrating the technology into their existing processes.
”Few have integrated AI into their core processes”
Although growing geopolitical instability is creating new disruptions that are fueling interest in more AI-orchestrated supply chains, Gartner expert Caleb Thomson noted that most companies are forced to adopt a strategy of developing their use of AI in stages.
– Persistent volatility is driving interest in evaluating AI-orchestrated capabilities, but investments are still limited by gaps in the underlying conditions. Even among leading supply chain companies that have succeeded in improving their performance with AI, few have truly integrated AI into their core processes, notes Caleb Thomson, Senior Director Analyst at Gartner’s Supply Chain practice.
Typical shortcomings in the basic conditions include technical deficiencies, the need for staff training, and a fragmented landscape of technology providers, which together limit the successful adoption and use of AI technology.
Today's Challenges for AI-Driven Orchestration
Unlike traditional tools for supply chain planning and execution, Gartner emphasizes that AI-driven orchestration makes it possible to continuously monitor events in supply networks and simulate various courses of action. This, in turn, enables rapid collaboration between humans and AI, allowing critical decisions to be made quickly. AI-driven supply chain orchestration enhances the ability to anticipate and respond to disruptions through end-to-end visibility across the supply network, cross-functional analysis, and increased automation powered by AI agents.
– Today’s technical and organizational challenges should not be a reason to delay the development of the underlying capabilities needed for AI-driven supply chain orchestration. Our research shows that the business value will be transformative, as it lays the foundation for future agent-based orchestration across entire supply chain networks, explains Caleb Thomson, who, during the Gartner Supply Chain Symposium/XPO, summarized the following factors that hinder the broader adoption of AI-driven transformation in the supply chain:
- Fragmented supplier landscapes—multiple tools for planning, visibility, and analysis.
- Data gaps limit adaptability—poor data quality, with many organizations struggling with the quality of basic master data.
- Inconsistent partner data – data quality challenges extend across the entire supply network, as information from trading partners is often incomplete or inaccurate.
- Human expertise remains crucial—achieving greater decision-making autonomy requires continuous professional development and gradual implementation, in which AI enhances, rather than replaces, human judgment.
- Process maturity is fundamental—clear processes, roles, and standardized data models are crucial for enabling orchestration and effective decision-making.
