The gap between AI ambition and actual deployment in supply chain management is widening, according to new research sponsored by Kinaxis, a cloud-based supply chain software firm. Despite near-universal adoption of AI tools, a majority of leaders remain skeptical of the technology's judgment, creating a bottleneck that could stall industry progress. Main Developments An IDC InfoBrief survey of more than 2,000 supply chain leaders across nine markets revealed that only 12 percent have fully embedded AI governance into their operations. Meanwhile, 52 percent cite lack of trust in AI-driven decisions as a primary barrier, even as 41 percent expect autonomous supply chains to become their core operating model within one to two years. This trust deficit persists despite high adoption rates. The research identified that just 12 percent of respondents consider themselves AI leaders, with 62 percent saying better data quality and integration would spur more investment, and 51 percent demanding a clear return on investment. Read also: Why Fashion's Hidden Chemical Risk Demands a Consumer-Facing Fix Kinaxis's field chief technology officer, Justin King, emphasized that the industry has moved past the question of whether to adopt AI. He pointed to the company's Maestro platform, which aims to make every AI recommendation explainable and auditable before it acts, ensuring accountability at the decision level rather than merely at the policy level. Background Kinaxis, which provides supply chain management and orchestration solutions, commissioned the IDC research to understand how companies are navigating AI integration. The findings arrive as businesses across sectors race to deploy AI, but the supply chain—with its complex web of suppliers, logistics, and demand forecasting—presents unique challenges for reliable automation. Previous industry reports have highlighted the potential of AI to optimize operations, yet the persistent trust gap suggests that many organizations are still in early stages of maturity. The research indicates that while AI tools are widely used, their integration into core planning and decision-making processes remains shallow, with governance structures lagging behind technological adoption. Why It Matters The disconnect between AI adoption and trust has significant implications for supply chain resilience and efficiency. Without confidence in AI-driven decisions, companies may hesitate to automate critical functions, missing opportunities to reduce costs, improve speed, and respond to disruptions. The research suggests that unproven value and data quality issues are compounding the problem, with leaders seeking clearer evidence before scaling AI. As Eric Thompson, IDC's research director for global supply chain planning, noted, the next phase of supply chain AI hinges on accountability—ensuring AI delivers trusted decisions, measurable value, and governed autonomy. For businesses, this means investing not just in AI technology but in the data infrastructure and governance frameworks that make it trustworthy. What's Next Companies that want to close the trust gap will likely need to prioritize explainable AI systems and demonstrate clear ROI through pilot projects. Kinaxis's Maestro platform is one example of how vendors are responding, but broader industry adoption will depend on whether organizations can prove AI's value in real-world settings. With 41 percent of leaders expecting autonomous supply chains within two years, the pressure is on to accelerate governance and data quality improvements. The coming months may see more companies investing in AI auditability and integration as they strive to turn ambition into operational reality.