Artificial intelligence is rapidly becoming embedded in global supply chain operations, but many companies appear to be moving toward autonomous decision-making faster than their governance frameworks can keep up.
A new IDC InfoBrief sponsored by supply chain orchestration company Kinaxis highlights a widening gap between organizations’ ambitions for AI-powered autonomy and their ability to ensure those systems remain trustworthy, accountable, and connected to measurable business outcomes.
The study, titled Making Supply Chain AI Accountable, surveyed more than 2,000 supply chain leaders across nine global markets.
According to the findings, AI adoption is already widespread. Only 2% of respondents said their organizations had no AI-enabled capabilities, yet just 12% considered themselves AI leaders.
The bigger challenge may emerge as companies give AI greater authority over operational decisions.
Autonomous Supply Chains Could Expand Rapidly
Only 6% of respondents said their supply chains currently operate autonomously at scale. However, 41% expect large-scale autonomous operations to become a core operating model within the next one to two years.
That anticipated shift creates a significant governance challenge.
Only 12% of surveyed organizations said AI planning governance was fully embedded across their operations, while 67% said governance would require the greatest change to establish accountability for AI-driven outcomes.
The findings suggest that the next stage of enterprise AI adoption will depend not only on what AI systems can automate, but also on whether businesses can understand, audit, and control the decisions those systems make.
Trust Remains a Major Barrier to AI Adoption
Trust was identified as one of the most significant obstacles to faster AI deployment.
More than half of respondents, or 52%, cited trust in AI-driven decisions as a key barrier to accelerating adoption.
Data infrastructure is another concern. About 62% said better data quality and integration would help accelerate AI investment, while 51% wanted clearer evidence of return on investment and the time required to generate value.
Those results point to a broader transition in enterprise AI. Companies are increasingly under pressure to demonstrate that AI investments produce measurable operational improvements rather than simply increasing the number of AI-enabled processes.
In supply chain management, those improvements could affect areas such as demand forecasting, inventory optimization, production planning, logistics, and responses to unexpected disruptions.
AI Accountability Becomes Critical as Autonomy Grows
Governance becomes particularly important as AI systems move beyond recommending actions and begin making or executing decisions with less direct human intervention.
Organizations adopting increasingly autonomous systems need mechanisms for defining decision boundaries, monitoring outcomes, maintaining human oversight, and tracing how recommendations or decisions were produced.
Kinaxis says its Maestro supply chain orchestration platform addresses some of these requirements through explainable AI, governance controls, human oversight, and tools designed to connect AI-generated insights with operational performance.
Because Kinaxis sponsored the IDC research, the study findings and the company’s own product positioning should be considered separately.
The broader issue identified by the research, however, reflects a growing challenge across enterprise AI: greater autonomy creates a corresponding need for stronger accountability, transparency, and oversight.
Supply Chain Jobs Are Expected to Change
The research also suggests that supply chain professionals largely view AI as an opportunity rather than an immediate threat to their jobs.
According to the study, 79% of respondents said they see AI as an opportunity.
As autonomous systems take responsibility for more routine analysis and operational decisions, human roles may increasingly focus on oversight, exception management, strategic judgment, and determining when automated recommendations should be challenged or overridden.
That transition could increase demand for professionals who combine AI knowledge with deep expertise in supply chain operations.
Governance Could Define the Next Phase of Supply Chain AI
For supply chain organizations, the race to deploy AI is increasingly becoming a race to govern it effectively.
The gap between the 6% of organizations currently reporting large-scale autonomous operations and the 41% expecting autonomy to become a core operating model within the next two years illustrates how quickly the industry expects to change.
Technology alone, however, will not determine whether that transition succeeds.
Data quality, explainability, auditability, human oversight, and measurable returns are likely to become increasingly important as companies allow AI systems to influence higher-value operational decisions.
The IDC findings suggest that organizations capable of building those controls alongside their AI systems could be better positioned for the next phase of autonomous supply chain management.
