A global market study conducted by research firm IDC and sponsored by Kinaxis highlights the ongoing transformation and operational bottlenecks surrounding AI deployment in global supply chains. The study surveyed more than 2,000 supply chain and business decision-makers across nine international markets. The findings demonstrate nearly ubiquitous baseline adoption: only 2 percent of surveyed organizations currently operate without any AI capabilities in their supply chain networks. However, only 12 percent consider themselves true AI leaders.
A substantial organizational deficit is emerging around the governance of autonomous systems. Although the sector is increasingly moving toward agentic AI, only 12 percent of organizations have fully established governance and oversight frameworks for autonomous decisions. This absence of structural guardrails serves as a severe impediment to broader enterprise rollouts. More than half of respondents, exactly 52 percent, cite a lack of trust in autonomous AI decisions as their main barrier to scaling the technology.
There is a notable discrepancy between current operational maturity and projections for the immediate future. At present, only 6 percent of organizations run autonomous processes at scale. Yet 41 percent of respondents expect autonomous software agents to become the core operating model of their supply chains within the next 12 to 24 months. This anticipated leap underscores the intense modernization pressure currently shaping enterprise logistics.
The research emphasizes that moving from basic digital assistance to self-executing agents introduces fundamentally new accountability demands. Prior enterprise implementations focused primarily on passive data analysis and forecasting. When autonomous algorithms begin independently issuing purchase orders, rerouting freight or reallocating warehouse capacity, operational errors must be detected and mitigated without human friction.
To enable sustainable deployment, the report calls for a rapid professionalization of internal control and audit architectures. Organizations must establish clear validation routines, human-in-the-loop escalation paths and verifiable accountability rules to close the gap between technical capability and operational control. Without robust governance frameworks, the efficiency gains promised by autonomous agents risk stalling over internal safety and reliability concerns.