Supply Chain AI Faces 'Accountability Gap' as Autonomy Ambitions Surge
An IDC study reveals a stark disconnect between the rush toward autonomous operations and the governance frameworks needed to manage them.
Supply chain leaders are racing toward autonomous operations while leaving critical governance and trust frameworks behind. A new IDC InfoBrief, sponsored by Kinaxis, warns of a widening "accountability gap" that threatens to undermine the rapid adoption of artificial intelligence across the industry.
According to the study, which surveyed over 2,000 supply chain leaders across nine global markets, AI adoption is now nearly universal; only 2% of respondents report having no AI-enabled capabilities. However, this ubiquity has not been matched by operational trust. Half of the respondents (52%) identify trust in AI-driven decisions as a primary barrier to faster adoption.
The most acute tension exists in the move toward autonomy. While only 6% of organizations are currently autonomous at scale, 41% expect this to be their core operating model within the next one to two years. This push for autonomy is occurring in an environment of minimal oversight: the research found that only 12% of organizations have fully embedded AI planning governance within their operations. This creates a precarious situation where the ambition for scale far outpaces the ability to control it.
Industry leaders acknowledge that current structures are insufficient. Approximately 67% of surveyed leaders state that establishing accountability for AI-driven outcomes will require the most significant changes to their governance models. This shift indicates that the primary bottleneck for the industry is no longer the availability of the technology itself, but the human and organizational frameworks required to manage it.
Moving toward autonomous-at-scale models without embedded governance exposes companies to high risks of costly, unmanaged failures. When AI makes decisions without a clear accountability trail, the potential for operational drift or systemic error increases. As companies move past the initial experimentation phase, the focus is shifting from simple adoption to the realization of actual ROI.
Eric Thompson, Research Director for Global Supply Chain Planning at IDC, notes that the next phase of AI is not about more adoption, but about accountability—ensuring that AI delivers trusted decisions and governed autonomy. Justin King, Field CTO at Kinaxis, echoed this sentiment, stating that the central question is no longer whether AI is adopted, but whether it delivers measurable value and trusted outcomes.
To bridge the accountability gap, organizations must prioritize the integration of governance into the core of their operational models rather than treating it as a secondary compliance task. The industry must now determine how to assign responsibility for AI-driven outcomes and create the guardrails necessary for safe autonomy.
Observers will be watching to see if the 41% of companies aiming for scale can build these frameworks in time to meet their 24-month goals, or if the lack of trust and governance will force a slowdown in the transition to autonomous supply chains.