A growing number of supply chain leaders are embracing artificial intelligence, but many still lack the governance needed to turn enthusiasm into dependable operational results, according to new IDC research.
The survey, which covered more than 2,000 supply chain leaders across nine global markets, suggests that the industry is moving quickly towards AI-enabled decision-making without first putting in place the controls required to manage risk. IDC found that only 6% of respond...
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ents describe their supply chains as autonomous at scale today, yet 41% expect autonomous operations to be their main model within the next one to two years.
That gap between ambition and readiness is at the heart of what IDC calls an accountability problem. Only one in eight organisations has governance fully embedded to support a broad rollout of AI, while just 12% say they are AI leaders. At the same time, 2% report having no AI-enabled capabilities at all, showing that adoption has become widespread even if maturity has not.
Trust remains the main obstacle. More than half of respondents, 52%, said confidence in AI-driven decisions is the biggest barrier to faster adoption. When asked about the risks of autonomous or agent-based AI, they pointed first to data quality and integration, followed by governance and accountability, incorrect decisions, loss of human control, lack of transparency and cost.
The research also shows that supply chain teams want clearer proof that AI can generate measurable value. Sixty-two per cent said better data quality and integration would encourage more investment, while 51% wanted clearer returns and faster payback. IDC also found that 67% believe accountability for AI outcomes will require the biggest change in governance practices.
Eric Thompson, research director for global supply chain planning at IDC, said in the report that the next phase of supply chain AI is about more than simple uptake. He said the priority is accountability, with AI expected to produce trusted decisions, measurable value and governed autonomy.
The findings align with broader industry caution. Gartner said in May that only 17% of supply chain organisations are pursuing immediate transformational redesigns, with most instead adding AI gradually to specific use cases. In a separate survey, Gartner found that technology integration, legacy systems and limited internal expertise remain major barriers to scaling AI across supply chains.
IDC has also argued in earlier research that many firms face an “intelligence gap”, where AI systems are being asked to act faster than organisations can verify the underlying data. That problem is echoed in SAP’s summary of IDC research, which found that although most companies now have AI strategies, only a small minority have achieved meaningful enterprise-level execution.
For Kinaxis, which sponsored the IDC study, the answer lies in building explainability and auditability into AI systems from the outset. Justin King, the company’s field chief technology officer, said its Maestro platform was designed so that AI recommendations can be explained and reviewed before they are carried out.
Even so, the wider message from IDC is that supply chain AI is no longer simply a story of adoption. It is becoming a test of whether companies can govern automation well enough to trust it, prove its value and keep human oversight in place as decision-making becomes increasingly machine-assisted.
Source: Noah Wire Services
