Why is Poor Gen AI use Causing Supply Chain Efficiency Gaps?

Generative AI is increasingly recognised as a transformational tool for supply chain operations, yet procurement teams are struggling to move beyond experimental phases.

EFESO Management Consultants has published its annual CPO Pulse Report, exploring how procurement leaders are implementing gen AI and identifying factors causing delays in wider adoption.

According to the 2026 Annual CPO Pulse Report, The State of Generative AI in Procurement, while experimentation with gen AI is now systematic across supply chain functions, only 5% of procurement teams have successfully industrialised these technologies.

Though leaders recognise the value gen AI could bring, there is a notable delay in applying it to areas where it can offer proven value to procurement processes.

Report findings

The CPO Pulse Report offers an in-depth analysis using qualitative interviews with 50 Chief Procurement Officers. The study explores how gen AI is being adopted across supply chain procurement, where value is coming from and why execution remains a challenge.

Generative AI is now understood to be a potential driver of business success through increases in efficiency, productivity and cost savings. However, while it is being tested across organisations, there remains a barrier to large-scale deployment.

As few as 5% of procurement functions have successfully utilised industrial gen AI across their supply chain operations. Though widely discussed as a priority, large-scale deployment is limited. Many have not yet attempted implementation, with 75% of organisations still in the experimentation phase. Of this, 40% are in early exploration and 35% are running pilot projects.

Only 20% report having partial deployment across their operations. Through this study, a growing gap between aims and execution has revealed itself.

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Gen AI limitations across supply chains

Supply chain leaders are seeing value creation from gen AI, but it remains limited to clearly defined areas. It is demonstrably successful across contract analysis and summarisation (69%), followed by supplier and market intelligence (61%) and automation of supplier sourcing processes (55%). These productivity-oriented use cases benefit from lower integration risk and high data density.

However, more complex uses demonstrate issues, as only 35% of procurement leaders see added value in AI-assisted negotiation. These solutions require more thorough data integration and stronger governance, demonstrating that procurement leaders do not trust gen AI's capabilities for more advanced requirements.

"What we are observing is not a lack of interest, but a rise in discipline," explains Gaël Sandrin, Principal at EFESO Management Consultants.

"CPOs are no longer asking whether generative AI works, but where it works, at what cost and under what conditions."

Leading organisations are now turning towards clearly defined use cases where there is proof of tangible impact from gen AI: contract analysis, supplier intelligence, spend classification and category insights. Companies that recognise where to successfully apply gen AI are moving beyond the pilot stage selectively.

Changing mindsets

Not all procurement leaders are satisfied or confident with the technology and its results. Only 34% of leaders report being satisfied with the value generated relative to initial investments, with 46% being partially satisfied and 20% reporting feeling disappointed.

Leaders are also facing barriers to scale when it comes to trust, showing concerns surrounding data reliability (68%), regulatory compliance and confidentiality (67%), skills shortages (57%) and data quality limitations (55%).

EFESO Management Consultants believe this represents a growing maturity across the sector rather than a slowdown in gen AI uptake. Functions are moving from generalised experimentation towards selective industrialisation.

The shift demonstrates an alignment of feasibility, data readiness, economic credibility and governance. Leaders are now looking to deploy gen AI where it can meaningfully support supply chain performance, rather than apply it across operations for the sake of it.