The 5% Blueprint: What the Performance Elite Got Right that Eluded their Peers

There’s a clear pattern emerging from joint research by GEP and the University of Virginia’s Darden School of Business into supply chain AI adoption.

A company launches an AI initiative, runs a promising pilot, but is unable to move beyond early gains.

However, in a small but growing set of organizations, that momentum continues — pilots expand, processes stabilize, and AI becomes part of how work actually gets done. These companies, which we call the “Performance Elite”, are not waiting for the technology to mature. They are already operating at scale.

About 5% of supply chain AI initiatives have successfully moved beyond experimentation to enterprise-wide deployment, based on surveys and interviews with 180 senior supply chain executives.

In these organizations, AI is not confined to isolated use cases. It is embedded in workflows, delivering measurable improvements in productivity, response time, and accuracy.

The remaining organizations are still earlier in the journey, with 22% in pilot stages and many others building toward execution. The gap is real, but it is also instructive. It shows what is already possible.

What the 5% Understand Early

The instinct, when an AI initiative struggles to progress, is to revisit the technology. Is the model accurate enough? Do we need better data? Should we switch vendors?

The organizations that scale tend to start somewhere else. They focus first on the process itself.

The research points to a consistent pattern among high-performing companies: they are deliberate about what they choose to automate. Before introducing AI, they examine how the work flows, where decisions are made, and how consistently those decisions can be executed. When the process is unclear or fragmented, they address that first.

One executive interviewed for the study captured this simply: “Don’t try to use AI to fix a problem. Fix the process and then use AI to make it efficient.”

It is a straightforward idea. In practice, it requires discipline.

Lean Thinking, Applied to AI

What distinguishes the Performance Elite is not access to better tools, but the way they approach deployment. They apply a form of operational discipline that has long existed in manufacturing and supply chain: map the process, identify waste, standardize, and then automate.

That sequence translates directly into AI deployment.

In one documented case, a company redesigned its purchase requisition validation process before introducing automation. Transactions were segmented by complexity and risk. Low-value, highly standardized requests moved through zero-touch approval. Mid-range transactions were handled by AI agents operating within defined parameters. Complex or high-risk cases were reserved for human review.

With that structure in place, nearly 80% of transactions were auto-cleared, and productivity improved dramatically within weeks.

The result did not depend on unusually advanced technology. It depended on a workflow that could support consistent execution.

The Intelligent Value Stream

What these organizations are building is not just automation, but a different kind of workflow — one designed with AI in mind from the outset. The research describes this as an “Intelligent Value Stream,” where decisions, data, and execution paths are aligned before automation is introduced.

This approach requires a different sequence. Instead of starting with a tool and searching for a use case, teams clarify decision rights, define how exceptions should be handled, and establish where human judgment adds value. Automation is then applied to a structure that can sustain it.

For many organizations, this feels like slowing down. In practice, it allows them to move faster once deployment begins.

The Proof Is Already in Production

The most important signal from the research is not that many organizations are still in early stages. It is that a small group has already demonstrated what scaled AI in supply chain operations looks like.

They are not experimenting in isolation. They are running workflows where AI handles a significant share of decisions, where human effort is focused on exceptions, and where performance improvements extend beyond individual tasks.

The technology is no longer the limiting factor. The question now is whether organizations are prepared to do the work required to support it. The companies in the top 5% have shown that when processes are designed with discipline, AI does not stall in pilot. It scales.

The GEP–UVA Darden report, The Supply Chain AI Readiness Report: Why Operational Discipline Determines Agentic AI Success, explores Intelligent Value Streams, the scaling blueprint, and what the Performance Elite do differently.Read the report now.