Why 94% of Supply Chains Plan to Deploy AI While Only 23% Have a Strategy

The uncomfortable part of the AI-powered supply chain story is no longer whether leaders are interested. They are. ABI Research found that 94% of surveyed supply chain professionals planned to deploy AI for decision support within two years, and 76% saw potential in supplier management agents specifically.[1] The harder question is whether those deployments are being governed by anything sturdy enough to survive contact with planning meetings, supplier escalations, data exceptions, and margin pressure.

That is where the numbers become less celebratory. Gartner reported that only 23% of supply chain organizations had a formal AI strategy, even among organizations already deploying AI.[2] The ABI and Gartner figures should not be treated as a perfectly matched equation; they come from separate surveys with different samples and methodologies. Still, they point in the same direction. Intent is abundant. Strategy is scarce.

PwC’s 2026 operations research adds a third signal. In its survey, 57% of operations leaders said their organizations had integrated AI somewhere, but only 27% had fully embedded it across business units. Just 4% met PwC’s high-performer definition across all measured dimensions.[3] That last figure is easy to misuse as a scare statistic. It is more useful as a description of how narrow the path to scaled value still is.

The ambition gap is an operating problem, not a messaging problem

Supply chain leaders are being asked to show AI progress while also protecting service levels, working capital, cost-to-serve, planner productivity, supplier performance, and customer commitments. That pressure produces a familiar pattern: a forecasting pilot in one region, a supplier-risk dashboard in procurement, a warehouse optimization proof of concept, a generative AI experiment for customer updates, and a few executive slides that make the portfolio look more coherent than it really is.

None of those projects is automatically wasteful. Many are sensible starting points. The problem is that a pile of AI activity does not become an operating model just because the use cases all mention the same technology. A supply chain organization needs to be able to answer a more prosaic set of questions: which decisions will AI support, what data will it use, who owns the changed process, how will planners and operators adopt it, and which business outcomes will determine whether it worked?

This is why the 94% and 23% figures belong in the same conversation, even though they do not come from the same study. The first captures appetite for AI-assisted decisions. The second captures the absence of a documented mechanism for deciding where AI should matter, how it should be deployed, and how it should be measured. That gap is where promising pilots become stranded systems.

There is already a broader version of this issue in the supply chain AI adoption paradox: leaders keep reporting strong belief in AI while many organizations struggle to move from isolated deployment to repeatable value. The 2026 version is sharper because the tolerance for experimentation without accountability is shrinking.

What project-by-project AI usually skips

The missing middle is not glamorous. It is the connective tissue between a model and a working supply chain process. Four areas show up repeatedly: data governance, use-case prioritization, workforce upskilling, and operating model redesign. They should not be treated as four equal boxes on a transformation slide. In practice, they compound.

Data governance comes first because AI decision support is only as useful as the decision data it can trust. A demand-planning assistant that draws from inconsistent product hierarchies, late shipment updates, or poorly maintained supplier records will not become more reliable because the interface is elegant. It may simply make bad assumptions easier to distribute. This is why data readiness work, including ownership, lineage, exception handling, and master-data discipline, deserves more attention than it usually receives in AI launch plans. For a deeper operational view, see the data foundations that warehouse AI actually needs and the CSCO’s data readiness checklist.

Use-case prioritization is the next weak point. A vendor demo may show that AI can produce an impressive recommendation. It does not answer whether that recommendation belongs near the top of the supply chain agenda. Leaders need to rank use cases by business criticality, decision frequency, data availability, integration effort, user readiness, and the size of the operating change required. A supplier management agent may be attractive because it promises faster risk sensing and follow-up. A replenishment recommendation engine may be more valuable if it directly changes stockouts, expedite costs, or inventory buffers. The right answer depends on the business problem, not on which demo looked most polished.

Workforce upskilling is often framed too narrowly as training people to use a new tool. The bigger issue is judgment. Planners, buyers, logistics managers, and supply assurance teams need to know when to accept an AI recommendation, when to challenge it, when to escalate, and how to document the override. If that behavior is not designed, AI becomes either ignored or blindly followed. Both outcomes can damage credibility.

Operating model redesign is where many programs quietly fail. A forecast signal that no one is accountable to act on is not decision support. A supplier-risk alert that procurement, planning, and logistics all see but no one owns is notification clutter. AI changes the flow of work only when decision rights, exception paths, meeting cadences, system integrations, and performance metrics change with it.

That is why the common project-by-project route is so fragile. It can prove technical feasibility without proving organizational readiness. It can deliver a narrow local benefit while creating another dependency for an already stretched planning team. It can also produce what looks like momentum until someone asks why the pilot did not scale. The pattern is examined in more detail in From Pilot to Profit and why AI in supply chain fails.

The 4% outliers are not just “better at AI”

PwC’s 4% high-performer cohort matters because it is not defined by a single shiny metric. The group met several conditions at once: AI embedded across business units, lower barriers to scaling, horizontal operating structures, and technology investments that were delivering expected results.[3] That makes the figure less sensational and more instructive. These organizations are not merely buying more AI. They appear to be arranging the business so that AI can change work across boundaries.

That distinction matters in supply chain because value rarely sits inside one function. A demand signal affects supply planning. Supply constraints affect customer allocation. Supplier disruption affects production sequencing, freight decisions, inventory positioning, and revenue risk. If AI is embedded in one function but the operating structure remains vertical, the recommendation can stall at the handoff.

PwC also found that 89% of operations leaders said technology investments had not fully delivered expected results, with integration complexity, data quality, and user adoption among the top blockers.[3] Those blockers are not edge cases. They are the normal work of scaling. A formal AI strategy does not make them disappear, but it gives leaders a place to assign ownership before the program is judged by results it was never structurally prepared to deliver.

It is also worth saying plainly that having a strategy is not enough. The 23% with a formal strategy can still fail if that strategy is generic, disconnected from business priorities, or unsupported by the operating changes required to make AI useful. A documented strategy is the starting condition, not the prize.

A practical roadmap starts with decisions, not tools

For a CSCO or VP of Supply Chain, the roadmap does not need to begin with a grand AI manifesto. It should begin with the decisions that matter most to the business. Which decisions are currently too slow, too manual, too reactive, or too inconsistent? Which ones have measurable consequences for service, cost, inventory, working capital, revenue protection, or resilience? Which ones are frequent enough that better decision support would compound?

The table is intentionally plain. Most failed scaling efforts do not collapse because nobody knew AI was exciting. They collapse because the organization never made these choices explicit. The result is a set of tools searching for owners.

Gartner’s Run-Grow-Transform logic is useful here because it prevents two common mistakes: chasing only quick wins or jumping straight to long-term reinvention.[2] “Run” work can target immediate operational friction, such as exception triage, planner workload, or supplier follow-up. “Grow” work can improve cross-functional decisions, such as inventory positioning or fulfillment prioritization. “Transform” work can redesign how planning, procurement, logistics, and operations coordinate around AI-supported workflows.

The balance matters. If every AI initiative must prove a short-term ROI case in isolation, leaders can end up with disconnected point solutions that add complexity faster than they remove it. If every initiative is framed as transformation, teams may wait too long for value and lose confidence. The better path is a portfolio that makes room for near-term operational wins while building the data, integration, and governance foundation required for larger changes. For leaders under pressure to time expectations, the real timeline for AI supply chain ROI is a useful companion.

What leaders should stop accepting as proof of progress

A supply chain AI program should not be judged primarily by the number of pilots launched, the number of users who saw a demo, or the theoretical productivity improvement in a vendor presentation. Those are activity measures. They may be useful early, but they cannot carry the burden of strategy.

A more serious review asks whether the organization can name the decisions being changed, the process owners accountable for adoption, the data dependencies that could break trust, the user behaviors required for value, and the metrics that will determine success. It also asks what will be stopped. If AI is added on top of every existing meeting, spreadsheet, approval loop, and manual reconciliation step, teams will experience it as another layer of work.

This is especially important for agentic decision support and supplier management agents. The promise is real: agents can help monitor events, surface exceptions, draft recommended actions, and reduce manual coordination. But in a supply chain context, the cost of a poor recommendation is not abstract. Someone may expedite freight, short a customer, change a production sequence, or escalate a supplier unnecessarily. Governance has to define where the agent advises, where a human approves, and where the system is allowed to act.

The organizations most likely to benefit from AI are not the ones that perform the most AI enthusiasm. They are the ones that make AI boring enough to manage: decision scope, data ownership, workflow design, adoption accountability, and outcome measurement. For teams still diagnosing the blockers, the five barriers to supply chain AI adoption is a practical next read.

The constraint in 2026 is discipline

The evidence does not support a simple story that supply chains are waiting for AI to become useful. The stronger reading is that many organizations are moving faster on deployment intent than on the management system needed to scale it. ABI’s deployment-interest signal, Gartner’s formal-strategy gap, and PwC’s outlier cohort are not one statistical proof. They are independent warnings from different angles.

Supply chain AI in 2026 is not mainly constrained by model capability or spending appetite. It is constrained by the absence of a formal strategy that can turn scattered AI activity into scaled operating change. Until leaders close that gap, many AI-powered supply chain programs will keep producing motion that looks impressive from the outside and feels unresolved to the teams expected to run the business with it.

References

1. Artificial Intelligence (AI) in Supply Chain Survey Results, ABI Research, 2025.
2. Gartner Survey Shows Just 23% of Supply Chain Organizations Have a Formal AI Strategy, Gartner, June 11, 2025.
3. 2026 Digital Trends in Operations, PwC.