New research reveals that only 10% of companies have operational AI in their supply chains, with progress hampered by data gaps, siloed information, and governance issues, highlighting the need for improved data architecture and partner integration.
AI is still largely confined to experiments in supply chain operations, held back less by scepticism than by gaps in the data and systems that feed it. New research from Sage shows that only 10% of companies have moved artif...
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icial intelligence into live supply‑chain workflows; most remain in proof‑of‑concept or pilot stages, using machine learning in narrow planning or decision‑support roles rather than for autonomous execution, according to Sage’s 2026 State of Supply Chain Report.
The report finds that the decisive factor separating pilots from scaled deployments is visibility. Organisations reporting strong multi‑tier and in‑transit insight are almost three times more likely to be piloting or running AI than those without it, Sage says. That visibility must extend beyond parcel tracking dashboards to consistent, timely information on supplier capacity, inventory positions, orders and movements that flows into planning, procurement and logistics systems without fragmentation. Where data is siloed, delayed or incomplete, algorithms lack the stable signals they need, producing outputs that planners distrust.
Industry observers say investment is shifting accordingly. Companies accelerating AI uptake are also committing funds to control towers, event‑management platforms and supply‑chain data hubs that normalise feeds from carriers, contract manufacturers and downstream partners. Sage itself is banking on those improvements: the vendor has introduced AI features in its X3 offering to give mid‑sized firms real‑time insights across finance, sales and supply‑chain operations, aiming to reduce manual work and support quicker, more confident responses to disruption, the company says.
Governance is emerging as the second gatekeeper. Teams that treat AI recommendations as high‑stakes inputs typically implement versioning, audit trails and clear override protocols so human planners retain final authority. That blend of oversight and modelling helps manage explainability and compliance risks, particularly where recommendations affect customer commitments, pricing or regulated products.
Not all research paints the same picture of readiness. A study by Epicor and Nucleus Research reports that 56% of supply‑chain businesses consider themselves highly AI‑ready and are moving from pilots toward operational integration, signalling that some sectors or firms have already modernised data systems to scale machine learning. Conversely, Gartner found in 2025 that only 23% of supply‑chain organisations had a formal AI strategy, warning that a narrow focus on short‑term ROI could undermine broader transformation.
Geography and policy also matter. AllAboutAI’s 2026 figures show markedly higher operational AI adoption in countries such as South Korea and the UAE, where national strategies and concentrated investments, particularly around port logistics and free‑trade hubs, have pushed adoption above 50%. That contrast suggests networks tied into advanced digital infrastructure and trade corridors will pull ahead.
The practical consequence of slow, uneven adoption is a rising performance divide. Sage and other analysts warn that companies building robust visibility and data discipline now will not only deploy AI earlier but will iterate and improve models faster as live feedback compounds learning. Firms that defer foundational work may face a costlier, more disruptive catch‑up, potentially requiring wholesale redesigns of how data flows across suppliers, carriers and internal systems.
The immediate managerial task is therefore clear: treat data architecture and partner integration as the priority investments that enable trustworthy AI, while pairing algorithms with governance that preserves human judgment. Those that do will be better placed to translate pilot promise into operational advantage; those that do not risk being left behind as peers capitalise on live‑system learning and tighter, AI‑enabled orchestration.
Source: Noah Wire Services
