Artificial intelligence is moving quickly to the centre of supply chain strategy, but the industry’s appetite for adoption is running ahead of its operational foundations. Across manufacturing, retail and logistics, many companies are eager to use AI to sharpen planning, improve visibility and strengthen compliance, yet surveys and industry analysis suggest that fragmented systems, weak data quality and uneven governance continue to limit what the technology can actually deliver.
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That gap between ambition and readiness is becoming one of the defining issues in supply chain transformation. In a recent Gartner survey highlighted by Supply Chain Brain, only 17% of chief supply chain officers said they were pursuing immediate AI-driven redesigns on a transformational scale, with data readiness, employee skills and disconnected vendor landscapes among the main obstacles. Other industry surveys point in the same direction: while a large majority of organisations plan to apply AI to supply chain operations, far fewer say they are truly prepared to use it at the level needed for predictive or prescriptive decision-making.
The problem is not simply a lack of technology. It is the quality of the environment into which that technology is being introduced. Research cited by Supply AI Hub argues that the main barrier is organisational rather than technical, with data architecture, strategy, governance and process alignment doing more to determine success than the latest model or platform. In practice, AI cannot reliably improve decisions if it is built on inconsistent, incomplete or poorly connected information.
That theme is echoed in comments from Allen Jacques, business development manager at OMP, who said many firms believe they are further along than they really are. He pointed to compliance pressures and data constraints as persistent weaknesses, arguing that companies often want AI to speed up operations without first assessing whether their underlying systems are ready to support it.
Visibility is another critical pressure point. In highly regulated environments such as cargo and pharmaceutical logistics, end-to-end oversight is not just an operational advantage but a requirement for auditability and control. Jacques said OMP’s planning tools are designed to give customers and their partners visibility across inventory and other key flows, while also creating a base for future AI projects. But he also acknowledged that external partners and multiple data sources can still become weak links.
Industry research suggests this is a broader pattern. A report covered by SCMR found that manufacturers may be advancing in operational AI controls, yet remain underprepared for adversarial AI threats, regulatory audits and third-party failures. That underscores a growing reality for supply chains: the more digital they become, the more they must also invest in cyber resilience, compliance discipline and risk governance.
For now, much of the sector is still using AI in a reactive way. Systems may be able to flag events and recommend next steps, but they are not yet consistently delivering the kind of forward-looking orchestration that leaders want. The transition from retrospective analysis to predictive intelligence depends on cleaner data flows, better integration and clearer ownership across functions and partners.
The commercial lesson is becoming sharper. Companies that focus on piling on tools without fixing their foundations are likely to see diminishing returns. Those that prioritise disciplined integration, stronger governance and customer-led innovation are more likely to turn AI into a durable advantage rather than another layer of complexity. As Jacques put it, the real challenge is not simply adopting AI, but working out what organisations truly need and building it with their customers, side by side.
Source: Noah Wire Services
