Artificial intelligence is moving from a useful back-office tool to a central part of supply chain risk strategy, according to executives taking part in EY discussions on resilience. Rather than focusing only on labour savings, senior leaders are increasingly viewing AI as a way to detect weakness earlier, improve judgement and connect risks across functions before they escalate into operational disruption.
Juan Uro, EY Americas leader for the CEL, said the conversation among C...
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-suite leaders has shifted markedly. Companies first encouraged experimentation, then wider adoption, and now the focus is on scaling. That, he noted, brings a harder reckoning with cost, governance and operating models. In practice, the debate has moved beyond whether AI can write an email or speed up a routine task. It is now about how it can reshape risk management itself.
For supply chain leaders, one of the most compelling arguments is AI’s ability to spot faint warning signs. Executives described a world in which models can identify patterns, sentiment changes and emerging vulnerabilities before they become visible to managers. In one example shared during the EY discussions, a consumer goods executive said AI could help leaders “look around those corners”. The point, the executive added, is not merely to cut costs, but to think more strategically about what the technology can reveal.
That broader framing is echoed by the growing number of specialist platforms entering the market. Infis AI says its command centre is designed to predict supplier disruption, automate procurement choices and protect manufacturing operations through real-time visibility and risk analytics. Intellectyx AI pitches similar capabilities, with agents monitoring suppliers, logistics networks and operational data to flag problems early. Traceage, meanwhile, says its analytics can provide risk scoring, anomaly detection and forecasting using existing traceability data, while Resilinc markets AI tools that automate risk scoring and estimate revenue impact. The common theme is that AI is increasingly being positioned as a decision aid, not just an efficiency tool.
EY’s discussions also reflected a broader shift in how executives think about return on investment. One chemicals chief operating officer said much transactional work has already been moved into shared services, meaning the value of AI is no longer best measured by headcount reduction. Instead, the real prize is expanding the capacity of existing teams. Another leader said their organisation had deliberately avoided treating AI as a pure efficiency play, preferring to use it to improve the speed and quality of decisions. A manufacturing executive went further, arguing that AI’s value lies in its ability to process many sources of information at once, around the clock, in ways people cannot.
There is also a demographic dimension to the case for adoption. One executive pointed to falling birth rates in Western economies as a reason to rethink how work is done and how agentic AI might be integrated into operations. That concern is beginning to resonate more widely as companies confront tighter labour pools and more complex supply networks.
Yet the same leaders warned that turning AI into something genuinely useful requires discipline. The technology may promise resilience, but it also demands top-down governance, clear process redesign and active change management. EY’s Uro argued that executives need to think about AI not only in terms of cost but in terms of value creation, including how processes are re-engineered and operating models adapted.
The most striking shift may be in how risk itself is being defined. A former chief operating officer, speaking in a boardroom context, said the question has evolved from how AI might pay for itself through savings to how it might reduce exposure. That means bringing together supply chain, finance and technology leaders to identify which risk areas can be connected and compressed. Uro said boards are increasingly pressing COOs and chief information officers between quarterly meetings to test whether risk components are being linked properly across the business.
Still, the human challenge remains substantial. Several executives said employees are uneasy about AI when it moves beyond simple drafting or admin support. Some fear it will replace their role; others are already using AI so extensively that their jobs have changed beyond recognition. One manufacturer’s supply chain leader compared the situation with collaborative robots on the factory floor, which were once expected to destroy jobs but ultimately coexisted with higher overall manufacturing employment.
That tension captures the current moment for supply chains. AI is no longer being discussed simply as a way to do old tasks faster. For many executives, it is becoming a tool for sensing risk earlier, making better decisions with the same or smaller teams and building a more resilient operating model. The challenge now is not whether AI belongs in supply chain management, but how quickly companies can govern and scale it without losing control of the risks it is meant to reduce.
Source: Noah Wire Services
