Supply chains’ new normal: how AI is enabling resilience in a permanent state of disruption

Supply chain disruption is no longer an exception to manage. It has become a permanent operating condition.

Since 2020, organisations have faced continuous shocks; from geopolitical conflict and climate-driven events to labour shortages and volatile demand. What many leaders once viewed as temporary instability has evolved into an enduring reality that is reshaping how supply networks operate.

Speaking on a CIO webcast, Raja Chandrashekar, Senior Partner, Operations Practice Lead, at Fujitsu’s consulting business Uvance Wayfinders Americas said: “The predictability of just-in-time supply chains has been broken. Companies need to deal with supply certainty challenges, demand volatility and the need to maintain reliability and resilience, which has fundamentally rewritten the rules of supply chain.”

For decades, supply chains were optimised for cost and efficiency. Today’s environment demands a different operating model, one built for resilience, visibility and rapid response.

This shift is not purely technological. It requires integrating operational expertise with data and AI capabilities to respond intelligently to change. It also requires rethinking workflows, decision rights and collaboration across extended supplier networks.

Modern supply chains operate far beyond enterprise boundaries. Layers of outsourcing and specialisation have created fragmented systems and opaque information flows, making it difficult to see risk beyond first-tier suppliers. New data capabilities and AI tools are beginning to connect these fragmented signals, improving visibility across complex ecosystems.

Many organisations have attempted to manage volatility with dashboards, reporting tools and faster planning cycles. Yet decision-making often still lags behind events.

“The main challenge is that decision models today are built on a much slower, less dynamic world,” said Sherif Goma, senior partner, data and AI, Uvance Wayfinders Americas. “By the time a practitioner looks at a dashboard and takes action, that decision window has already expired.”

At the same time, the volume and diversity of supply chain data have grown exponentially. Information arrives from suppliers, logistics providers, market signals, weather systems and geopolitical developments — often in incompatible formats and at different speeds. This widening gap between complexity and responsiveness is where AI is creating practical value.

AI as an enabler of human decision-making

While discussions about autonomous supply chains often dominate headlines, the most immediate value of AI lies in improving decision speed and insight. AI can automate bounded decisions within defined thresholds, detect anomalies and generate scenario models that evaluate trade-offs across cost, service levels, sustainability and risk. This allows planners to focus on higher-value decisions while gaining visibility into options that were previously too complex or time-consuming to evaluate.

Equally important, AI is most effective when it augments human judgment rather than replaces it. Humans remain essential for defining risk tolerance, managing supplier relationships and making strategic trade-offs.

What should leaders do now?

As supply chains grow more dynamic, leaders must rethink not only technology investments but also how decisions are made. A practical first step is mapping supply chain decision points to determine which should remain human-led and which can be augmented or automated. Strategic decisions such as supplier diversification, risk tolerance and crisis escalation require human judgment, while AI can support demand sensing, anomaly detection, scenario modelling and automated responses within defined parameters.

Organisations must also bring their people along. Introducing AI as a decision-support tool helps build trust and familiarity, paving the way for deeper collaboration between human expertise and intelligent systems.

In a world where disruption is inevitable, the goal is no longer perfect prediction. It is building supply networks that can see more clearly, respond more quickly and adapt intelligently when change arrives.

Watch the other videos in this series, on data sovereignty and AI trust, now.