10 November 2025

A new Economist Impact study, commissioned by Kinaxis, finds that global companies are accelerating AI adoption to manage supply chain risk — but few can make real-time decisions.

If the last five years taught chief supply chain officers anything, it’s that “normal” is not coming back. Tariffs, wars, inflation, labour shocks and climate extremes have turned global trade into a rolling stress test. In the scramble for resilience, artificial intelligence (AI) has become the boardroom’s favourite multi-tool.

A new global supply chain AI study from Economist Impact, commissioned by Kinaxis, shows firms are accelerating AI at pace, even as most admit they aren’t yet equipped to use it where it matters most. 

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AI adoption in supply chains: ambition outpaces execution

The survey of more than 800 senior leaders across Europe, North America and Asia-Pacific paints a clear picture of intent versus capability.

• 71% say they’ve sped up AI deployment because of tariffs, inflation and geopolitical volatility.

• 97% are experimenting with AI somewhere in the supply chain.

• Just 20% claim they can make real-time decisions — the ultimate goal of AI-driven operations.

• Only 22% have a defined AI supply chain strategy, but those that do are over three times as likely to see measurable ROI.

Underneath the topline figures, firms are comfortable with the “safe” end of AI — demand forecasting and predictive analytics — but slower to deploy it against the risks driving adoption. Predictive analytics is “fully integrated” at 52% of organisations, yet fewer than 15% use AI for supplier monitoring, anomaly detection, or geopolitical risk tracking.

Regionally, Asia-Pacific (81%) and Europe (71%) are moving faster than North America (57%), but readiness remains thin: just 11% use AI for scenario modelling, and 3% for geopolitical tracking.

Economic pressure adds urgency. 79% have passed higher costs to consumers, and three-quarters report worsening component shortages. Two-thirds of executives expect ROI from AI within a year — but fewer than half of junior leaders agree. That confidence gap is a clear implementation risk.

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Why Kinaxis commissioned the Economist Impact study

Kinaxis — a leader in AI-powered supply chain orchestration software — commissioned the independent research to explore how far AI adoption has come and where it’s still failing to deliver. The goal: highlight the gap between AI ambition and supply chain readiness.

“Disruption is no longer cyclical, it’s structural,” said Fab Brasca, SVP at Kinaxis, in a press statement. Economist Impact’s Oliver Sawbridge added that most companies “lack the data, systems, and strategies to make [AI] work in real time.”

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Supply chain AI adoption ≠ supply chain advantage

It’s easy to be dazzled by adoption numbers. The harder question is whether AI in supply chain management is actually improving decision-making. On that test, the study suggests most organisations are still in the early stages.

Three main gaps emerge:

1. Strategy before software

Only one in five firms has a coherent AI strategy for the supply chain. Those that do are far more likely to see ROI. The key isn’t the algorithm; it’s the operating model — who owns the data, how decisions are made, and how exceptions escalate.

2. Governance as a resilience enabler

Fewer than 25% expect AI-related risks to rise — suggesting overconfidence. When AI recommends rerouting production or reallocating inventory, those choices must be auditable and explainable. Governance isn’t an afterthought; it’s the foundation of trust.

3. The organisational tax

The mismatch between executive optimism and team-level realism reflects a change management challenge. Without buy-in from planners and operators, AI remains an experiment. Kinaxis and Economist Impact both flag organisational inertia as a top barrier.

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From automation to adaptation

Kinaxis describes the next stage as the “agentic AI era” — systems that can propose and, under human guardrails, execute actions. Whether or not you buy the phrasing, the direction is clear: resilience depends on continuous planning and orchestration, not quarterly reviews.

That shift requires moving from static plans to dynamic scenarios, from alerts to actionable recommendations, and from isolated optimisations to network-wide trade-offs. The study’s data suggests most firms are only beginning this transition.

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What supply chain leaders should do now

For those at the intersection of AI, logistics and manufacturing, the report offers practical focus areas:

• Prioritise high-impact use cases. Invest in supplier monitoring, anomaly detection, and scenario modelling before perfecting forecasts. These are the capabilities that build geopolitical resilience.

• Build decision traceability. Make explainability and audit trails user-friendly. If planners can’t interrogate an AI’s logic, they’ll ignore it.

• Reduce latency. Only 20% can act in real time. Stream data pipelines and link sensing, simulation, and execution tools.

• Align incentives and metrics. Measure teams on resilience KPIs — scenario coverage, recovery time, risk exposure avoided — not just cost efficiency.

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The takeaway: AI for supply chain resilience is real, but uneven

The Economist Impact–Kinaxis report is both mirror and map. It reflects a market sprinting toward AI adoption out of necessity, and it sketches the path toward a more resilient model of supply chain decision-making.

For now, most firms are wiring up the data — but not yet the muscles that act on it. Governance, transparency and cross-team collaboration will determine which companies can truly turn AI into a competitive advantage in supply chain resilience.