[ INSIGHT ] ARTIFICIALINTELLIGENCE
by Eric Lefebvre Chief Engineering Officer, JAGGAER elefebvre@jaggaer.com | 919-659-2100
Rewiring the Supply Chain for the AI Era
AI promises a level of precision and agility that traditional systems simply cannot achieve. While this technology’s potential is enormous, the question remains: Are supply chains truly prepared to embrace it?
risk signals, making robust governance frameworks indispensable. These should define clear lines of accountability, ensure transparency in decision- making and provide mechanisms for human review, covering data privacy, explainability and bias detection while aligning with regulatory standards. 5. Protect the digital supply chain. The more connected a supply chain becomes, the more vulnerable it is to disruption. Defensive strategies must therefore evolve alongside digital transformation, and organizations should adopt a “secure-by-design” approach, embedding cybersecurity protocols from the earliest stages of AI deployment, from encrypting training data to monitoring third-party integrations and safeguarding AI models against manipulation or data poisoning. Regular testing, employee awareness campaigns, and incident-response planning are equally critical. AI’s potential to reshape the supply chain is undeniable, but technology alone cannot deliver transformation. True readiness lies at the intersection of reliable data, skilled people, strong ethics, and resilient infrastructure. For organizations, this means intentional adoption aligned with business objectives, measuring performance by outcomes rather than outputs, and fostering collaboration across the supply chain ecosystem.
contrast, incorporate domain expertise into their design. For example, machine learning models trained on transport data can optimize fleet utilization and fuel efficiency, while AI can continuously evaluate supplier performance against ethical, financial, and environmental criteria. 3. People power. Perhaps the most underestimated aspect of AI readiness lies in people and as AI systems take on data-intensive work such as forecasting or scheduling, employees’ roles will evolve. This transition will shift emphasis from manual execution to interpretation, oversight and strategy, requiring both technical fluency and critical thinking. Continuous learning is essential to help staff understand not only how to use AI tools but also why they produce certain outcomes, and when to question them. 4. Build trust by design. AI can make decisions faster than any human but faster doesn’t necessarily mean better. Algorithms trained on biased data or lacking contextual understanding can lead to distorted outcomes, from unfair supplier assessment to misinterpreted
To unlock the full power of AI, supply chain leaders need to prepare across five critical areas: data, technology, people, ethics, and security. 1. The data dilemma. No AI model, no matter how sophisticated, can outperform the quality of the data it learns from. Supply chains are inherently data-rich environments, however that data is often dispersed across incompatible systems, suppliers, and geographies. For AI to deliver trustworthy insights, data must be consistent, clean and connected. Establishing a unified data architecture by standardizing formats, creating shared taxonomies and ensuring interoperability between systems and partners, is therefore the first and most important step. 2. Select technology with purpose, not fashion. AI in the supply chain should be seen as a toolkit of technologies, each suited to a specific challenge. Generic platforms rarely account for sector-specific nuances, such as fluctuating lead times or evolving compliance requirements. Industry-specific AI solutions, by
20 Inbound Logistics • March 2026
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