The AI-skilled supply-chain talent gap requires role redesign, not hiring alone

Rajavel Sekaran highlights a rapid increase in demand for professionals who combine supply-chain domain knowledge with practical AI skills. Citing Gartner’s analysis of 35 million job postings, he reports that demand for AI-skilled supply-chain roles has increased by 387% since the first quarter of 2023.

The gap reflects three concurrent changes. AI is being introduced across demand forecasting, supplier evaluation and logistics; organizations need people who understand both operational decisions and AI; and traditional recruitment and training pipelines are not adapting quickly enough. Sekaran also points to retiring experienced workers and the resulting loss of institutional knowledge.

His recommendations for chief supply-chain officers are to develop AI fluency internally, redesign roles created before the current AI transition and work with external partners when the required technology and implementation capabilities cannot be built quickly enough.

Operational perspective: workforce transformation should begin with decisions and responsibilities rather than generic AI training. Organizations need to identify which planning, procurement, logistics and execution tasks will be supported or performed by AI, who remains accountable for the outcome and what evidence employees must review before accepting a recommendation.

Training should use the organization’s own processes, data and failure scenarios. Role descriptions should distinguish model use, validation, escalation and governance responsibilities. Critical institutional knowledge should also be documented in approved sources with named owners, version control and review procedures before it is embedded in AI agents or decision-support tools.