Artificial intelligence has moved beyond a niche research tool to a mainstream driver of operational change across the logistics landscape. The launch of generative AI in late 2022 marked a pivotal moment, redefining productivity for every supply‑chain function. As the next wave—[agentic AI](https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-future-of-work-is-agentic "Building and managing an agentic AI workforce | McKinsey")—gains traction, the industry can expect even faster, more autonomous transformations, particularly in workforce design.
Today’s AI systems are not only streamlining routine tasks; they are reshaping how talent is developed and deployed. In the past, a junior analyst might spend hours troubleshooting failed transactions, crafting scripts to cleanse data, or crafting responses to support tickets. Modern AI can perform these activities in seconds, while agentic AI takes the next step by acting as an autonomous assistant that anticipates problems and initiates corrective actions. Throughout the process, the system keeps human stakeholders informed, ensuring transparency and accountability.
This shift raises a critical question for supply‑chain leaders: what becomes of the entry‑level roles that historically served as the training ground for future managers? While AI frees senior professionals to focus on strategic decision‑making, it simultaneously erodes the supportive roles that provide hands‑on learning. The loss of these early‑career positions threatens the long‑term resilience of the supply‑chain talent pipeline. Historically, junior roles have been the most reliable mechanism for cultivating future leaders, offering real‑world exposure that cannot be replicated in a classroom.
The World Economic Forum projects that AI will generate a net gain of 78 million jobs by 2035, yet it will also displace 9 million, with 40 percent of employers anticipating workforce reductions due to automation. Even as AI drives efficiencies, the sector will still require operations managers, integration architects, and other skilled professionals. Without the foundational experience that entry‑level roles provide, the next generation may lack the practical knowledge needed to fill these critical positions.
AI is not a threat but an opportunity that demands proactive workforce planning. Supply‑chain leaders must now rebuild the career pathways that AI is quietly diminishing. The first step is to redesign how early‑career talent is trained and advanced, ensuring that the skills required for tomorrow’s roles are cultivated today.
Rather than relying on a traditional apprenticeship model, modern supply‑chain education should adopt structured, risk‑managed learning pathways. By pairing junior staff with AI mentors that guide them through realistic scenarios, organizations can provide the same depth of experience that civil engineers gain from supervised bridge‑building projects, but with minimal operational risk.
Equally important is the investment in domain fluency. A recent jobs report highlighted a gap between the software skills listed in job descriptions and the deeper understanding of supply‑chain dynamics that employers truly need. Only 54 percent of positions required software knowledge, and AI was mentioned in a mere 2 percent of job descriptions. This disconnect underscores the necessity of moving beyond surface‑level tool proficiency to cultivate intuitive, systems‑level thinking.
Creating feedback loops is another essential component of a future‑ready workforce. Even as agentic AI takes on autonomous actions, human oversight remains critical. By embedding junior staff into review cycles—where they evaluate AI decisions, question the rationale, and assess outcomes—organizations can foster judgment and analytical rigor. This collaborative approach ensures that the next generation of leaders develops the ability to reason through uncertainty, a skill that no algorithm can fully replicate.
In the years ahead, supply‑chain operations will increasingly run on AI, but leadership will remain human. Those leaders must understand not only how systems operate but why they behave the way they do. The capacity to troubleshoot, to lead through ambiguity, and to make judgment calls will never be fully automated.
Supply‑chain executives should act now, before the erosion of foundational roles erodes the very talent that will steer future innovations. By investing in structured apprenticeships, deepening domain fluency, and embedding feedback loops, the industry can preserve its talent pipeline while harnessing the full power of AI. The future of logistics depends on this balanced partnership between technology and human expertise.
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