Many organizations saw modern AI as just a futuristic concept, something interesting to keep an eye on just a few years ago. When tools like ChatGPT became more mainstream though, suddenly every team was looking for ways to work smarter. Now, indeed already, AI has already moved from hype to operational necessity. That shift happened extremely fast in supply chain tech, where today’s AI tools are making decisions, identifying issues, predicting disruptions, optimizing order flows, and more.

As AI continues to evolve and bring greater orchestrational finesse into supply chains, it’s not just impacting operations. AI is also causing ripples across the workforce. Organizations are understandably focused on harnessing these technologies to boost productivity, but they also need to ask: What are the long-term impacts on our people? Specifically, what are the impacts on how early-career roles are defined, and how are we preparing talent for the future?

The Human Cost of the Productivity Payoff

I’ve spent much of my career building and integrating platforms that power modern supply chains, and I’ve seen firsthand how entry-level roles have evolved. Only a few years ago, junior staff started their careers handling tasks like updating spreadsheets, tracking shipments manually, or calling vendors to resolve order delays. It wasn’t flashy work, but it gave new hires real exposure to how supply chains operated, from order to delivery. These mundane but crucial tasks served as hands-on training for employees to build the instincts and experience they’d need to advance in their careers.

Now, organizations are increasingly automating these tasks in the push for speed and efficiency. A recent report found that AI knowledge was only mentioned as a requirement in 2% of job descriptions, even though the technology is actively reshaping how work gets done. This disconnect exposes a deeper issue: Supply chain dependent industries like logistics, manufacturing, retail, healthcare and others, are using AI to transform the work but not yet how employees succeed in the workforce.

Why is this important? Because when automation capabilities encroach on entry-level job requirements, there are often fewer opportunities available for employees to build foundational knowledge they need to grow. These early roles provided important real-world exposure beyond just checking tasks off a list. The result is a skills cliff, a steep drop-off in critical workforce capabilities as experienced workers retire faster than new talent can gain the skills to replace them.

Imagine a manufacturing plant where experiential learning is baked into career progression. Early production roles have long served as entry points to supervisory, technical, and process improvement positions. However, the Manufacturing Institute reports that 2.1 million U.S. manufacturing jobs may go unfilled by 2030. Not because those jobs won’t exist, but because companies won’t be able to find people with the right skills to fill them.

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Smarter Tech Needs Smarter Training Models

Without a doubt, AI is a vital tool for supply chain orchestration, and companies should implement it properly. However, companies without a plan to reimagine workforce and skills development expose a gap in their future management layer. To avoid falling off the skills cliff, the industry must evolve beyond just viewing AI as a tool for automation. While automation is obviously an important aspect to building resilient supply chains, organizations must also look for ways to design AI into the ways people learn, adopting models that leverage AI technology as a training and mentoring resource.

Apprenticeships can take many forms, from simulated failure resolution exercises and post-action reviews to personalized coaching pathways. When done properly, AI apprenticeships become a hands-on, adaptive training system that scales mentorship and preserves institutional knowledge. Examples of this model include:

• Troubleshoot Simulated Issues

• • Like pilots learn in a flight simulator before taking to the skies, AI can help junior staff practice responding to common issues like transaction errors, late shipments, partner disputes, and more. New hires can walk through resolution scenarios, guided by AI to build their instinct, experience, and confidence.

• Review and Analyze Decisions Made

  • AI doesn’t just do the work but can also show its reasoning. By looping team members into post-action reviews, managers can help them see what the AI did and why. From there, the budding supply chain professionals can analyze whether the decisions made were the right call and what they might have done differently.

• Surface Skills Gap and Guide Development

  • The same systems being used for optimizing workflows can track the specific types of decisions employees are exposed to at various levels. Using that intel, company leaders can build adaptive training paths or connect employees to mentors with expertise in a particular area.

Supply chain leaders must reimagine workforce development as a strategic priority, embedding AI-powered learning into onboarding and upskilling programs. This apprenticeship model will partner humans with AI to learn and develop the skills needed to grow and succeed in their careers.

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