AI is Replacing Entry-Level Work. So How Will We Develop Future Supply Chain Leaders?
Mark Guyer
June 16, 2026
Artificial intelligence, large language models (LLMs), and workflow automation are rapidly transforming supply chain operations. Tasks that once required hours of manual effort can now be completed in minutes. Forecasting models are becoming more accurate, planning cycles are accelerating, errors are decreasing, and capacity decisions are increasingly supported by powerful analytical tools.
For supply chain leaders, these advancements are exciting—and they should be. Automation enables teams to accomplish more, improve service levels, and operate with greater efficiency than ever before.But there is a question we need to start asking now:
What happens when the entry-level work that traditionally develops future leaders disappears?
A Hidden Challenge of Automation
Historically, many supply chain professionals learned the business by doing foundational work. They built forecasts, managed spreadsheets, analyzed inventory, tracked orders, coordinated with operations, and solved everyday problems. These tasks were often repetitive, but they provided the building blocks necessary to understand how supply chains actually function.
As AI increasingly handles these activities, organizations may find themselves facing an unexpected talent gap. If entry-level analysts no longer perform the work that teaches core supply chain concepts, where will future planners, managers, and directors gain the experience needed to lead?
The fully automated dark warehouse is an impressive vision, and automation will continue to expand across planning, logistics, and operations. Yet the reality is that supply chains will never be completely autonomous. Businesses still require people to collaborate across departments, communicate with customers, manage change, resolve exceptions, and make judgment calls that technology cannot fully replace.
The question is not whether we need to use these tools to improve productivity, we do. The question is how we use these tools and continue developing the people who will eventually lead our organizations.
Learning from the past
We've already seen examples of this transition in manufacturing and distribution environments. Robotics have taken over repetitive, physically demanding tasks while employees focus on more complex problem-solving, quality control, and operational decision-making. When conveyance became the standard in high performing distribution operations, warehouse labor didn’t disappear.
The most successful organizations have not simply replaced workers—they have improved their experience and continue to use the team for insights and future feedback on what to address next. This example from The Feed, shows how the team implemented Brightpick automation into their operations to support growth without losing their existing team.
https://brightpick.ai/resources/the-feed/
Automation reduces costs and improves performance while creating opportunities for employees to develop higher-value skills. The workforce evolves rather than disappears.
Supply chain planning and analytics teams will face a similar challenge. If AI performs much of the routine analysis, what experiences will prepare today's junior analyst to become tomorrow's planning manager?
Rethinking Strategy and Development
As leaders adopt AI, they must also redesign how people learn.
This may require:
Building stronger partnerships with universities and technical programs to expose students to real-world operations.
Creating rotational assignments that give employees broad business exposure.
Providing opportunities to lead small projects early in their careers.
Expanding cross-functional collaboration experiences.
Developing change management and communication skills.
Giving employees ownership of process improvement initiatives.
Teaching teams how to effectively leverage AI rather than simply compete against it.
Many of these skills are uniquely human and increasingly valuable in an AI-enabled workplace.
AI can identify patterns. People still influence change. People still lead organizations.
The Leadership Opportunity
The organizations that thrive in the next decade will not be the ones that simply eliminate positions. They will be the ones that intentionally redesign career paths for an AI-powered world.
The challenge for supply chain leaders is not deciding whether to adopt AI. That decision has already been made.
The challenge is ensuring that while technology becomes more capable, we continue investing in the people who will guide our organizations in the future.
Take a look at your team today and ask yourself:
Which entry-level responsibilities are likely to be automated within the next three to five years?
How do you use fractional leadership roles & support from teams like Summit, continue to provide support for your business in leading projects.
How are employees currently gaining the foundational knowledge needed for leadership roles?
What kind of Change Management support is in place for your team managing these new models of operation while still running the business?
What new experiences can replace the learning opportunities that automation removes?
The answers may determine not only how effectively your organization adopts AI, but also whether you have the talent needed to lead it in the future.
We don't have all the answers yet, but we believe this is one of the most important workforce questions facing supply chain leaders today. How is your organization approaching the integration of automation with talent development and opportunities for future supply chain leaders?
