If AI Takes the Entry-Level Work, Who Develops the Experienced Worker?

When asked to partner in creating AI-related training curriculum for the workforce, the typical request is for a program that will help workers develop AI skills to improve their productivity. Necessary and relevant work, for sure, and often the kind of training we are happy to offer because we understand there is a need for it.

However, as a lifelong learning and workforce development unit within a university, our mission is not just upskilling incumbent employees but also preparing students for pathways into employment, often through entry-level roles. Until now, we have assumed that if we get our learners to those entry points, experience will come next. In the age of AI, that assumption may no longer be true.

Jobs for the Future recently warned that AI could narrow early-career workers’ access to the experience needed to build stable careers. Its initiative focuses on work-based learning, durable skills and employer engagement as possible interventions to keep early-career pathways open. Research from Brookings raised a similar concern. It found that AI could significantly affect nearly half of the pathways workers without four-year degrees use to move from stepping-stone jobs into higher-paying work.

We understand that entry-level work is where people learn the ropes of a job. Not only do they learn the practical aspects of day-to-day work itself, but they learn about a company’s culture and how to navigate it. They learn how to interact with teams, communicate with colleagues, resolve workplace conflicts and serve customers. Through repetition, correction and increasing responsibility, early-career workers learn judgment. It is also where the leadership skills expected of them much later begin to form.

When organizations use AI to perform entry-level tasks rather than assigning that work to new workers, they create their own paradox. They reduce the need to hire beginners while continuing to expect an applicant pool with at least some experience. But experience is not something a worker can acquire through education alone. It develops through supervised practice. Through actual work.

If AI removes the opportunity for these workers to gain early experience, then it is up to universities and employers to create another way for them to gain it.

Potential approaches worth testing

• Identify pathway roles before turning them over to AI. Employers can determine whether an entry-level role regularly leads to more advanced positions. If it does, the role can be redesigned so AI assists with the work without eliminating the role itself.
   
• Entry-level roles can be designed around supervised, AI-assisted work. New employees can perform real assignments with AI while experienced employees review their work, explain decisions and increase responsibility toward developing competency.
   
• Create paid, progressive work-based learning. Universities and employers can develop paid internships, apprenticeships or other learning positions that connect education to real work and increase responsibility over time.
   
• Pilot short, paid AI projects. Students or new employees can work with experienced staff on a defined workplace problem. The employer gains useful results while the student or new employee gains actual experience. The work could be either an entry-level assignment or a student project.

As a society, we can decide that the first rung of the career ladder belongs to AI. But without a strategy to replace the experience that rung once provided or redesign the rungs that follow, the ladder will not be of much use. Collectively, we will fail the generations expected to adapt to these changing conditions. Experienced workers are developed through the work of work, not simply created from nothing. Education prepares people with the knowledge and skills to enter a field, but it is work that gives them the opportunity to apply what they know, understand what they do not know, and fill in those gaps.

To address these concerns, employers across sectors can partner with universities to identify which early-career experiences AI is removing and create new ways for workers to gain them. If employers expect to hire experienced workers later, their role in developing those workers should begin now.

Work-based learning has never been as important as it is today.

To discuss partnership opportunities within the Division of Lifelong Learning and Workforce Development at Cleveland State University, email Dr. Nancy Pratt at n.pratt@csuohio.edu.

Source note: This essay drew on Jobs for the Future’s Advancing AI-Resilient Early-Career Pathways initiative and Brookings’ How AI May Reshape Career Pathways to Better Jobs.

Links

https://www.jff.org/idea/advancing-ai-resilient-early-career-pathways/ https://www.brookings.edu/articles/how-ai-may-reshape-career-pathways-to-better-jobs/

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