When AI Takes Over Entry-Level Work, Who Will Develop Future Experts?

As AI takes over more of the work traditionally assigned to entry-level employees, a difficult question emerges. Routine tasks once helped people learn what good work looks like, receive feedback from experienced colleagues, and handle exceptions. Without those experiences, employees may produce results quickly while struggling to judge whether they are right. Developing the next generation of experts has therefore become an HR challenge that brings together work design, on-the-job guidance, and the management of development records.

Consider a junior HR professional preparing a monthly labor cost report. Previously, they would gather data from each department and investigate discrepancies, learning firsthand how changes in headcount affect different pay components. AI can now help compile the data and draft the report much faster. But someone still needs to answer a question such as, “Is this month’s increase in labor costs temporary, or will it continue into the next quarter?”

If the employee simply passes on an AI-generated report, they spend less time building tables but also have fewer opportunities to understand what the numbers mean. This is where companies need to focus: identifying the learning that happened within the tasks they have automated, and deciding how to provide that learning in a different way.

Judgment matters more but opportunities to practice it are shrinking

In its July 2026 article, “Building expertise in the age of AI: Who trains the next generation?”, McKinsey notes that AI is reshaping tasks such as research, drafting, and basic analysis through which entry-level employees have traditionally learned their jobs. It proposes bringing knowledge management, role design, learning through work, and manager coaching together to build expertise. The article also notes that the extent to which AI is responsible for declining entry-level hiring remains contested. One suggested learning approach is straightforward: the employee attempts the task first, AI assesses the attempt, and the employee then discusses the differences with a manager (McKinsey, 2026).

AIHR’s HR trends for 2027 describe a related “talent paradox”: human judgment is becoming more important, even as the work that develops foundational capabilities is diminishing (AIHR, 2026). This creates two risks. Existing skills may weaken through disuse, while employees at the start of their careers may never develop those skills in the first place. Early-career development needs to pay particular attention to the second risk.

In a BCG survey cited by AIHR, half of 70 C-suite leaders and senior executives said they were already seeing deskilling in their organizations (BCG, 2026). Research from the World Economic Forum and PwC also indicates that 37% of young workers globally are employed in occupations with medium to high exposure to AI-driven changes in tasks (WEF, 2026). These figures need careful interpretation. They do not mean that half of entry-level employees have lost skills or that 37% of young people’s jobs will disappear. They describe what executives have observed and how many young workers are exposed to changes in their work.

For companies in Korea, this raises a clear question: “As we ask early-career employees to exercise more judgment, are we also giving them assignments and feedback that help them develop it?” Completing AI training alone does not answer that question.

Redesign the work employees do alongside the training they receive

Deloitte’s “Closing the experience gap” explores the divide between the experience employers expect and the opportunities people have to gain it. Experience, in this context, is more than time spent in a job. It is the ability to apply knowledge in real situations, with real constraints (Deloitte, 2025). Early-career development should therefore connect formal learning with practical performance on the job.

In large organizations, new entry-level hires often complete a common induction program before joining different subsidiaries and departments. Even with the same initial training, their development can vary considerably depending on the work they receive and the people they learn from. As automation expands, each department needs to review which tasks employees should still perform themselves and define the essential experiences for each role.

Return to the labor cost report example. This does not mean asking the employee to compile all the data manually again. Instead, they could check a sample of source records, develop their own hypotheses about why the figures have changed, and decide which departments to contact and what to ask. These activities build the foundations for assessing whether AI-generated figures and explanations stand up to scrutiny.

Design assignments that build judgment in the age of AI

Start by looking at the tasks AI can perform alongside the capabilities employees still need to develop. The following example applies this approach to labor cost analysis.

The key is to have employees record their own thinking before seeing the AI response. Use the sequence outlined earlier: an independent attempt, a review of AI feedback, and a discussion of the differences with a manager. AI’s assessment must not be treated as the definitive answer. Judgments should be evaluated against criteria validated by subject-matter experts and the underlying evidence.

Practice should extend beyond familiar cases. If the first assignment involves a month with a scheduled bonus payment, the next could include organizational transfers or retroactive pay, where amounts owed for an earlier period are paid later. Seeing whether employees can identify what to check when the circumstances change helps distinguish memorized explanations from a sound grasp of the reasoning behind them.

A safe starting point is historical data or simulated cases that do not affect actual pay or personnel decisions. As employees demonstrate their capabilities, gradually expand their responsibilities under the supervision of experienced colleagues. Access controls and personal data protection requirements still apply during practice.

Build coaching time into workload plans

Even a well-designed assignment has limited value if no one is available to review it. Without guidance, employees may do little more than check AI output on their own. Managers need to examine the evidence employees used and what they missed, as well as the final deliverable. Questions such as “Why did you check this item first?” and “What would have to change for your conclusion to be different?” help broaden their judgment.

Relying entirely on experienced colleagues’ goodwill is unlikely to be sustainable. Companies should assign a reviewer and time for feedback to each task, and account for coaching in those colleagues’ workloads. Counting mentoring sessions alone may produce more meetings without a useful record of what was learned. Even a brief note on the strengths and development needs revealed by an assignment, and what the employee should tackle next, can make a difference.

Nor will time for development emerge if every hour saved through AI is filled with more work. From the outset of automation planning, organizations should reserve some of that time for case reviews and practice. This supports both current output and the expertise the business will need in the future.

Assess what employees can do as well as what they have learned

Training completion rates alone offer a limited picture of development. An employee may complete every course and still miss an exception, while another may progress quickly through practical assignments despite taking fewer courses. Companies need to assess what employees can do and the level of proficiency they demonstrate, alongside what they have learned.

Useful measures might include the proportion of employees who complete a practical assignment after training, how well they identify errors or issues requiring further investigation in an unfamiliar case, and whether they repeat the same mistakes after feedback. Employees who meet the required standards can take on broader responsibilities with less supervision. Faster task completion, however, is not sufficient evidence that someone is ready to work independently.

Connecting learning history with on-the-job performance requires a way to bring development records together.

HCG’s hunel provides learning management capabilities for tracking training plans and completion, along with career development capabilities for managing career development plans (CDPs), mentoring, and career paths. Organizations can use these capabilities to connect employees’ learning histories with their development plans and establish processes for monitoring how development is progressing on the job.

This is particularly important for corporate groups that assign entry-level hires to different subsidiaries after a common induction program. They need visibility across the group while preserving each subsidiary’s development standards. hunel supports multiple companies and legal entities as a core part of its structure, enabling subsidiaries to operate different learning and career development programs while consolidating information at group level.

Managing practical work samples, assignment-specific feedback, and assessments of readiness to work independently requires further decisions. Companies first need to define their operating standards, determine what data to retain, and confirm the scope of system configuration and integration. The division of responsibility is clear: the system helps maintain continuity in development records when departments or managers change, while business teams remain responsible for assigning work and assessing capability.

As AI changes entry-level work, responsibility for developing the next generation of experts needs to become more explicit. HR defines development pathways and common standards, business teams provide opportunities to practice judgment and receive feedback, and leaders make time for both. That is why assessing automation should include two questions: “How quickly can we get the work done?” and “Are we developing the people who will carry this work forward?”