AI Is Reshaping Entry-Level Jobs and the Path to Career Growth

Artificial intelligence is changing the way organizations approach work, including many of the tasks traditionally assigned to people at the beginning of their careers. While AI can help younger employees learn faster and become productive sooner, its growing use also raises concerns about how future professionals will gain the experience needed to move into senior roles.

The issue has similarities to earlier periods of technological change. When machinery transformed industry in the 19th century, institutions such as the mechanics’ institute founded in Bolton in 1824 focused on helping people gain the knowledge needed to adapt. Today, AI can perform or accelerate tasks such as writing, analysis, coding, research summaries, document review, testing and customer responses.

This creates a challenge for organizations. Many of these activities have traditionally been part of junior positions and, although repetitive, they have provided opportunities for employees to develop practical knowledge and judgement.

The pressure on younger workers is already significant. In the UK, 981,000 people aged 16 to 24 were not in education, employment or training between April and June 2026. The figure was 30,000 higher than a year earlier despite a small quarterly improvement. Research cited by the Work Foundation also indicates that starter jobs have declined by 49% over the past decade.

Graduate recruitment has become increasingly competitive. Employers receive around 140 applications for each graduate vacancy, compared with 38 two decades ago, while the Institute of Student Employers expects graduate vacancies to fall by another 7% in 2026.

AI is not the only factor affecting hiring. Weak economic growth, higher employment costs and organizations retaining experienced employees while delaying recruitment are also contributing to the situation. However, AI can change hiring even when an entire job is not eliminated because companies can automate or accelerate individual tasks.

Research from Stanford’s Digital Economy Lab found that employment among US workers aged 22 to 25 in occupations exposed to AI was 19% below the level it would have reached if it had followed less-exposed occupations. The researchers describe this as an early descriptive indicator rather than proof of a causal relationship. Their findings also showed that employment was flat or increasing in occupations where AI complemented human work, while declines were concentrated in roles where AI substituted for human tasks.

For employers, this creates a distinction between short-term cost savings and long-term workforce capability. Removing junior positions can improve immediate costs, but if those positions were also the route through which employees developed the knowledge required for senior roles, organizations may face capability gaps later.

AI can also have a positive role in developing less experienced workers. A large field study published by the US National Bureau of Economic Research found that generative AI increased productivity among customer support agents by an average of 14%, with the largest improvements among less experienced employees.

This suggests that organizations can use AI to help newer workers become effective faster rather than simply using the technology as a reason to avoid hiring them.

The changing workplace therefore requires organizations to reconsider what junior roles look like. Entry-level employees can use AI and take on valuable work sooner, but they still need opportunities to handle real problems, receive feedback, take supervised responsibility and understand the underlying work.

Universities and employers also have a role in preparing graduates for this environment. The 2026 HEPI Student Generative AI Survey found that 95% of UK undergraduates were using AI and 94% were using it to support assessed work. At the same time, fewer than half said teaching staff were helping them develop the AI skills needed for their careers.

Organizations can help by focusing recruitment and development on reasoning, verification and judgement rather than simply polished AI-assisted applications. Students and junior employees also need exposure to imperfect systems, complex information, competing priorities and practical consequences.

AI programmes should therefore be measured through more than hours saved or positions removed. Organizations can also consider whether employees are becoming capable faster, whether knowledge is spreading beyond experienced staff, whether junior employees are moving into more valuable work and whether the organization is developing skills it will need in the future.

The broader challenge is maintaining a path from entry-level work to expertise. AI can help people gain knowledge faster and take on more valuable responsibilities, but organizations need to deliberately preserve opportunities for learning and development as automation changes the tasks that once formed the starting point of professional careers.