AI is raising the first rung: Who will turn graduates into professionals?

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This blog was kindly authored by Professor Mohamed Abdel-Maguid, Pro Vice-Chancellor for Education, Student growth and Global Engagement at Canterbury Christ Church University.

The debate about Artificial Intelligence (AI) and graduate jobs is usually framed as a question of job numbers: how many entry-level roles will disappear? This matters, but in my view, it is not the most important question. A deeper risk is that artificial intelligence is absorbing the work through which graduates develop into trusted professionals.

Routine analysis, first drafts, basic coding, first-triage casework, standard research and reporting were never the most exciting parts of professional life. But they did two jobs simultaneously. They created value for the employer and gave junior staff repetitive tasks, feedback, supervised responsibility and space to make bounded mistakes. This was a form of apprenticeship, even when nobody called it so.

For decades, there was an implicit arrangement: Universities developed disciplinary knowledge, skills and initial competences; Employers recruited graduates and carried part of the cost of turning graduate competences into trusted professional judgement and, where relevant, professional bodies set the standards. Over time, the graduate transformed from a novice with foundational capabilities to a professional trusted to act independently and exercise judgement when faced with new and unfamiliar situations.

Artificial intelligence disrupts this implicit arrangement.

The Institute of Student Employers’ Student Development Survey 2026, which draws on responses from 144 employers, reports that 87 per cent of employers expect artificial intelligence to reshape graduate and apprentice roles. Forty-three per cent say their entry-level roles have already evolved in response to it but have not been formally redesigned.

PwC’s 2026 Global AI Jobs Barometer provides clear evidence of what is happening. Its analysis of 2.4 million entry-level jobs in the United States found that most AI-exposed junior roles are seven times more likely than the least AI-exposed roles to require capabilities traditionally associated with more senior staff. These ‘seniorised’ roles that require leadership, judgement and adaptability from junior staff have grown by 35 per cent since 2019, while non-seniorised roles have declined by 10 per cent. PwC reaches a similar conclusion about the development problem: as AI absorbs routine work, some of the activity that once served as an informal apprenticeship is disappearing.

Evidence from the UK Institute of Student Employers (ISE) points to a similar story. ISE’s Student Recruitment Survey 2025 found that graduate hiring had fallen by 8 per cent year-on-year, with competition reaching a historic high of 140 applications per vacancy.

This is not just a simple story of disappearing jobs. The first rung of the professional ladder is being raised. Employers are asking for judgement, leadership and adaptability earlier, while reducing the routine work through which those capabilities were traditionally developed. We are asking graduates to walk into their first jobs with experience before giving them the space to develop it.

Who now owns graduate professional formation?

Universities cannot solve this by adding an artificial intelligence module to every course; nor should they claim the responsibility for the whole of professional formation. Employers and professional bodies remain essential. But the implicit arrangement between them is beginning to break down. Universities cannot safely assume that employers will continue to provide the same developmental bridge between graduate competences and professional judgement.

Three decisions are now needed:

First, universities should define what graduate-level judgement means in each discipline and design for it across the whole student learning journey. Judgement is not something we can assess in separate modules and hope it will somehow accumulate into professional capability. Students need repeated opportunities to work with unfamiliar problems, incomplete or conflicting evidence and consequences. They also need ample opportunities to use AI, challenge it and explain why they accepted, modified or rejected its outputs. A polished output or a working artefact is no longer sufficient evidence of capability.

Second, employers should consider not only the productivity value of junior work but also its developmental value before fully automating it. This is not an argument for preserving inefficient tasks or workflows. It is an argument for replacing their implicit apprenticeship function in a deliberate way through supervised projects, simulation, exposure to clients and progressively greater decision authority. Otherwise, there is a risk that optimising today’s workflows will weaken tomorrow’s pipeline of expertise.

Third, government should use the newly established Early Careers Jobs Alliance to develop a ‘graduate formation model’ for AI-exposed sectors. Universities, colleges, employers, professional bodies and Skills England should work together to establish who develops which capabilities, where that development happens, and who pays for it. Funding for placements, degree apprenticeships and employer-led training should then support this model, with access and success monitored by socioeconomic background.

The equity point is important because if professional formation moves out of paid junior work and into mainly unpaid internships, and other routes dependent on additional credentials, graduates from disadvantaged backgrounds will face a first rung that has moved well beyond their reach.

On the positive side, AI may create more interesting and productive entry-level roles. However, we still need a credible route from novice to trusted professional.

In summary: The strategic question is no longer simply what jobs graduates will enter.
It is who will now provide the experience through which novices become trusted professionals, and who will pay for it.