Will AI replace graduate jobs?

Some graduate hiring is slowing in the most AI-exposed sectors, but the sharper effect is a change in what graduate roles demand. As the routine work is automated, what remains asks for judgement, communication and ownership earlier than before. Graduates are less likely to be replaced outright than to face a higher bar on day one, which is why the skills that survive AI matter more, not less.

How will juniors gain experience if AI does the junior work?

This is the real question, and it is Rahim Hirji's missing rungs problem: the junior tasks that used to build senior judgement are being automated before anyone notices they were load-bearing. If organisations remove the rungs without building new ones, they get output that looks senior with no one growing into genuine seniority, which is synthetic seniority and, at scale, capability debt. The answer is to redesign how juniors develop, deliberately, rather than assume the ladder still works.

What should companies do about AI and early-career hiring?

Do not just cut graduate intake because the tasks are cheaper to automate; that trades short-term saving for a future with no senior bench. Redesign graduate and apprenticeship programmes to build judgement directly, keep some work unaided so people still practise, build new development rungs to replace the ones automation removed, and measure capability, not just output. The organisations that keep hiring and developing juniors deliberately will own the senior talent others will be missing in ten years.

Will AI replace entry-level jobs?

Q: Will AI replace entry-level jobs?

Not wholesale, but it is reshaping them faster than any other tier. AI is automating the routine tasks that used to make up junior roles, and, according to PwC, entry-level roles most exposed to AI are now seven times more likely to require traditionally senior human skills such as leadership and judgement. The result is fewer purely junior openings and 'seniorised' entry roles that expect more, sooner. The deeper risk is not graduate unemployment; it is the leadership pipeline, because the tasks being automated are the ones that used to build senior judgement.

Will AI replace entry-level and graduate jobs? Less by deleting them than by dissolving the routine tasks inside them and pushing senior demands down into junior roles. The measurable risk is to the leadership pipeline. This is part of Rahim Hirji's work, developed in SuperSkills (Kogan Page, 2026).

AI is not so much replacing entry-level jobs as hollowing out the tasks inside them and raising the bar for what is left. The routine research, drafting and analysis that used to fill a junior's first years are exactly what the tools now do cheaply, so the openings shrink and the ones that remain expect judgement sooner. But the frightening number is not the graduate unemployment rate. It is this: the tasks being automated are the ones through which people used to build senior judgement. Remove them, and you can run a productive-looking operation for a few years while quietly producing no senior people at all. The question worth asking is not whether AI takes the junior job. It is where the next generation of experts is supposed to come from once the rungs they used to climb have been automated away.

Three questions, not one

"Will AI replace entry-level jobs?" is really three questions, and conflating them is what produces bad answers. Is AI replacing junior tasks? Yes, quickly, and that will continue. Is AI replacing junior jobs? Partly and unevenly: some hiring is slowing in the most exposed sectors, but many roles are being redefined rather than removed. And the third, which almost nobody asks: what happens to an organisation if the junior tasks disappear but the senior roles still require the experience those tasks used to create? That third question is where the real damage lives, and it is the one this page is about.

What the evidence shows

The clearest picture comes from PwC's Global AI Jobs Barometer, built from close to a billion job postings. It finds that entry-level roles most exposed to AI are now seven times more likely to require traditionally senior, human-intensive skills such as leadership and face-to-face judgement, and that these "seniorised" entry roles have grown by more than a third since 2019. In other words, the floor of the job is rising: the easy start is being automated, and what remains asks more of a newcomer than it used to. The World Economic Forum's 2025 Future of Jobs report names skills gaps as the single biggest barrier to transformation, and analytical thinking as the most valued skill, precisely the capability a hollowed-out junior role no longer builds by default.

The productivity research adds the twist. Brynjolfsson, Li and Raymond, studying 5,179 support agents, found AI raised the output of the newest and least experienced staff by thirty-four percent while barely moving the experts, because the tool hands expert patterns to novices. That is genuinely good for a graduate's first-week output. It is also the exact mechanism by which a junior can produce senior-looking work without doing the thinking that used to build the capability underneath it. The number goes up. The person does not.

The real risk: the missing rungs

This is what I call the missing rungs problem: the junior tasks that used to carry people up to senior judgement are being removed by automation before anyone notices they were load-bearing. At the level of the individual it shows up as synthetic seniority, output that looks like ten years of judgement produced by someone who has not built it. Across an organisation it accumulates as capability debt, invisible while the outputs look fine, and expensive the moment a decision arrives that no junior has been developed to make. The saving from automating entry-level work is immediate and easy to book. The cost is deferred, compounding, and lands on the people who made the decision years later.

Where the evidence is uncertain

Two honest caveats. Labour-market data is noisy, and it is hard to separate AI's effect on entry-level hiring from ordinary economic cycles; some of the slowdown in graduate roles is macroeconomic, not machine. And the pipeline effect operates on a horizon of years, which means the strongest claims here, about senior talent shortages to come, are well-reasoned projections rather than measured outcomes. What is not in doubt is the direction: the tasks that built junior judgement are being automated, and organisations are mostly not redesigning how that judgement now gets built. That gap is the thing to act on before it is proven, because by the time it is proven the missing cohort is already missing.

What to do about it

For organisations, the instinct to shrink graduate intake because the tasks are now cheap is the trap: it trades a visible saving for an invisible future liability. The better move is to redesign early-career development for a world where AI does the routine. Build new rungs on purpose to replace the ones automation removed, moments that develop judgement directly rather than as a by-product of grunt work. Keep some work deliberately unaided so juniors still practise the reasoning the tools would otherwise do for them. Redesign apprenticeship and graduate programmes around capability, not task completion. And measure whether people are becoming capable, not just whether the output is good, because output has stopped being a reliable signal. For individuals starting out, the same logic points to building the skills that survive AI, judgement, framing and the willingness to do the hard thinking yourself, because those are now the differentiator that the automated tasks used to hide.

Key research and primary sources

PwC (2025). Global AI Jobs Barometer, on seniorised entry-level roles.
World Economic Forum (2025). The Future of Jobs Report 2025.
Brynjolfsson, E., Li, D. and Raymond, L. (2023). Generative AI at Work. NBER Working Paper 31161.

Related SuperSkills research

This connects to the missing rungs, synthetic seniority, the missed reps, capability debt and human skills in the age of AI. On how juniors build capability when AI does the practice, see how humans learn with AI, and on the individual career response, staying valuable in the age of AI.

About this research

Rahim Hirji is the author of SuperSkills: The Seven Human Skills for the Age of AI (Kogan Page, 2026) and the founder of The SuperSkills Intelligence Company. This work draws on research across more than 200 organisations in 30 countries over seven years. Findings are attributed to the studies that produced them and kept separate from the interpretation and named concepts, which are the author's. This is a living reference, reviewed and updated as significant new evidence appears.

Cite this

Hirji, R. (2026). Will AI replace entry-level jobs? The SuperSkills Intelligence Company. Last reviewed 25 August 2026. thesuperskills.com/research/will-ai-replace-entry-level-jobs