When AI Capabilities Replace University Degrees

6 Min. read time

Nearly 3,000 companies, one uncomfortable figure: one in five believes it’s possible to replace university graduates with less qualified but AI-assisted employees. That’s the finding from the ifo survey in June. For SMEs, this shifts the question of what a CV even stands for anymore.

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What the ifo figures really reveal

The ifo Institute surveyed nearly 3,000 companies in May, asking whether qualifications and professional experience could be replaced by artificial intelligence. The answer is more nuanced than the headline suggests. A minority sees the swap as feasible, while the majority remains sceptical. It’s this minority that sends the real signal.

What does replacing qualifications with AI actually mean? It means companies could cover part of the formal knowledge previously guaranteed by a university or vocational degree through AI tools and trained operational skills. The degree loses weight-without disappearing entirely-and the ability to confidently control AI moves up as a hiring criterion.

Around 20 percent of companies already using AI believe it’s easy or very easy to replace university graduates with non-specialist but AI-proficient workers. For professional experience, that figure is 15 percent. The gap is small, but it follows clear logic.

What’s crucial here is what the study doesn’t say. It measures companies’ perceptions, not actual layoffs. It shows a door opening-not one everyone has already walked through. That’s precisely why this minority deserves attention. The fact that one in five AI-using businesses considers swapping degrees for AI competence feasible is more telling than the reassuring majority behind it.

Where AI Replaces the Credential-and Where It Doesn’t

The impact isn’t evenly spread across the economy. In roles where tasks are highly standardized and well-documented, AI is trusted more. Where work depends on physical processes or hard-to-codify experience, it’s trusted less.

For mid-sized companies, this means the question isn’t whether AI replaces qualifications in general, but how standardizable your roles are. An administrative role in retail looks very different from a construction manager with twenty years of on-site intuition.

Why Experience Is Harder to Replace Than a Degree

A degree certifies knowledge that’s often already embedded in models and tools. Experience, on the other hand, is distilled pattern recognition from real-world cases-including mistakes no one documents. This kind of tacit knowledge doesn’t easily fit into a prompt.

This aligns with the ifo Institute’s findings: experience appears slightly harder for AI to compensate for than a formal credential. For workforce planning, that’s a crucial distinction. Entry-level roles with clearly defined tasks face pressure first, while experience-driven functions remain protected longer.

An everyday example makes the difference clear. AI can help a junior buyer compare offers and check contracts for standard clauses. But it can’t replace the instinct of a seasoned procurement manager who knows which supplier delivers under pressure-and which one just sounds good on paper. That knowledge isn’t in any dataset; it’s built from years of escalations. That’s why junior tasks with clear rulebooks shift to AI first, while hard-to-codify experience stays human.

What This Means for Recruiting in Mid-Sized Companies

Most job postings still filter first by degree and years of experience. But as AI skills gain value, this filter becomes a roadblock. In recruiting, that shifts several routines.

1. The degree loses its must-have status. Someone who uses AI effectively and learns quickly can overcome a formal barrier that was once non-negotiable.
2. Junior profiles become more hybrid. An entry-level employee with strong AI skills can take on tasks that previously required more experience.
3. Experienced professionals gain value. Those who can interpret and correct AI outputs become the team’s quality control, ensuring the tool alone can’t deliver.
4. Upskilling beats hiring. With talent in short supply, it’s often cheaper to train existing staff in AI than to wait for the perfect résumé.

How to Adapt Your Hiring Logic Now

Qualifications aren’t being devalued by the study-it simply highlights where the hiring framework can be usefully expanded. Three steps work immediately, without requiring a new budget.

First, include AI proficiency as a distinct criterion in job postings, backed by a concrete task in the selection process. Second, for junior roles, lower the formal minimum requirements and replace them with a short practical test. Third, train your existing team so experienced staff can confidently guide AI tools.

SMEs even have an advantage here. Short decision-making paths allow them to assess candidates based on real tasks, not just certificates. Those who leverage this find talent that large corporations with rigid requirement profiles might overlook.

One limit remains: today’s AI skills can’t replace tomorrow’s judgment. If you only optimize for tool proficiency, you’ll build a team that delivers results quickly but fails to question them critically. The smarter take on the ifo data is this: keep formal foundations where they matter, and foster AI skills wherever speed counts. Those who combine both gain velocity and reliability in one.

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Image source: AI-generated (Juni 2026)