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A degree still matters in many hiring processes, but it is no longer the only way to signal capability. Employers expect to weigh work experience and skills assessments alongside formal education, while AI is changing tasks across many jobs—not making every worker an AI specialist. The practical shift is from treating a degree as a complete proxy for ability to showing what you can do through several kinds of evidence.
Do employers care more about skills than degrees?
Skills-first hiring means assessing what a candidate can demonstrate, whether those skills came from formal education, non-formal training, or informal experience. It does not simply mean removing degree requirements. Degrees remain part of employers’ plans: the World Economic Forum’s 2025 survey found that 43% of surveyed employers expected to continue requiring degrees by 2030, while 48% expected to use skills assessments. In the same survey, 81% expected to rely on work experience when assessing candidates for hiring during 2025–2030. These are employer expectations, not records of actual hiring or evidence that one method produces better employees.
An OECD account of the WEF survey reported that more than 40% of respondents expected to rely on university degrees and about 80% expected to prioritize work experience. The figures differ somewhat in wording and should be read as survey-based expectations, not universal rules. They point to a mixed approach: employers may use education, experience, and direct assessment together.
There are practical reasons credentials have not disappeared. A degree can be a familiar screening signal, while evaluating skills consistently across a large applicant pool takes time and reliable methods. OECD also identifies uneven access to skills validation and employer doubts about non-traditional credentials as barriers. A hiring process that drops a degree requirement can still be difficult to navigate if candidates cannot get their abilities assessed or employers do not trust the evidence.
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What does AI change about the skills employers need?
AI readiness does not mean every worker needs to learn machine learning or become a software developer. In a 2024 OECD paper, author Andrew Green writes: “Most workers who will be exposed to artificial intelligence (AI) will not require specialised AI skills (e.g. machine learning, natural language processing, etc.).” For many people, the relevant change is how AI affects their existing work and which skills help them use, supervise, or work alongside new systems.
The World Economic Forum’s 2025 report says 86% of surveyed employers expected AI and information-processing technologies to transform their business by 2030. That expectation signals broad organizational change, not a guarantee that a particular job will vanish or that every worker will perform the same new tasks. OECD’s 2024 analysis also finds management and business skills among those demanded in occupations highly exposed to AI, while noting evidence that demand for some of those skills may be beginning to fall. The direction is not simple enough to support a single career prescription.
Which skills should you build if AI changes your job?
The World Economic Forum’s 2025 report says employers expect 39% of workers’ core skills to change by 2030, compared with 44% in its 2023 report. The 2025 report names AI and big data among the fastest-growing skills. These are employer survey expectations about workforce change, not a forecast of any one person’s prospects.
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Start with the tasks in the role you want or already have. Identify where AI may change the workflow, then look for the skills and evidence employers actually request. Depending on the job, that may mean showing sound judgment with AI-supported outputs, relevant technical ability, communication, or business and management capability. The available evidence does not establish one universal set of skills that every worker should acquire.
- Read job postings for the target role and location; note required credentials separately from preferred skills.
- Look for ways to demonstrate the requested capabilities, such as relevant work experience, a portfolio, a work sample, an apprenticeship, or an employer’s assessment.
- Check whether the role involves regulated or routine credential requirements before deciding that a non-degree route is viable.
- Revisit the requirements as tools and workflows change instead of assuming that one course or credential will remain sufficient.
Can you get an AI-related job without a degree?
Sometimes a candidate may be able to qualify through experience, demonstrated skills, or another credential, but the answer depends on the particular job, employer, sector, and location. The WEF survey’s expected use of skills assessments and work experience shows that degrees are not the only signals employers anticipate using. Its expected continuation of degree requirements also makes clear that a skills-first shift is not a blanket waiver.
Before choosing a route, compare the actual requirements and accepted evidence for the job. A portfolio or work sample may show practical ability where an employer accepts it; an assessment may test a defined skill; an apprenticeship can combine training and workplace experience. None automatically substitutes for a degree when an employer, professional standard, or applicable regulation requires one. The cited evidence does not establish the return on investment of a particular degree, certificate, or training program.
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How to compare a degree with another skills pathway
- Check the requirement. Find out whether the target job legally or routinely requires a degree in the relevant location, and distinguish mandatory qualifications from preferences.
- Identify acceptable proof. Review which specific skills employers seek and whether they accept work experience, a portfolio, a work sample, an apprenticeship, a recognized credential, or an assessment.
- Assess recognition. Confirm that an alternative credential or evidence of skill is recognized by employers in the sector and place where you plan to work.
- Compare time and cost. Evaluate the time and expense of each route against the requirements you verified; the available evidence does not provide an individual return-on-investment verdict.
- Consider access and fairness. Ask whether you can realistically obtain the assessment or validation required. Skills tests can reproduce bias if poorly designed, and access to trusted validation is uneven.
What the hiring figures can—and cannot—tell you
The WEF percentages describe surveyed employers’ expectations for 2025–2030, not the share of all employers that have already adopted a practice, actual hiring outcomes, or proof that a practice improves job performance. Employer practices vary by role, sector, country, organization size, and regulatory setting. The OECD’s AI skills analysis was published in 2024; it is useful evidence about the distinction between AI exposure and specialized AI skills, but it cannot settle future labor demand on its own.
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The evidence supports a measured conclusion: degrees are still relevant, but they increasingly sit alongside work experience and demonstrations of skill. Whether a degree matters for you depends on the role’s requirements and the evidence employers in that field will trust.
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