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AI is more likely to change many jobs than to make them disappear outright, but that does not mean every worker will benefit. For people entering the workforce, the impact will depend on which tasks their jobs involve, how employers use AI, the skills they can build, and the conditions of the labor market around them.
Will AI take jobs away from young people?
Some tasks can be automated, and some roles may shrink or disappear. But an occupation’s exposure to AI is not a forecast that the occupation will be eliminated. Exposure means that some tasks in the work could be affected; automation means a task can be done by a system; job transformation means the mix of tasks or the way a person does them changes; displacement means workers lose jobs. Those are related possibilities, not interchangeable outcomes.
The International Labour Organization’s 2025 analysis estimates that one in four workers worldwide is in an occupation with some degree of generative AI exposure. Its conclusion is that most jobs are more likely to be transformed than made redundant, in part because human input remains necessary. The share in the ILO’s highest exposure category is much smaller: 3.3% of global employment. Neither figure predicts how many people will be laid off.
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The ILO’s revised 2025 index assesses tasks within occupations, drawing on task-level data, expert input and AI predictions. It groups occupations into four exposure gradients. That approach is more informative than treating a whole job title as either “safe” or “doomed,” but it still measures potential exposure, not future hiring or employment totals.
Which jobs and workers are most exposed?
Look at tasks, not just job titles
Work that is routine, text-heavy or already digitized may contain tasks that generative AI can assist with or automate. Jobs requiring varied judgment, physical activity, interpersonal interaction or responsibility may have different exposure profiles. Most roles combine several kinds of tasks, so a change to some of them does not necessarily remove the need for the role.
There is no universal list of occupations that are guaranteed to be safe. Even within the same occupation, responsibilities and AI adoption can differ by employer. A useful question is not simply “Can AI do this job?” but “Which parts of this job can AI affect, and what human work remains or becomes more important?”
Exposure varies by country and labor market
The ILO’s 2025 estimates show that some degree of generative AI exposure applies to 11% of employment in low-income countries and 34% in high-income countries. These differences reflect variation in the kinds of work people do; they do not mean workers in one group face no change or that every exposed worker will lose a job.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Young people also enter work under very different local conditions. The ILO’s 2024 youth report describes uneven regional outcomes, persistent insecurity and particular disadvantages for young women in parts of the recovery. A global exposure estimate cannot tell an individual what will happen in a particular city, occupation or national labor market.
What does the youth employment picture look like?
AI is arriving in a labor market that was already difficult for many young people. The ILO reported that 64.9 million young people were unemployed worldwide in 2023, while the global youth unemployment rate was 13%—the lowest in 15 years. At the same time, 20% of young people were not in employment, education or training, a status abbreviated as NEET. Two in three young people classified as NEET globally were women.
These figures describe youth employment conditions, not outcomes caused by AI. They also show why an average global rate can hide substantial differences: a relatively low unemployment rate can coexist with a large number of young people outside work, education and training, and with uneven prospects across regions and genders.
What skills will matter when you start work?
Preparing for AI does not mean every young worker needs to become a programmer or AI specialist. OECD analysis finds that most workers in AI-exposed occupations will not need specialized AI skills, even when their tasks and skill requirements change. The useful combination depends on the role: occupation-specific knowledge, digital fluency, communication, judgment and the ability to work with other people can matter alongside familiarity with AI tools.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesThere are signs that employers value a broader mix of capabilities. In OECD analysis, the share of vacancies in highly AI-exposed occupations requesting at least one emotional, cognitive or digital skill rose by eight percentage points. Management and business skills are also among those in demand in highly exposed occupations. This points to changing skill mixes, not a single credential that guarantees employment.
Build capability around the work you want to do
- Learn the field. Build sound occupational knowledge so you can judge whether an AI-generated result fits the real task and its standards.
- Practice digital and AI literacy. Understand how tools fit into a workflow and where their output needs human review; do not confuse tool familiarity with specialist AI development skills.
- Strengthen communication and judgment. Explaining decisions, working with people and handling exceptions can remain important when routine parts of a job change.
- Keep learning as tasks shift. Employers may reorganize responsibilities as they adopt tools, so adaptability is useful alongside formal qualifications.
The ILO’s 2024 youth report finds that training and entrepreneurship programs can improve labor outcomes, with results varying by country income group. Its review indicates that combined programs incorporating soft skills and certification tend to perform better. This is broader youth-employment evidence, not a direct test proving that a particular AI course will lead to a job.
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Can AI create opportunities as well as risks?
AI may help people complete some tasks faster, compensate for skill gaps or shift time toward other work. But those possibilities do not guarantee better pay, greater autonomy or more jobs. Whether productivity gains improve workers’ conditions depends on how employers introduce the technology and how the gains are shared.
OECD’s 2025 SME report offers a window into employer experience, not a universal forecast. It draws on a representative survey conducted in late 2024 of more than 5,000 small and medium-sized enterprises in Austria, Canada, Germany, Ireland, Japan, Korea and the United Kingdom. Among AI-using SMEs that reported skill gaps, 39% said generative AI helped compensate for them. Among that group’s firms also reporting improved employee performance, the share was 46%.
The same survey found that 19.7% of SMEs reported an increased need for highly skilled workers, while 9.4% reported a decrease. These are firms’ reports about their experiences, not causal proof that AI increases or reduces employment across businesses. The findings do show why the effect can be mixed: a tool may help workers do some tasks while also increasing demand for other capabilities.
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What is still uncertain about AI and future work?
Current exposure measures describe occupational task profiles and potential susceptibility. They do not establish which specific occupations will be created or eliminated for the next generation, the net number of jobs that will result, or a reliable timeline. Outcomes will depend on adoption by employers, the design of jobs, the pace of change and the labor markets people enter.
Young people’s views also contain both concern and optimism. The OECD’s education trends report describes worries about job elimination alongside hopes that AI could make work less boring or better suited to personal life. In the report’s underlying analysis, major employment effects had not yet been clearly evidenced. That is a qualification on what had been established in that analysis, not a guarantee that effects will not emerge later.
For someone choosing a path now, the practical response is to build expertise in a field, develop transferable human and digital skills, and stay open to learning as tasks change. No single course or career choice can remove uncertainty, and global evidence cannot substitute for local information about the opportunities and requirements in a specific labor market.
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