Artificial Intelligence and the Future of Entry-Level Work: A Framework for Safeguarding and Reinventing Early Career Pathways

Artificial intelligence is transforming the structure and experience of entry-level work, with the effects particularly visible in occupations where routine information-processing tasks can increasingly be supported or automated by AI. The report estimates that 37% of young workers globally are employed in occupations with medium to high exposure to AI-driven task change. Exposure varies substantially by region, reaching 75% in Eastern Asia, 69% in Northern America and 63% in Europe. Knowledge-intensive sectors such as financial services, information and communication, professional services, science and education show the highest levels of exposure.

The report cautions against treating current declines in entry-level hiring as evidence of straightforward AI-driven job displacement. Research indicates that early-career job postings have declined more sharply in highly AI-exposed occupations, including a reported 16% decline in entry-level jobs in AI-exposed US fields since late 2022. However, declines in job postings began before ChatGPT was released, while employer interviews suggest that economic uncertainty, cost pressures and cautious headcount expansion are also important drivers.

At the same time, AI can increase the productivity and scope of entry-level work. 68% of entry-level workers report increased productivity from AI, but 45% say AI has also increased the amount of time they spend working. This creates a central challenge: productivity gains do not automatically translate into better jobs. Without deliberate redesign, organizations risk replacing learning opportunities with automation, intensifying workloads or leaving employees with lower-value tasks.

The report therefore proposes a four-dimensional framework for safeguarding and reinventing early-career pathways:

1. Job access – maintain deliberate entry-level hiring as part of workforce planning and prevent AI adoption from unintentionally restricting access to early-career opportunities.
2. Job design – redesign roles around meaningful human–AI collaboration while deliberately retaining tasks that develop judgement, problem-solving and domain expertise.
3. Talent pipelines – move from rigid, hierarchical career structures towards capability-based development, enabling more non-linear progression and proactive matching of talent to evolving roles.
4. Education system alignment – strengthen employer–education collaboration, work-integrated learning and alternative pathways so that curricula and training respond more rapidly to changing labour-market requirements.

The report uses a mixed-methods approach, combining quantitative labour-market analysis with worker and executive survey data, expert dialogue, stakeholder interviews and organizational case studies.

1. Labour-market and AI-exposure analysis

The report combines ILO ILOSTAT youth-employment data with the PwC AI Jobs Barometer 2026. AI exposure is calculated at occupation level and mapped to ISCO-08 major occupational groups. Exposure scores are rescaled to a 0–1 range and grouped using Jenks natural breaks before being mapped onto ILOSTAT employment data for workers aged 15–24.

Sector-level AI exposure is calculated as an employment-weighted average of occupation-level AI-exposure scores, based on the extent to which job tasks align with current AI capabilities.

2. Skills-change analysis

The report uses the PwC AI Jobs Barometer's Net Skill Change (NSC) measure to assess how rapidly the mix of skills required for occupations is changing. NSC is calculated by summing absolute increases and decreases in the frequency of skills appearing in job postings; higher values indicate greater change in skill requirements.

The report also analyses the emergence of "new" skills in job postings, defining a new skill as one with more than 10 mentions in 2025 but five or fewer mentions in the same occupation in 2019.

3. Worker and executive surveys

The analysis incorporates the PwC Global Workforce Hopes & Fears Survey 2025, which captures the views of more than 9,000 entry-level workers across 48 countries, alongside data on managers, senior executives and organizational expectations.

The report also draws on the PwC Global CEO Survey 2026 and other PwC research to examine organizational structures, skills availability, hiring expectations and AI-related business transformation.

4. Expert consultation and qualitative research

Quantitative evidence is complemented by perspectives from the World Economic Forum's Global Dialogue on AI and Entry-Level Work, which convened more than 200 leaders and experts. The report also incorporates interviews with business leaders deploying AI and working sessions with organizations across industries.

The contributors include senior representatives from organizations such as ABN AMRO, Allianz, Cisco, Dentsu, Dropbox, Fujitsu, Hitachi, Indeed, Merck, Sony, Siemens, UBS and others.

5. Case studies

The report uses organizational and policy examples to illustrate how the proposed framework can be implemented in practice. Examples include Dropbox on expanding early-career programmes and reinvesting AI productivity gains; Shoosmiths on AI adoption and capability frameworks; Hitachi on deliberate job redesign; Merck on university partnerships and AI training; Singapore on skills-based labour-market infrastructure; and Canada's Digital Skills for Youth programme on reducing barriers to first digital employment.

Methodological note: The report is an Insight Report, not a standalone primary research study. Its conclusions are synthesized from multiple datasets, surveys, external research sources and qualitative consultations. The report itself notes that its findings and conclusions resulted from a collaborative process facilitated and endorsed by the World Economic Forum.

The report's principal data and evidence sources are:

1. International Labour Organization (ILO), ILOSTAT – youth employment by sex, age and occupation.
2. PwC AI Jobs Barometer 2026 – AI exposure, job postings and skills-change analysis.
3. PwC Global Workforce Hopes & Fears Survey 2025 – worker perceptions, AI use, productivity, working time, career expectations and future skills.
4. PwC 29th Global CEO Survey 2026 – organizational structure and skills-availability risks.
5. World Economic Forum Future of Jobs Report 2025 – projected changes in skills requirements.
6. World Economic Forum New Economy Skills: Unlocking the Human Advantage (2025) – human-centric and applied skills.
7. World Economic Forum & LinkedIn, Gender Parity in the Intelligent Age (2025).
8. OECD, Digital Skills for Seniors (2025).
9. ILO, Gen AI, occupational segregation and gender equality in the world of work (2026).
10. Deloitte research on GenAI costs and AI-enabled work design.
11. Stanford Digital Economy Lab research on recent employment effects of AI.
12. SSRN and Ratio Institute research on generative AI and labour-market outcomes.
13. Harvard Business Review research on AI and work intensification.
14. Academic research on AI-related skill decay and the effects of automation on meaningful work.
15. Additional evidence from Indeed, ResumeBuilder.com, ADP Research Institute, Strada Institute for the Future of Work, Salesforce and the Institute of Student Employers.