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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →PwC’s 2025 Global AI Jobs Barometer found that industries most exposed to artificial intelligence had much faster revenue-per-employee and wage growth than industries with lower exposure. But the evidence does not show that GenAI automatically raises every worker’s pay or protects every job. It shows an association between AI exposure, business performance and labor-market demand—while also revealing slower hiring in exposed occupations and rapidly changing skill requirements.
What PwC actually measured
PwC analyzed nearly one billion job advertisements across six continents, thousands of company financial reports and occupational data describing which tasks artificial intelligence can perform. The 2025 edition used job-posting data through the end of 2024 and compared industries in higher and lower AI-exposure groups. The findings are summarized in PwC’s 2025 press release and full report.
Exposure is not the same as adoption. An occupation can be highly exposed because its tasks are technically suitable for AI even when an employer has not deployed an AI system. The study therefore compares outcomes associated with exposure; it does not count every AI tool in use or prove that GenAI caused each change.
What “worker value” can mean
- Business value: revenue generated per employee.
- Labor-market value: wages associated with jobs requiring AI skills.
- Employment value: whether job postings continue to grow.
- Capability value: the ability to handle more complex work with AI assistance.
- Scarcity value: the premium for combining domain expertise with AI capability.
These measures are not interchangeable. Higher revenue per employee does not guarantee an individual raise, and a wage premium attached to an AI-skilled job posting is not a guaranteed premium for everyone who uses a chatbot.
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Productivity rose faster in highly exposed industries
| Measure, 2018–2024 | Most AI-exposed industries | Least AI-exposed industries |
|---|---|---|
| Revenue per employee | 27.0% growth | 8.5% growth |
| Wages per employee | 16.7% growth | 7.9% growth |
PwC described the productivity increase as nearly a fourfold acceleration in the most exposed industries after 2022. In its comparison, productivity growth in those industries rose from 7% during 2018–2022 to 27% during 2018–2024, while the least-exposed group moved from 10% to 9%. See the PwC release.
PwC’s productivity proxy is revenue per employee, an industry-level business measure. It is not the same as output per hour, total-factor productivity, worker wellbeing or the amount of work completed by an individual. Revenue per employee can also change because of prices, product mix, outsourcing, headcount decisions, market concentration, capital investment or demand conditions.
Software publishing and financial services are among highly exposed sectors, while mining, logging, hospitality and construction appear in the lower-exposure comparison. Those industries have different capital intensity, business cycles, margins and workforce profiles. PwC says the pattern is suggestive, not conclusive proof that AI alone caused the acceleration.
Wages show an association, not an automatic raise
Industry wage growth
Wages increased 16.7% in the most AI-exposed industry quartile from 2018 to 2024, compared with 7.9% in the least-exposed quartile, according to the 2025 report. That is an industry comparison, not evidence that each AI-using employee received a similarly sized increase.
The 56% AI-skill premium
PwC found that job postings requiring AI skills carried an average 56% wage premium over otherwise comparable postings without those requirements in its 2025 analysis. The premium appeared in every industry included in the analysis, but “every industry” does not mean every employer or worker.
Several factors can contribute to the premium besides AI making a person more productive:
- AI requirements may be concentrated in senior, technical or highly educated roles.
- Postings may be clustered in high-paying locations and larger firms.
- Employers may use AI language as a proxy for analytical or managerial ability.
- Early demand and limited supply may create a temporary scarcity premium.
- Advertised or inferred compensation is not necessarily realized earnings.
The newer 2026 edition reports an average AI-skill premium of 62%, up from 57% in the previous edition, according to PwC’s current AI Jobs Barometer materials. That is a later figure and should not be substituted for the 56% result from the 2025 analysis.
AI-exposed jobs grew, but more slowly
PwC did not find generalized job-posting collapse during 2019–2024. Postings in more AI-exposed occupations grew 38%, while postings in less-exposed occupations grew 65%. AI-skill postings themselves grew 7.5% in PwC’s measured dataset even as total postings declined 11.3%.
The direction matters: exposed occupations continued to grow, but at a slower rate. PwC’s US analysis is more pronounced, reporting roughly 1% annual growth in postings for the most exposed occupations versus 20% for less-exposed roles between 2019 and 2024; details are on the PwC US page.
Job postings are not the same as filled jobs, total employment, hours, layoffs, job quality or bargaining power. Positive posting growth can coexist with fewer entry-level openings, heavier workloads or higher performance expectations.
Automated versus augmented work
PwC distinguishes occupations containing tasks AI can carry out (“automated”) from occupations where AI helps a person perform better (“augmented”). Both categories experienced job growth in every industry PwC analyzed, although augmented occupations generally grew faster.
An occupation can be highly automatable without disappearing. Routine production may shrink while demand rises for reviewing outputs, handling exceptions, managing clients, making judgments under uncertainty, integrating systems and accepting responsibility for decisions. The task—not necessarily the whole occupation—is often the unit that changes.
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Skills are changing faster than job titles
Employer-requested skills changed 66% faster in highly AI-exposed occupations than in less-exposed occupations, according to the 2025 report. This is a relative comparison, not a claim that every skill changed by 66%.
Explicit degree requirements also fell in job advertisements. For AI-augmented jobs, postings requiring a degree declined from 66% to 59% between 2019 and 2024; for AI-automated jobs, they fell from 53% to 44%. These figures describe advertised requirements, not actual hiring standards or the value of education.
The practical risk is that a role changes faster than a worker, training program or employer can adapt. Learning one software product may have short-lived value; evaluation, data interpretation, communication, domain knowledge and sound judgment transfer more readily as tools change.
Who is most likely to benefit?
Potential beneficiaries
- Workers who pair AI fluency with scarce domain expertise.
- People whose roles are augmented rather than dominated by routine automation.
- Professionals who can test, verify and explain AI output.
- Employees who move toward advisory, creative, strategic or interpersonal work.
- Firms using AI to create new products or revenue rather than only reduce costs.
Groups facing greater pressure
- Entry-level workers whose routine tasks traditionally provided training.
- People in standardized, codifiable occupations.
- Employees whose employers add AI without role redesign or training.
- Workers without access to paid tools, quality data or employer-sponsored learning.
- Women, who PwC reports are more represented than men in AI-exposed occupations in every country it analyzed and therefore face both opportunity and skills pressure.
Why productivity gains may not reach paychecks
When a company produces more revenue per employee, the gain can go to wages, shareholders, customers through lower prices, managers or new investment. AI may increase the value of workers who complement it while reducing demand for workers performing substitutable tasks.
Labor shortages, bargaining power, unions, local labor markets and management strategy influence who captures the surplus. Industry-level wage growth therefore cannot establish that AI caused an individual’s pay increase, nor that every employer will share gains in the same way.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What PwC’s 2026 update adds
PwC’s newer 2026 Global AI Jobs Barometer describes a “two-track” labor market. AI-professionalized or augmented work can expand, while human-intensive capabilities such as judgment, creativity and leadership become more valuable. This is complementarity, not a claim that technical AI skills are becoming irrelevant. The reported 62% AI-skill premium is an update to the later edition, not a revision of the 2025 figures above.
Practical decisions for workers
- Anchor AI learning to a domain. Choose a business problem in finance, operations, sales, law, design or another field you understand.
- Measure an outcome. Track cycle time, error rates, quality, revenue, customer response or hours saved before and after using AI.
- Learn verification. Build skills in fact-checking, data handling, privacy, security and escalation when an output is uncertain.
- Show evidence, not buzzwords. A portfolio demonstrating a verified improvement is stronger than listing “prompt engineering” alone.
- Strengthen human capabilities. Communication, stakeholder management, judgment and accountability become more important when routine drafting is automated.
Practical decisions for employers
- Set a measurable objective and establish a baseline before deployment.
- Redesign jobs and training instead of simply adding a tool to an unchanged workflow.
- Define human review, escalation, privacy, security and intellectual-property controls.
- Protect entry-level learning pathways so automation does not remove the route into skilled work.
- Review pay, promotion and workload when productivity rises.
- Monitor effects across demographic groups and test whether gains create growth or merely reduce headcount.
Enterprise assistants such as ChatGPT Business or Enterprise, Microsoft 365 Copilot, Google Workspace with Gemini and Claude for Work may support these efforts, but purchasing a subscription does not reproduce PwC’s outcomes. Results depend on workflow redesign, governance, skills and whether the organization creates value that it can measure and share.
Frequently Asked Questions
Does PwC prove that GenAI caused higher productivity?
No. PwC found an observational association between AI exposure and revenue-per-employee growth and explicitly says the analysis cannot establish causation with certainty.
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No. It is an average premium associated with job postings requiring AI skills in PwC’s 2025 comparison. Seniority, location, occupation, education, firm type and scarcity can also affect the difference.
Are AI-exposed jobs disappearing?
PwC found positive posting growth in exposed occupations from 2019 to 2024, but growth was slower than in less-exposed occupations. Postings do not measure every layoff, filled job, hour worked or change in job quality.
The Bottom Line
PwC’s evidence supports a qualified conclusion: AI exposure is linked with faster industry productivity and wage growth, and AI skills command a substantial labor-market premium. The benefits are uneven, hiring growth is slower in exposed occupations, and skills are changing quickly. The decisive question is which workers and organizations can combine AI with expertise, judgment and accountability—and who receives the resulting gains.
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