The Productivity Paradox

Companies most exposed to AI are delivering 40% higher productivity growth than their peers, according to PwC’s latest AI Jobs Barometer. Yet this productivity surge masks a troubling employment picture: of the S&P Global 1200 index, 994 participants—83%—had lower headcount in January 2026 compared to January 2025. Only 153 companies (13%) experienced an increase.

The S&P Global Purchasing Managers’ Index survey reinforces this trend, showing a global net employment impact of -5 percentage points over the past 12 months, with a further -2 points forecast for the coming year.

Skills Transformation Outpacing Job Growth

The employment challenge extends beyond simple headcount. Skills needed for the most AI-exposed jobs are changing more than twice as fast as for the least AI-exposed roles, forcing rapid workforce reskilling.

Entry-level roles tell a particularly stark story. Seniorised entry-level positions have grown 35% since 2019, whilst overall early-career job postings have flatlined in highly AI-exposed sectors. Most troublingly, junior roles in AI-exposed sectors are 7 times more likely than their least AI-exposed counterparts to demand traditionally senior skills like leadership.

Job Market Bifurcation

Professionalised jobs are growing twice as fast as democratised roles, with 42% faster wage growth since 2021. This widening divide suggests AI adoption is creating a two-tier labour market where specialised, senior-level positions thrive while entry pathways shrink.

Enterprise Strategy Misalignment

Despite the employment headwinds, enterprise AI objectives tell a different story. Process efficiency (64%) and employee productivity (59%) are much more commonly prioritized than headcount reduction (24%). Yet outcomes lag ambition: only 46% of AI initiatives launched in the past year are on track to achieve positive ROI within 12 months, and just 37% are live and delivering value.

Among large enterprises with 10,000+ employees, only 44% cite a clear, documented AI strategy aligned with core business goals—a critical gap for organisations navigating this transition.

Size Matters: Divergent Forecasts

Company size significantly shapes AI employment outlook. Large companies forecast a net negative employment impact of -13 points, whilst small firms continue to forecast a net positive effect of +3 points and medium-sized firms +2 points. This divergence suggests consolidation risks for mid-market firms caught between.

Trust and Autonomy Gaps

Confidence in third-party AI models has fallen markedly. In 2026, just 16% completely trust them, down from 24% in 2023, while 30% mostly trust them compared to 42% three years ago. Only 22% of AI projects target a fully autonomous end state where AI operates without human intervention, indicating most organisations expect sustained human involvement.

Emerging Skills Bottlenecks

Cybersecurity skills gaps are hitting hardest: 64% of respondents cite moderate or severe impact on their AI initiatives from cybersecurity talent shortages—the highest among technical functions. This represents a critical vulnerability as enterprises scale AI deployment.

With adoption rates averaging 50% currently and planned expansion to 37% in the next year, European and Irish firms face a narrow window to align workforce strategy with deployment reality.

Source: S&P Global