Key Development

While AI’s overall impact on labour markets remains modest, Deutsche Bank analysts have identified a troubling pattern: companies are increasingly attributing job cuts to artificial intelligence when other cost pressures may be the true driver. The term “AI redundancy washing” describes this practice of using AI as a convenient narrative to justify layoffs that might otherwise face scrutiny.

This emerges as the U.S. tech sector reported 52,050 job cuts in Q1 2026, with AI cited as the leading stated reason for March layoffs (15,341 firings, roughly 25% of total cuts). However, the actual causation behind these numbers remains unclear—and potentially obscured.

Why This Matters

The distinction between genuine AI-driven automation and opportunistic cost-cutting is not merely semantic. It affects:

For Workers: If job losses are attributed to inevitable technological change rather than company strategy, it shifts responsibility away from employers and may reduce pressure for retraining, severance, or transition support.

For Policymakers: Labour market interventions, skills development programmes, and social safety nets depend on understanding why jobs are disappearing. Misdiagnosis leads to misaligned policy.

For the broader AI narrative: If AI becomes a convenient scapegoat for redundancies driven by profit maximization, recession fears, or poor management, public trust erodes—potentially fuelling backlash against legitimate AI development.

The Broader Context

Morgan Stanley’s analysis found that fears of AI-driven job losses appear overstated: there’s limited evidence of broad-based displacement so far. Yet simultaneously, AI is creating opportunities at scale—600,000+ new data centre jobs and 1.3 million roles like AI Engineers and Data Annotators.

Critically, workers with AI skills command wage premiums up to 56% higher than peers (per PwC’s 2025 Global AI Jobs Barometer). This creates a skills gap rather than a jobs apocalypse—but only for those who can access upskilling.

Practical Implications for European Builders and Companies

For Irish and European tech companies navigating the August 2026 EU AI Act transparency deadline and emerging regulatory frameworks, this issue carries weight. As Ireland’s distributed enforcement model (involving 15 sectoral regulators) takes shape ahead of the August 2026 compliance crunch, corporate claims about AI-driven changes may face increased scrutiny.

What to consider:

  • Document the actual drivers of organisational change—technological, economic, strategic
  • Be transparent with employees and regulators about automation decisions
  • Distinguish between AI capability and deployment choices (the latter is under management’s control)
  • Plan retraining and transition support proactively, not reactively

Open Questions

How will regulators distinguish genuine AI automation from redundancy washing under the EU AI Act? Will transparency requirements force companies to disclose whether AI was truly the primary driver of labour changes, or will self-reporting remain vague? And critically: if workers and communities are harmed by layoffs framed as inevitable AI progress, who bears responsibility for transition support?

As the labour market data clarifies, the narrative clarity matters as much as the numbers.

Source: Deutsche Bank Analysis / Morgan Stanley Labour Market Report