The Economic Implications of Artificial Intelligence for Employment and Labour Productivity: A Descriptive Evidence Synthesis (2020–2025)

Abstract

This study examines the economic implications of artificial intelligence (AI) for employment and labour productivity using published evidence from 2020–2025. It synthesizes enterprise AI-adoption statistics, occupational-exposure assessments, and empirical workplace studies to distinguish technological diffusion, potential labour-market transformation, and reported productivity outcomes. The reviewed evidence indicates expanding business adoption of AI and productivity improvements in selected customer-support and professional-writing tasks. However, occupational exposure does not necessarily translate into actual job displacement, and productivity gains observed in specific workplace settings cannot be directly generalized to national labour productivity. Potential labour-market adjustment pressures vary across income groups and genders, while some evidence suggests that less experienced workers may benefit disproportionately from AI assistance. Overall, the findings indicate that task transformation and worker augmentation provide a more balanced interpretation of AI's employment implications than the assumption of uniform labour replacement. Key policy priorities include improving digital infrastructure, strengthening relevant workforce skills, supporting workers during occupational transitions, and protecting workers' interests. As a descriptive evidence synthesis rather than an original causal analysis, this study highlights the need for more harmonized AI-adoption data and further research into employment, wages, and long-term productivity outcomes.

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