Effect of Artificial Intelligence Adoption on Labour Productivity in The United States
DOI:
https://doi.org/10.53819/81018102t2581Abstract
Artificial intelligence (AI) adoption has become one of the most important economic changes in the United States. The technology is increasingly used in software development, customer service, professional writing, data analytics, health services, logistics, finance, and other knowledge-intensive activities. This paper examines the effect of AI adoption on labour productivity in the United States using a journal-style conceptual and evidence-based approach. The study relies on secondary evidence from the U.S. Census Bureau, U.S. Bureau of Labor Statistics, OECD, IMF, Stanford AI Index, McKinsey Global Institute, NBER, and recent experimental research published from 2020 onward. The paper argues that AI adoption can raise labour productivity by reducing task completion time, improving worker decision-making, automating routine cognitive tasks, supporting software development, and enabling faster knowledge processing. However, the productivity effect is not automatic. It depends on firm-level adoption, worker skills, complementary investment in software and data systems, managerial readiness, task suitability, and the ability of organisations to redesign workflows around AI. Evidence from recent experiments shows strong task-level productivity gains, including faster writing, improved customer support performance, and quicker software development. At the same time, macroeconomic evidence remains cautious because AI diffusion is still uneven across industries and many firms are in early adoption stages. The paper concludes that AI adoption is likely to have a positive effect on labour productivity in the United States, but the magnitude will depend on broad diffusion, responsible governance, reskilling, and effective integration into real production processes.
Keywords: artificial intelligence, labour productivity, United States, generative AI, digital transformation, economic growth
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