Authors : Isher Singh; Bhopinder Singh Kambo

Volume/Issue : Volume 11 - 2026, Issue 10 - October

Google Scholar : https://tinyurl.com/4yzzecrt

DOI : https://doi.org/10.38124/ijisrt/26Oct1314

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Artificial intelligence (AI) offers considerable potential to improve productivity, yet its growing adoption has not necessarily translated into corresponding gains in aggregate labour productivity. This paper examines Canada's AI productivity challenge using evidence from Statistics Canada, Bank of Canada research, academic literature, and documented expert commentary. It considers the historical productivity paradox alongside business adoption patterns, projected productivity gains, sectoral differences, and emerging labour-market adjustments. Canadian evidence highlights a gap between the widespread personal use of AI by business leaders and its integration into core business operations. Conceptual illustrations demonstrate how implementation costs, organizational restructuring, complementary investment, and workforce skill development may influence the realization of AI's economic benefits. The analysis distinguishes planned adoption from actual implementation, projected gains from observed outcomes, and associations from causal effects. It argues that realizing AI's productivity potential requires effective organizational integration, workforce learning, and broader diffusion across businesses. The paper concludes with policy implications for Canada and emphasizes the importance of monitoring productivity and labour-market outcomes as AI adoption evolves.

Keywords : AI Productivity Paradox, Bank of Canada, Monetary Policy, Potential Output (Y*), Labour Market Adjustments, Technology Diffusion, General-Purpose Technologies, Two-Speed Economy.

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