Artificial Intelligence and Canada's Productivity Challenge: Bridging the Gap Between Promise and Economic Reality
Isher Singh, Bhopinder Singh Kambo · International Journal of Innovative Science and Research Technology (IJISRT) · 3 Nov 2026
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Where this sits in the debate
- Will AI actually boost productivity and growth? · its finding: it depends (why studies differ)
- How fast will the impact arrive? · its finding goes against the claim (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
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.
About this publication
Unranked journal. A journal that is not on the economics or computer-science rankings this guide uses. That says nothing about one paper, but no ranking vouches for the journal’s review.
- Published
- 2026 (recently)
- Citations
- 0 (OpenAlex)
0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison.
Reaches a different conclusion
Papers on the same question whose finding points the other way, as read from the abstracts by an AI model (AI-written). Why studies differ.
- Experimental evidence on the productivity effects of generative artificial intelligence
Productivity: its finding supports the claim · Shakked Noy and Whitney Zhang · 2023 - The simple macroeconomics of AI
Productivity: its finding goes against the claim · Daron Acemoğlu · 2024 - AI and Growth: Where Do We Stand?
Pace: its finding supports the claim · Philippe Aghion and Simon Bunel · 2024 - Who is using AI to code? Global diffusion and impact of generative AI
Pace: its finding supports the claim · Simone Daniotti et al. · 2026
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