--- canonical_url: "https://csls.ca/ipm-archive/ipm-issue-48/opportunities-and-risks-of-artificial-intelligence-for-productivity/" title: "Opportunities and Risks of Artificial Intelligence for Productivity" author: name: "CSLS" url: "https://csls.ca/author/cslc/" date_published: "2026-07-13T08:15:26+00:00" date_modified: "2026-07-17T10:02:18+00:00" post_type: "ipm" summary: "" ipm_issue_period: - "Spring 2025" ipm_keyword: - "adoption" - "AI" - "artificial intelligence" - "Baumol" - "G7" - "innovation" - "market concentration" - "next decade" - "OECD" - "productivity" - "task-based" - "TFP" ipm_research_theme: - "Policy and institutions" - "Technology and innovation" ipm_country_focus: - "G-7" - "International" - "OECD" - "United States" featured_image: "https://csls.ca/wp-content/uploads/2026/06/IPM_50_banner.webp" status: "publish" --- # Opportunities and Risks of Artificial Intelligence for Productivity Canonical URL: https://csls.ca/ipm-archive/ipm-issue-48/opportunities-and-risks-of-artificial-intelligence-for-productivity/ # Opportunities and Risks of Artificial Intelligence for Productivity [Download full PDF article](https://csls.ca/wp-content/uploads/2026/07/IPM-Issue-48-1-Filippucci-Gal-Laengle-Schief-Unsal.pdf) Cite this article × Cite this article as: Filippucci, Francesco, Peter Gal, Katharina Laengle, Matthias Schief, and Filiz Unsal. 2025. “Opportunities and Risks of Artificial Intelligence for Productivity.” International Productivity Monitor, No. 48 (Spring 2025): 1–26. https://csls.ca/ipm-archive/ipm-issue-48/opportunities-and-risks-of-artificial-intelligence-for-productivity/ ## Authors: - Francesco Filippucci OECD Economics Department - Peter Gal OECD; Graduate Institute of International and Development Studies - Katharina Laengle OECD Economics Department - Matthias Schief OECD Economics Department - Filiz Unsal OECD Economics Department ## IPM Issue 48 Spring 2025 pp. 1–26 ## Related IPM articles [Is the SNA Still Useful? 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Paul Fisher Spring 2025 | IPM Issue 48 | pp. 115–138](https://csls.ca/ipm-archive/ipm-issue-48/de-industrialization-and-the-great-productivity-slowdown-what-comes-next/) [The UK Productivity Slowdown: A Review of Timing, Magnitude, and Drivers Josh Martin Spring 2025 | IPM Issue 48 | pp. 1–34](https://csls.ca/ipm-archive/ipm-issue-48/the-uk-productivity-slowdown-a-review-of-timing-magnitude-and-drivers/) [Appendix: UK Productivity Growth Rates Across Data Vintages and Studies Josh Martin Spring 2025 | IPM Issue 48 | pp. 65–73](https://csls.ca/ipm-archive/ipm-issue-48/appendix-uk-productivity-growth-rates-across-data-vintages-and-studies/) ## Abstract ## Résumé EN FR Copy link Email LinkedIn This article reviews recent evidence and projections on the impact of Artificial Intelligence (AI) on productivity growth, with a focus on G7 economies. Drawing on OECD work and related studies, it synthesizes a range of estimates, suggesting that AI could raise annual total factor productivity (TFP) growth by around 0.3–0.7 percentage points in the United States over the next decade. Projected gains in other G7 economies are up to 50 per cent smaller, reflecting differences in sectoral composition and assumptions about the relative pace of AI adoption. The article compares alternative modeling approaches and explores key mechanisms underpinning these projections. It also discusses risks —such as market concentration, algorithmic collusion, and Baumol effects as well as upside potentials related to innovation, skills, and trade integration through AI-driven efficiency gains. Cet article passe en revue les données probantes récentes et les projections sur l’impact de l’intelligence artificielle (IA) sur la croissance de la productivité, avec un accent sur les économies du G7. S’appuyant sur les travaux de l’OCDE et des études connexes, il synthétise un éventail d’estimations, suggérant que l’IA pourrait augmenter la croissance annuelle de la PMF d’environ 0,3 à 0,7 point de pourcentage aux États-Unis au cours de la prochaine décennie. Les gains projetés dans d’autres économies du G7 sont jusqu’à 50 % plus faibles, reflétant des différences dans la composition sectorielle et des hypothèses sur le rythme relatif d’adoption de l’IA. L’article examine également les risques — tels que la concentration du marché, la collusion algorithmique et les effets Baumol — ainsi que les potentiels à la hausse. [Download full PDF](https://csls.ca/wp-content/uploads/2026/07/IPM-Issue-48-1-Filippucci-Gal-Laengle-Schief-Unsal.pdf)