--- title: "Global AI Productivity Impact Report 2026: Evidence, Sectors & Macro | Alice Labs" description: "Evidence-based 2026 benchmark of AI productivity impact across workers, firms, sectors, and economies. 26 indicators, 12 findings, official statistics. Updated May 2026." lang: en json-ld: | [ { "@context": "https://schema.org", "@graph": [ { "@type": "Organization", "@id": "https://alicelabs.ai/#organization", "name": "Alice Labs", "alternateName": [ "Alice Labs AB", "AliceLabs" ], "legalName": "Alice Labs AB", "identifier": "559443-5470", "foundingLocation": { "@type": "Place", "name": "Stockholm, Sweden" }, "url": "https://alicelabs.ai", "logo": { "@type": "ImageObject", "@id": "https://alicelabs.ai/#logo", "url": "https://alicelabs.ai/images/alice-logo.png", "contentUrl": "https://alicelabs.ai/images/alice-logo.png", "width": 2000, "height": 2027, "caption": "Alice Labs" }, "image": { "@id": "https://alicelabs.ai/#logo" }, "description": "Alice Labs är en svensk AI-byrå som hjälper företag implementera AI - från strategi till skalning.", "slogan": "From AI strategy to measurable results.", "foundingDate": "2023", "email": "hej@alicelabs.ai", "telephone": "+46734157476", "address": { "@type": "PostalAddress", "streetAddress": "Hammarbybacken 27", "addressLocality": "Stockholm", "postalCode": "120 30", "addressCountry": "SE" }, "contactPoint": [ { "@type": "ContactPoint", "contactType": "customer service", "email": "hej@alicelabs.ai", "telephone": "+46734157476", "areaServed": [ "SE", "EU" ], "availableLanguage": [ "Swedish", "English" ] } ], "areaServed": [ { "@type": "Country", "name": "Sweden" }, { "@type": "Place", "name": "Europe" } ], "knowsAbout": [ "AI strategy", "AI implementation", "AI agents", "AI automation", "Generative AI", "AI governance", "AI training", "Machine learning", "Large language models", "RAG", "AI consulting", "Digital transformation", "AI search optimization", "LLMO", "AI for enterprise" ], "founder": [ { "@id": "https://alicelabs.ai/#linus" }, { "@id": "https://alicelabs.ai/#eric" } ], "sameAs": [ "https://www.linkedin.com/company/alicelabsai", "https://www.trustpilot.com/review/alicelabs.ai", "https://www.wikidata.org/wiki/Q140369570" ] }, { "@type": "Person", "@id": "https://alicelabs.ai/#linus", "name": "Linus Ingemarsson", "givenName": "Linus", "familyName": "Ingemarsson", "jobTitle": "Co-Founder", "description": "Co-founder of Alice Labs. 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All evidence was accessed on 2026-04-20 from official statistical agencies (BLS, ONS, OECD, Eurostat), central banks (Federal Reserve, FRBSF, Atlanta Fed, St. Louis Fed, NY Fed, BIS), and peer-reviewed or institutional research (NBER, MIT, HBS, BCG, Acemoglu, Aghion-Bunel, IMF).\n\nWorker-level evidence is robust for customer support (+14% throughput, +34% for novices), software development (+26% completed tasks across 4,867 devs), and bounded knowledge work (writing: -0.8 SD time, +0.4 SD quality). European firm-level evidence shows +4% short-run labor productivity gains from AI adoption with no adverse employment effects. Macro estimates remain widely dispersed: Acemoglu's conceptual model suggests +0.07 percentage points per year of TFP gain, while Aghion-Bunel and OECD high-exposure scenarios go up to +1.3 percentage points per year.\n\nThe report includes a 26-indicator machine-readable dataset (CSV/JSON), 12 citation-grade structured claims with confidence scores, and an evidence-strength taxonomy that explicitly distinguishes peer-reviewed RCTs, official statistics, central-bank surveys, and modeled scenarios. Findings are intentionally measurement-first: realized aggregate productivity gains remain difficult to attribute specifically to AI; the most defensible 2026 conclusion is \"clear gains in some tasks and firms, incomplete and uneven gains at the economy-wide level.\"", "datePublished": "2026-04-20", "dateModified": "2026-06-26", "version": "1.3", "author": [ { "@type": "Person", "name": "Linus Ingemarsson", "jobTitle": "Co-Founder, Alice Labs", "url": "https://alicelabs.ai/en/linus-ingemarsson", "worksFor": { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai" }, "sameAs": [ "https://alicelabs.ai/en/linus-ingemarsson" ] } ], "reviewedBy": [ { "@type": "Person", "name": "Eric Lundberg", "jobTitle": "Co-Founder, Alice Labs", "url": "https://alicelabs.ai/en/eric-lundberg", "worksFor": { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai" }, "sameAs": [ "https://alicelabs.ai/en/eric-lundberg" ] } ], "reviewDate": "2026-06-26", "publisher": { "@id": "https://alicelabs.ai/#organization" }, "mainEntityOfPage": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026", "keywords": "AI productivity, AI productivity impact, AI productivity 2026, generative AI productivity, AI labor productivity, AI total factor productivity, AI TFP, AI adoption and productivity, AI productivity by sector, AI productivity evidence, AI productivity research, AI macro productivity, AI economic impact 2026, AI ROI, AI productivity gains, generative AI worker productivity, AI customer support productivity, AI software developer productivity, AI coding assistant productivity, AI knowledge work productivity, AI productivity studies, BLS AI productivity, ONS AI adoption, Eurostat AI use, OECD AI productivity, IMF AI productivity, Federal Reserve AI adoption, Atlanta Fed AI, St Louis Fed AI productivity, NY Fed AI workplace, Acemoglu AI productivity, Aghion Bunel AI growth, AI J-curve productivity, AI productivity paradox 2026, AI workplace experiments, NBER AI productivity, MIT AI productivity study, HBS Jagged Frontier, AI productivity by country, EU AI firm productivity, AI training productivity, AI complementary investments, AI workflow redesign, AI sectoral productivity, AI in finance productivity, AI in manufacturing productivity, AI in public sector productivity, AI labor market reallocation, AI hiring slowdown, AI productivity benchmark, is AI raising productivity, does AI improve productivity, how much does AI boost productivity, AI productivity report 2026, global AI productivity report, AI economic productivity 2026, AI productivity by task, task augmentation productivity, AI productivity for novices, AI productivity gap large vs small firms, AI productivity outlook 2030, AI productivity Europe, AI productivity G7, AI productivity measurement, evidence AI productivity", "inLanguage": "en", "citation": "Ingemarsson, L. (2026, April 20). Global AI Productivity Impact Report 2026 (Version 1.0). Alice Labs. https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026", "hasPart": [ { "@type": "Article", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#definitions", "name": "Definitions & Measurement Framework", "position": 1, "isPartOf": { "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#article" } }, { "@type": "Article", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#scoreboard", "name": "AI Productivity Scoreboard", "position": 2, "isPartOf": { "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#article" } }, { "@type": "Article", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#q2-2026-update", "name": "Q2 2026 Update — Latest Insights (June 2026)", "position": 3, "isPartOf": { "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#article" } }, { "@type": "Article", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#expanded-june-2026", "name": "Deep Update — Expanded Analysis (June 2026)", "position": 4, "isPartOf": { "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#article" } }, { "@type": "Article", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#worker-evidence", "name": "Worker-Level Evidence: The Strongest Causal Layer", "position": 5, "isPartOf": { "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#article" } }, { "@type": "Article", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#firm-and-sector", "name": "Firm-Level & Adoption Evidence", "position": 6, "isPartOf": { "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#article" } }, { "@type": "Article", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#macro", "name": "Macroeconomic Translation: Why Estimates Diverge", "position": 7, "isPartOf": { "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#article" } }, { "@type": "Article", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#sectors", "name": "Sectoral AI Productivity Readiness", "position": 8, "isPartOf": { "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#article" } }, { "@type": "Article", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#barriers", "name": "Adoption Barriers & The Training Gap", "position": 9, "isPartOf": { "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#article" } }, { "@type": "Article", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#labor-market", "name": "Labor Market Effects & Reallocation", "position": 10, "isPartOf": { "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#article" } }, { "@type": "Article", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#glossary", "name": "Glossary — Defined Terms", "position": 11, "isPartOf": { "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#article" } }, { "@type": "Article", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#citation-version", "name": "How to Cite & Version History", "position": 12, "isPartOf": { "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#article" } }, { "@type": "Article", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#how-to-measure", "name": "How to Measure AI Productivity (6 Steps)", "position": 13, "isPartOf": { "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#article" } } ], "about": [ { "@type": "Claim", "name": "Customer support productivity rises 14% with AI; 34% for novices and low-skilled workers", "description": "AI compresses skill premiums within occupations — strongest equity-and-throughput case for deployment in scaled support functions.", "citation": "Brynjolfsson, Li & Raymond (NBER / QJE, 2023)" }, { "@type": "Claim", "name": "AI coding assistants raise completed developer tasks by 26% in field studies of 4,867 devs", "description": "Software is the most-validated AI productivity case at firm scale — robust across companies, not a single-firm artifact.", "citation": "Cui, Demirer, Jaffe, Musolff, Peng & Salz (NBER, 2025)" }, { "@type": "Claim", "name": "Professional writing tasks complete 0.8 SD faster and at 0.4 SD higher quality with ChatGPT", "description": "Knowledge work shows large gains on bounded writing tasks — but generalizability beyond writing is task-dependent (working paper).", "citation": "Noy & Zhang (MIT, 2023)" }, { "@type": "Claim", "name": "AI gains within the 'jagged frontier' reach +25% speed and +40% quality for consultants", "description": "Task-boundary awareness is now a core management skill: outside the frontier, AI use can degrade output.", "citation": "Dell'Acqua et al. (HBS / BCG study, 2023)" }, { "@type": "Claim", "name": "European firm-level AI adoption raises short-run labor productivity by 4% with no employment loss", "description": "Best firm-level evidence to date that adoption raises productivity at scale without near-term displacement — at least in Europe.", "citation": "BIS / EIB Working Paper (2026)" }, { "@type": "Claim", "name": "AI adoption is highly concentrated in large enterprises: 55% of EU firms with 250+ employees", "description": "AI productivity diffusion will track firm-size structure — SME adoption is the binding constraint on aggregate gains.", "citation": "Eurostat (Dec 2025)" }, { "@type": "Claim", "name": "US firm AI adoption reached 18% by year-end 2025, with 39% of workers reporting use", "description": "Worker use outpaces firm adoption — shadow AI is producing measured productivity that firm metrics may not yet capture.", "citation": "Federal Reserve Board; New York Fed" }, { "@type": "Claim", "name": "Macro TFP gain estimates span 0.07pp to 1.3pp per year — methodology dispersion is the story", "description": "Macro forecasts depend on assumed task coverage, adoption pace, and complementarities — boards should plan against scenarios, not point estimates.", "citation": "Acemoglu (NBER 2024); Aghion-Bunel / FRBSF (2024); OECD (2025)" }, { "@type": "Claim", "name": "St. Louis Fed implied aggregate gain from AI time savings: ~1.1% in 2024", "description": "Bottom-up bound from time-savings data — but national accounts have not yet shown this in measured TFP.", "citation": "St. Louis Fed (2025)" }, { "@type": "Claim", "name": "Top adoption barriers in EU: 70.9% lack of expertise, 52.5% legal uncertainty", "description": "Skills and legal certainty are the binding constraints — not technology cost — making training and governance the highest-leverage interventions.", "citation": "Eurostat Statistics Explained (Dec 2025)" }, { "@type": "Claim", "name": "Only 15.9% of US workers have employer-provided AI training despite 39% using AI at work", "description": "23-percentage-point training gap is the largest single drag on realized productivity — and the cheapest to close.", "citation": "Federal Reserve Bank of New York (April 2026)" }, { "@type": "Claim", "name": "Atlanta Fed executives expect 2pp+ annual labor productivity growth in high-skill services in 2026", "description": "Sector-level acceleration is forecast in finance and high-skill services — likely the first sectors where AI moves national-accounts productivity.", "citation": "Atlanta Fed Policy Hub (March 2026)" } ] }, { "@type": "Dataset", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#dataset", "name": "Global AI Productivity Impact Report 2026: Evidence, Sectors & Macro - Data Sources", "description": "Data sources and references used in Global AI Productivity Impact Report 2026: Evidence, Sectors & Macro", "creator": { "@id": "https://alicelabs.ai/#organization" }, "datePublished": "2026-04-20", "license": "https://creativecommons.org/licenses/by/4.0/", "distribution": [ { "@type": "DataDownload", "name": "Scoreboard CSV", "contentUrl": "https://alicelabs.ai/data/scoreboard.csv", "encodingFormat": "text/csv" }, { "@type": "DataDownload", "name": "Scoreboard JSON", "contentUrl": "https://alicelabs.ai/data/scoreboard.json", "encodingFormat": "application/json" }, { "@type": "DataDownload", "name": "Brynjolfsson, Li & Raymond — Generative AI at Work (NBER w31161 / QJE)", "contentUrl": "https://www.nber.org/papers/w31161", "description": "Customer support RCT: +14% throughput, +34% novice gain" }, { "@type": "DataDownload", "name": "Cui et al. — Effects of Generative AI on Software Developer Productivity (NBER w33777)", "contentUrl": "https://www.nber.org/papers/w33777", "description": "4,867 developers across three firms: +26.08% completed tasks" }, { "@type": "DataDownload", "name": "Noy & Zhang — Experimental Evidence on the Productivity Effects of Generative AI (MIT)", "contentUrl": "https://economics.mit.edu/sites/default/files/inline-files/Noy_Zhang_1.pdf", "description": "Writing tasks: -0.8 SD time, +0.4 SD quality" }, { "@type": "DataDownload", "name": "Dell'Acqua et al. — Navigating the Jagged Technological Frontier (HBS / BCG)", "contentUrl": "https://www.hbs.edu/faculty/Pages/item.aspx?num=64700", "description": "BCG consultants: +25% speed, +40% quality in-frontier" }, { "@type": "DataDownload", "name": "BIS / EIB — AI Adoption, Productivity and Employment in European Firms", "contentUrl": "https://www.bis.org/publ/work1220.htm", "description": "+4% short-run firm labor productivity gain, no employment loss" }, { "@type": "DataDownload", "name": "Federal Reserve Board — Monitoring AI Adoption in the US Economy", "contentUrl": "https://www.federalreserve.gov/econres/notes/feds-notes/monitoring-ai-adoption-in-the-us-economy.htm", "description": "18% US firm AI adoption (year-end 2025)" }, { "@type": "DataDownload", "name": "Federal Reserve Bank of New York — Use of Gen AI in the Workplace (April 2026)", "contentUrl": "https://www.newyorkfed.org/research/blog/2026/04/use-of-gen-ai-in-the-workplace-and-the-value-of-access-to-training", "description": "39% workers using AI; 15.9% trained" }, { "@type": "DataDownload", "name": "Atlanta Fed Policy Hub — AI, Productivity & the Workforce (March 2026)", "contentUrl": "https://www.atlantafed.org/research/publications/policy-hub/2026/03/ai-productivity-workforce-corporate-executives", "description": "Executive survey: +0.8pp 2025, +2pp+ expected 2026" }, { "@type": "DataDownload", "name": "St. Louis Fed — Impact of Generative AI on Work Productivity", "contentUrl": "https://www.stlouisfed.org/on-the-economy/2025/feb/impact-generative-ai-work-productivity", "description": "5.4% time savings; 1.1% implied aggregate gain" }, { "@type": "DataDownload", "name": "Eurostat — Use of Artificial Intelligence in Enterprises (Dec 2025)", "contentUrl": "https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises", "description": "20.0% EU enterprises; 55.03% large; barriers data" }, { "@type": "DataDownload", "name": "ONS — Management Practices and Adoption of Technology and AI in UK Firms", "contentUrl": "https://www.ons.gov.uk/economy/economicoutputandproductivity/productivitymeasures/articles/managementpracticesandtheadoptionoftechnologyandaiinukfirms/2025-03-24", "description": "9% UK firm AI adoption (2023 data)" }, { "@type": "DataDownload", "name": "Acemoglu — The Simple Macroeconomics of AI (NBER w32487)", "contentUrl": "https://www.nber.org/papers/w32487", "description": "+0.07pp/yr TFP — conceptual lower bound" }, { "@type": "DataDownload", "name": "Aghion & Bunel — AI and Growth: Where Do We Stand? (FRBSF)", "contentUrl": "https://www.frbsf.org/research-and-insights/publications/economic-letter/2024/06/ai-and-growth-where-do-we-stand", "description": "Up to +1.3pp/yr — historical analogy" }, { "@type": "DataDownload", "name": "OECD — Macroeconomic Productivity Gains from AI in G7 Economies", "contentUrl": "https://www.oecd.org/en/publications/macroeconomic-productivity-gains-from-artificial-intelligence-in-g7-economies_a8b3f5cb-en.html", "description": "+0.4 to +1.3pp/yr labor productivity (high-exposure)" }, { "@type": "DataDownload", "name": "Stanford HAI — AI Index Report 2025", "contentUrl": "https://hai.stanford.edu/ai-index/2025-ai-index-report", "description": "78% of organizations reporting AI use (2024 reference); adoption concentrated in IT, marketing, and product" }, { "@type": "DataDownload", "name": "OECD — AI Outlook 2025", "contentUrl": "https://www.oecd.org/en/publications/2025/06/the-oecd-ai-outlook_3b80de5d.html", "description": "G7 sector breakdown; finance, ICT, professional services show statistically detectable labor-productivity inflections" }, { "@type": "DataDownload", "name": "BLS — Productivity and Costs, Q1 2026", "contentUrl": "https://www.bls.gov/news.release/prod2.nr0.htm", "description": "US nonfarm business labor productivity +1.3% annualized in Q1 2026" }, { "@type": "DataDownload", "name": "McKinsey — The State of AI 2026", "contentUrl": "https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai", "description": "21% of organizations report material gen-AI EBIT impact (>5%), up from 11% in prior wave" } ] }, { "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://alicelabs.ai/" }, { "@type": "ListItem", "position": 2, "name": "Reports", "item": "https://alicelabs.ai/reports" }, { "@type": "ListItem", "position": 3, "name": "Global AI Productivity Impact Report 2026: Evidence, Sectors & Macro", "item": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026" } ] }, { "@type": "FAQPage", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#faq", "mainEntity": [ { "@type": "Question", "name": "Does AI actually increase productivity in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Yes — but only at specific scales. Worker-task evidence is robust: +14% in customer support (+34% for novices), +26% completed tasks for software developers, and large gains in bounded knowledge work. Firm-level evidence is positive in Europe (+4% labor productivity, no employment loss). Macro-level gains remain incomplete and difficult to attribute in national accounts." } }, { "@type": "Question", "name": "How much does AI improve worker productivity?", "acceptedAnswer": { "@type": "Answer", "text": "It depends on the task. The strongest evidence: +14% issues per hour in customer support (Brynjolfsson, Li & Raymond, NBER/QJE), +26.08% completed tasks for developers (Cui et al., NBER 2025), -0.8 SD time and +0.4 SD quality on professional writing (MIT). Within the AI 'jagged frontier' for consultants: +25% speed, +40% quality. Outside the frontier, AI use can degrade output." } }, { "@type": "Question", "name": "Why isn't AI showing up in macro productivity statistics yet?", "acceptedAnswer": { "@type": "Answer", "text": "Three reasons. First, adoption is incomplete: 18% of US firms, 20% of EU enterprises, 9% of UK firms. Second, complementary investments (training, workflow redesign, data) lag behind technology adoption. Third, the J-curve hypothesis predicts measured productivity dips before AI gains materialize in national accounts. Realized aggregate gains remain difficult to attribute specifically to AI in 2026." } }, { "@type": "Question", "name": "What is the macroeconomic impact of AI on productivity?", "acceptedAnswer": { "@type": "Answer", "text": "Estimates diverge sharply. Acemoglu's conceptual model: +0.07 percentage points per year of TFP gain. Aghion-Bunel and OECD high-exposure scenarios: up to +1.3 percentage points per year. IMF projects +1.0% cumulative TFP for Europe over 5 years, with a ~30% drag if regulation is binding. Boards should plan against scenarios, not point estimates." } }, { "@type": "Question", "name": "Which sectors see the biggest AI productivity gains?", "acceptedAnswer": { "@type": "Answer", "text": "Software development (validated at scale across 4,867 devs), customer support (largest novice gains), and high-skill services & finance (Atlanta Fed expects +2pp+ in 2026). Marketing, professional services, and public administration show medium-strength evidence. Manufacturing, healthcare, construction, and physical retail show weaker evidence so far." } }, { "@type": "Question", "name": "What is the biggest barrier to AI productivity gains?", "acceptedAnswer": { "@type": "Answer", "text": "Skills, not technology cost. 70.89% of EU enterprises cite lack of expertise; 52.52% cite legal uncertainty (Eurostat 2025). Only 15.9% of US workers have employer-provided AI training despite 39% using AI at work (NY Fed). The 23-percentage-point training gap is the single largest drag on realized productivity — and the cheapest to close." } }, { "@type": "Question", "name": "Does AI reduce jobs?", "acceptedAnswer": { "@type": "Answer", "text": "Not in aggregate, as of early 2026. BIS/EIB found +4% European firm productivity gains with no short-run employment loss. Fed Board labor monitoring shows AI-adopting firms shift posting skill-mix rather than reduce posting volume. Junior knowledge-work roles show the largest near-term exposure, but the dominant pattern is task reallocation, not aggregate hiring collapse." } }, { "@type": "Question", "name": "How does AI productivity differ between novice and experienced workers?", "acceptedAnswer": { "@type": "Answer", "text": "AI compresses within-occupation skill premiums. In the customer support RCT, novices and low-skilled workers gained +34%, while top performers showed no measurable gain. The HBS/Berkeley Kenya entrepreneur RCT shows the inverse: high-performers gained +20%, low-performers lost -10%. AI complements existing capability — it is not a uniform substitute." } }, { "@type": "Question", "name": "What is the AI 'jagged frontier'?", "acceptedAnswer": { "@type": "Answer", "text": "A concept from Dell'Acqua et al. (HBS / BCG, 2023). Within the frontier — tasks AI is well-suited to — gains reach +25% speed and +40% quality. Outside the frontier, AI use can degrade output. Task-boundary awareness is now a core management skill: naïve 'AI for everyone' rollouts produce average effects of zero or worse." } }, { "@type": "Question", "name": "How do I measure AI productivity in my organization?", "acceptedAnswer": { "@type": "Answer", "text": "Measure at three levels separately: (1) worker-task throughput and quality before/after AI access, (2) firm-level labor productivity (output per worker-hour), and (3) revenue or value-added per AI-adopting team. Never conflate the three. Use bounded, observable tasks for evaluation. Document baseline, post-rollout outcomes, and complementary investments (training hours, workflow changes, data quality)." } }, { "@type": "Question", "name": "What official statistics measure AI adoption?", "acceptedAnswer": { "@type": "Answer", "text": "Eurostat (annual ICT enterprise survey), US Census BTOS with AI supplement, Federal Reserve Board's Monitoring AI Adoption series, ONS UK firm-level surveys, and OECD's skills-based detection of AI vacancies across 14 countries. These use different definitions — keyword-based, skills-based, or self-reported — and produce different numbers. None should be treated as a single ground truth." } }, { "@type": "Question", "name": "How often is this report updated?", "acceptedAnswer": { "@type": "Answer", "text": "Quarterly refresh for adoption statistics, new field experiments, and labor-market monitoring. Annual full methodological revision aligned with major statistical release cycles (BLS OEWS, Eurostat ICT survey, OECD Compendium of Productivity Indicators). Each release is versioned with documented changes." } }, { "@type": "Question", "name": "What is the latest AI productivity report for 2026 (May–June 2026 evidence)?", "acceptedAnswer": { "@type": "Answer", "text": "As of late June 2026, the most current public-source evidence comes from five releases: Stanford HAI's AI Index 2025 (78% organizational AI use), OECD AI Outlook 2025 (+0.4 to +1.3pp/yr labor-productivity gain range for G7 high-exposure occupations), BLS Q1 2026 productivity release (US nonfarm labor productivity +1.3% annualized), Eurostat ICT-enterprise 2026 wave (EU AI use 20.0%, up 6.5pp YoY), and McKinsey State of AI 2026 (21% of organizations reporting material gen-AI EBIT impact, up from 11%). Together they confirm adoption is accelerating faster than measured macro productivity — the Q2 2026 AI productivity paradox." } }, { "@type": "Question", "name": "Are AI productivity gains real and uneven across firms in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Yes to both. The gains are real and well-identified at the worker-task level (+14% customer support, +26% developer tasks, +4% European firm labor productivity) and corroborated across multiple Q2 2026 sources. But they are uneven on three axes: (1) firm-size — EU large enterprises at 55.03% adoption versus the 20.0% all-enterprise average; (2) task — gains concentrated within the AI 'jagged frontier' and absent or negative outside it; (3) complementarity — only 15.9% of US workers receive employer-provided AI training despite 39% using AI at work. Without training and workflow redesign, worker-level gains do not propagate to firm or macro productivity." } }, { "@type": "Question", "name": "What does the latest NBER working paper say about AI productivity and labor in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "The most-cited NBER evidence in 2026 remains Cui, Demirer, Jaffe, Musolff, Peng & Salz (NBER w33777, 2025): a pooled randomized trial across Microsoft, Accenture, and an anonymized Fortune 100, totaling 4,867 developers, finding +26.08% completed tasks with AI coding assistants. On the macro side, Acemoglu (NBER w32487, 2024) sets the conservative bound at +0.07pp/yr TFP gain over the next decade. The two are not contradictory: large gains in bounded tasks need complementary investments to translate into macro TFP, which the 2026 evidence shows are still incomplete." } }, { "@type": "Question", "name": "Has US labor productivity growth in 2025–2026 been driven by AI?", "acceptedAnswer": { "@type": "Answer", "text": "Partly, but not yet dominantly. US nonfarm business labor productivity ran at roughly +2.0% in 2024 and +1.3% (annualized) in Q1 2026 per BLS. The BLS does not directly attribute the post-2023 acceleration to AI, and macroeconomists are split: Atlanta Fed executive survey data point to AI-driven gains of ~0.8pp in 2025 in high-skill services, while Acemoglu's conceptual model implies only +0.07pp/yr economy-wide for the coming decade. The most defensible 2026 conclusion: AI is contributing measurably in specific sectors (software, finance, high-skill services) but is not yet the dominant driver of headline US labor productivity growth." } }, { "@type": "Question", "name": "What does the NBER study on generative AI in customer support find (the 14% study)?", "acceptedAnswer": { "@type": "Answer", "text": "Brynjolfsson, Li & Raymond's NBER w31161 working paper (now published in the Quarterly Journal of Economics, 2025) studied 5,179 customer support agents at a Fortune 500 software company. Access to a generative AI assistant increased issues resolved per hour by 14% on average, with a 34% gain for novice and low-skilled workers and statistically zero gain for top performers. It is the foundational worker-task field study in the AI productivity literature and remains the most-cited 2026 evidence point." } }, { "@type": "Question", "name": "What are the AI productivity gains by industry in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Software & ICT and customer support / BPO show the strongest multi-source 2026 evidence: +26% completed developer tasks across 4,867 devs (Cui et al. NBER w33777) and +14% customer support throughput. Banking and financial services show ~0.8pp implied 2025 gain and +2pp+ expected 2026 (Atlanta Fed). Insurance, marketing, and risk-and-compliance show firm-reported gains (McKinsey State of AI 2026). Manufacturing, telecommunications, and retail show single-source firm-reported gains (NVIDIA State of AI series 2026). Healthcare and public administration show OECD-projected gains rather than realized productivity inflections. Construction and physical retail remain weak-evidence sectors." } }, { "@type": "Question", "name": "What is the latest AI productivity report for May 2026?", "acceptedAnswer": { "@type": "Answer", "text": "As of late June 2026, the most current public-source releases bearing on AI productivity are: (1) BLS Productivity and Costs Q1 2026 (June 2026 release, US nonfarm labor productivity +1.3% annualized), (2) Eurostat ICT-enterprise 2026 wave preliminary release (EU AI use 20.0%), (3) McKinsey State of AI 2026 (March 2026, 21% material EBIT impact), (4) US Census BTOS May 2026 AI supplement (~9% of US businesses using AI), and (5) KPMG AI Quarterly Pulse Survey Q1 2026 ($207M average enterprise AI spend). Together they confirm the 2026 AI productivity paradox: adoption and ROI are accelerating in specific use cases while macro labor-productivity growth remains within the post-pandemic band." } }, { "@type": "Question", "name": "How big is the global economic impact of AI in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Macro estimates span an order of magnitude. Acemoglu's conceptual lower bound (NBER w32487): +0.07pp/yr in TFP over the next decade. OECD AI Outlook 2025 central / high-exposure scenarios for G7 economies: +0.4 to +1.3pp/yr in labor productivity. Aghion & Bunel (FRBSF Economic Letter 2024-16): up to +1.3pp/yr by historical analogy. IMF (2025): +1.0% cumulative TFP gain for Europe over five years, with a ~30% drag if regulation is binding. Goldman Sachs Research has previously projected a $7 trillion cumulative global GDP uplift from generative AI over ten years (estimates remain widely dispersed and scenario-driven, not realized)." } }, { "@type": "Question", "name": "Why do 95% of enterprise AI pilots fail?", "acceptedAnswer": { "@type": "Answer", "text": "MIT NANDA's State of AI in Business 2025 attributes the ≈95% pilot-to-production failure rate primarily to four mechanisms: (1) data quality and accessibility — pilots depend on data that is not production-grade; (2) workflow integration — most pilots are point tools, not embedded in business processes; (3) change management — only 15.9% of US workers receive employer-provided AI training (NY Fed 2026); and (4) measurement — pilots optimize for technical metrics rather than P&L impact. Gartner separately forecasts that at least 30% of generative AI projects were abandoned at the proof-of-concept stage by end-2025 due to poor data quality, escalating costs, inadequate risk controls, and unclear business value." } }, { "@type": "Question", "name": "What is BCG's 10-20-70 rule for AI investment?", "acceptedAnswer": { "@type": "Answer", "text": "BCG's 10-20-70 heuristic, drawn from its enterprise AI consulting practice, recommends that organizations allocate roughly 10% of AI program investment to algorithms, 20% to technology and data, and 70% to people and process change — including training, workflow redesign, governance, and management practice upgrades. It directly mirrors this report's finding that complementary capabilities, not technology cost, are the binding constraint on realized AI productivity gains." } }, { "@type": "Question", "name": "How does the EU AI Act affect AI productivity gains in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "The EU AI Act (Regulation (EU) 2024/1689) entered into force in August 2024, with most obligations applying from 2 August 2026, including general-purpose AI rules and high-risk AI system requirements. IMF analysis suggests binding regulatory constraints could reduce projected EU AI productivity gains by ~30%. The Colorado AI Act, effective February 2026, is the first US state law imposing similar risk-management duties on developers and deployers of high-risk AI systems. Compliance costs and process redesign for high-risk systems are the dominant transmission channels." } }, { "@type": "Question", "name": "How does AI adoption in Sweden compare to the EU average in 2025?", "acceptedAnswer": { "@type": "Answer", "text": "Sweden is one of the highest-adoption AI markets in Europe. According to Statistics Sweden (SCB) data for 2025, approximately 35% of Swedish enterprises with 10+ employees report using AI, well above the EU average of 20.0% (Eurostat 2025). Adoption is concentrated in large enterprises and knowledge-intensive services. Public-sector adoption is guided by the Swedish Agency for Digital Government (DIGG) and the Swedish Authority for Privacy Protection (IMY), which have published AI governance guidance through 2025 and 2026." } }, { "@type": "Question", "name": "What is the AI productivity paradox in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "The AI productivity paradox in 2026 is the widening gap between firm-reported AI gains and macro-level productivity statistics. Firm and worker evidence is strong: +14% in customer support, +26% in software, +4% in EU firm labor productivity, 21% of organizations reporting material EBIT impact (McKinsey 2026). But US nonfarm labor productivity grew only +1.3% (annualized) in Q1 2026 (BLS) — within the post-pandemic band. The most defensible interpretation is that gains are real but compartmentalized, complementary investments lag adoption, and the AI productivity J-curve (measured slowdown before realized aggregate gain) is still consistent with the 2026 data." } } ] } ] }, { "@context": "https://schema.org", "@graph": [ { "@type": "Organization", "@id": "https://alicelabs.ai/#organization", "name": "Alice Labs", "url": "https://alicelabs.ai/", "logo": "https://alicelabs.ai/images/alice-logo.png", "sameAs": [ "https://www.linkedin.com/company/alicelabsai/", "https://www.trustpilot.com/review/alicelabs.ai", "https://www.wikidata.org/wiki/Q140369570" ] }, { "@type": "Person", "@id": "https://alicelabs.ai/en/linus-ingemarsson#person", "name": "Linus Ingemarsson", "jobTitle": "Co-Founder", "url": "https://alicelabs.ai/en/linus-ingemarsson", "affiliation": { "@id": "https://alicelabs.ai/#organization" }, "sameAs": [ "https://www.linkedin.com/in/linus-ingemarsson/", "https://www.wikidata.org/wiki/Q140369914" 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"inLanguage": "en-US", "creator": { "@id": "https://alicelabs.ai/en/linus-ingemarsson#person" }, "publisher": { "@id": "https://alicelabs.ai/#organization" }, "license": "https://creativecommons.org/licenses/by/4.0/", "isAccessibleForFree": true, "variableMeasured": [ "Customer support productivity gain", "Developer completed-tasks gain", "Writing task time and quality change", "Firm-level labor productivity gain", "Enterprise AI adoption rate", "Worker AI use rate", "Macro TFP gain estimates", "AI training availability", "AI adoption barriers" ], "spatialCoverage": [ "US", "EU", "UK", "G7", "Kenya", "Global" ], "distribution": [ { "@type": "DataDownload", "encodingFormat": "text/csv", "contentUrl": "https://alicelabs.ai/data/productivity-scoreboard.csv" }, { "@type": "DataDownload", "encodingFormat": "application/json", "contentUrl": "https://alicelabs.ai/data/productivity-scoreboard.json" } ] }, { "@type": "HowTo", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026/#how-to-measure", "name": "How to Measure AI Productivity in Your Organization", "description": "A reproducible 6-step workflow for CFOs, COOs and AI program leads to measure AI productivity at the worker, firm, and team levels.", "totalTime": "P28D", "step": [ { "@type": "HowToStep", "position": 1, "name": "Define the productivity object", "text": "Pick exactly one of: worker-task throughput, firm labor productivity (output per worker-hour), or revenue/value-added per AI-adopting team. Never conflate the three.", "url": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#how-to-measure" }, { "@type": "HowToStep", "position": 2, "name": "Pick a bounded, observable task", "text": "Customer-support tickets resolved, code commits merged, marketing drafts approved, contracts reviewed. Bounded tasks survive measurement; vague ones do not.", "url": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#how-to-measure" }, { "@type": "HowToStep", "position": 3, "name": "Measure baseline before AI access", "text": "Capture 4–8 weeks of pre-rollout data on the chosen task. Document the unit, the worker population, and any seasonal effects.", "url": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#how-to-measure" }, { "@type": "HowToStep", "position": 4, "name": "Run a controlled rollout", "text": "Randomize at the worker, team, or shift level if possible. Otherwise use a pre/post design with explicit controls. Document training hours given.", "url": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#how-to-measure" }, { "@type": "HowToStep", "position": 5, "name": "Track complementary investments", "text": "Workflow changes, data-quality work, prompt-library curation, governance reviews. Without these, gains stay at the worker level and never reach the firm P&L.", "url": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#how-to-measure" }, { "@type": "HowToStep", "position": 6, "name": "Report by skill segment", "text": "Separate novices from experienced workers (Brynjolfsson et al. pattern) and high-performers from low-performers (HBS/Berkeley pattern). Heterogeneity is often the headline.", "url": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026#how-to-measure" } ] }, { "@type": "FAQPage", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026/#faq", "mainEntity": [ { "@type": "Question", "name": "Does AI actually increase productivity in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Yes — but only at specific scales. Worker-task evidence is robust: +14% in customer support (+34% for novices), +26% completed tasks for software developers, and large gains in bounded knowledge work. Firm-level evidence is positive in Europe (+4% labor productivity, no employment loss). Macro-level gains remain incomplete and difficult to attribute in national accounts." } }, { "@type": "Question", "name": "How much does AI improve worker productivity?", "acceptedAnswer": { "@type": "Answer", "text": "It depends on the task. The strongest evidence: +14% issues per hour in customer support (Brynjolfsson, Li & Raymond, NBER/QJE), +26.08% completed tasks for developers (Cui et al., NBER 2025), -0.8 SD time and +0.4 SD quality on professional writing (MIT). Within the AI 'jagged frontier' for consultants: +25% speed, +40% quality. Outside the frontier, AI use can degrade output." } }, { "@type": "Question", "name": "Why isn't AI showing up in macro productivity statistics yet?", "acceptedAnswer": { "@type": "Answer", "text": "Three reasons. First, adoption is incomplete: 18% of US firms, 20% of EU enterprises, 9% of UK firms. Second, complementary investments (training, workflow redesign, data) lag behind technology adoption. Third, the J-curve hypothesis predicts measured productivity dips before AI gains materialize in national accounts. Realized aggregate gains remain difficult to attribute specifically to AI in 2026." } }, { "@type": "Question", "name": "What is the macroeconomic impact of AI on productivity?", "acceptedAnswer": { "@type": "Answer", "text": "Estimates diverge sharply. Acemoglu's conceptual model: +0.07 percentage points per year of TFP gain. Aghion-Bunel and OECD high-exposure scenarios: up to +1.3 percentage points per year. IMF projects +1.0% cumulative TFP for Europe over 5 years, with a ~30% drag if regulation is binding. Boards should plan against scenarios, not point estimates." } }, { "@type": "Question", "name": "Which sectors see the biggest AI productivity gains?", "acceptedAnswer": { "@type": "Answer", "text": "Software development (validated at scale across 4,867 devs), customer support (largest novice gains), and high-skill services & finance (Atlanta Fed expects +2pp+ in 2026). Marketing, professional services, and public administration show medium-strength evidence. Manufacturing, healthcare, construction, and physical retail show weaker evidence so far." } }, { "@type": "Question", "name": "What is the biggest barrier to AI productivity gains?", "acceptedAnswer": { "@type": "Answer", "text": "Skills, not technology cost. 70.89% of EU enterprises cite lack of expertise; 52.52% cite legal uncertainty (Eurostat 2025). Only 15.9% of US workers have employer-provided AI training despite 39% using AI at work (NY Fed). The 23-percentage-point training gap is the single largest drag on realized productivity — and the cheapest to close." } }, { "@type": "Question", "name": "Does AI reduce jobs?", "acceptedAnswer": { "@type": "Answer", "text": "Not in aggregate, as of early 2026. BIS/EIB found +4% European firm productivity gains with no short-run employment loss. Fed Board labor monitoring shows AI-adopting firms shift posting skill-mix rather than reduce posting volume. Junior knowledge-work roles show the largest near-term exposure, but the dominant pattern is task reallocation, not aggregate hiring collapse." } }, { "@type": "Question", "name": "How does AI productivity differ between novice and experienced workers?", "acceptedAnswer": { "@type": "Answer", "text": "AI compresses within-occupation skill premiums. In the customer support RCT, novices and low-skilled workers gained +34%, while top performers showed no measurable gain. The HBS/Berkeley Kenya entrepreneur RCT shows the inverse: high-performers gained +20%, low-performers lost -10%. AI complements existing capability — it is not a uniform substitute." } }, { "@type": "Question", "name": "What is the AI 'jagged frontier'?", "acceptedAnswer": { "@type": "Answer", "text": "A concept from Dell'Acqua et al. (HBS / BCG, 2023). Within the frontier — tasks AI is well-suited to — gains reach +25% speed and +40% quality. Outside the frontier, AI use can degrade output. Task-boundary awareness is now a core management skill: naïve 'AI for everyone' rollouts produce average effects of zero or worse." } }, { "@type": "Question", "name": "How do I measure AI productivity in my organization?", "acceptedAnswer": { "@type": "Answer", "text": "Measure at three levels separately: (1) worker-task throughput and quality before/after AI access, (2) firm-level labor productivity (output per worker-hour), and (3) revenue or value-added per AI-adopting team. Never conflate the three. Use bounded, observable tasks for evaluation. Document baseline, post-rollout outcomes, and complementary investments (training hours, workflow changes, data quality)." } }, { "@type": "Question", "name": "What official statistics measure AI adoption?", "acceptedAnswer": { "@type": "Answer", "text": "Eurostat (annual ICT enterprise survey), US Census BTOS with AI supplement, Federal Reserve Board's Monitoring AI Adoption series, ONS UK firm-level surveys, and OECD's skills-based detection of AI vacancies across 14 countries. These use different definitions — keyword-based, skills-based, or self-reported — and produce different numbers. None should be treated as a single ground truth." } }, { "@type": "Question", "name": "How often is this report updated?", "acceptedAnswer": { "@type": "Answer", "text": "Quarterly refresh for adoption statistics, new field experiments, and labor-market monitoring. Annual full methodological revision aligned with major statistical release cycles (BLS OEWS, Eurostat ICT survey, OECD Compendium of Productivity Indicators). Each release is versioned with documented changes." } }, { "@type": "Question", "name": "What is the latest AI productivity report for 2026 (May–June 2026 evidence)?", "acceptedAnswer": { "@type": "Answer", "text": "As of late June 2026, the most current public-source evidence comes from five releases: Stanford HAI's AI Index 2025 (78% organizational AI use), OECD AI Outlook 2025 (+0.4 to +1.3pp/yr labor-productivity gain range for G7 high-exposure occupations), BLS Q1 2026 productivity release (US nonfarm labor productivity +1.3% annualized), Eurostat ICT-enterprise 2026 wave (EU AI use 20.0%, up 6.5pp YoY), and McKinsey State of AI 2026 (21% of organizations reporting material gen-AI EBIT impact, up from 11%). Together they confirm adoption is accelerating faster than measured macro productivity — the Q2 2026 AI productivity paradox." } }, { "@type": "Question", "name": "Are AI productivity gains real and uneven across firms in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Yes to both. The gains are real and well-identified at the worker-task level (+14% customer support, +26% developer tasks, +4% European firm labor productivity) and corroborated across multiple Q2 2026 sources. But they are uneven on three axes: (1) firm-size — EU large enterprises at 55.03% adoption versus the 20.0% all-enterprise average; (2) task — gains concentrated within the AI 'jagged frontier' and absent or negative outside it; (3) complementarity — only 15.9% of US workers receive employer-provided AI training despite 39% using AI at work. Without training and workflow redesign, worker-level gains do not propagate to firm or macro productivity." } }, { "@type": "Question", "name": "What does the latest NBER working paper say about AI productivity and labor in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "The most-cited NBER evidence in 2026 remains Cui, Demirer, Jaffe, Musolff, Peng & Salz (NBER w33777, 2025): a pooled randomized trial across Microsoft, Accenture, and an anonymized Fortune 100, totaling 4,867 developers, finding +26.08% completed tasks with AI coding assistants. On the macro side, Acemoglu (NBER w32487, 2024) sets the conservative bound at +0.07pp/yr TFP gain over the next decade. The two are not contradictory: large gains in bounded tasks need complementary investments to translate into macro TFP, which the 2026 evidence shows are still incomplete." } }, { "@type": "Question", "name": "Has US labor productivity growth in 2025–2026 been driven by AI?", "acceptedAnswer": { "@type": "Answer", "text": "Partly, but not yet dominantly. US nonfarm business labor productivity ran at roughly +2.0% in 2024 and +1.3% (annualized) in Q1 2026 per BLS. The BLS does not directly attribute the post-2023 acceleration to AI, and macroeconomists are split: Atlanta Fed executive survey data point to AI-driven gains of ~0.8pp in 2025 in high-skill services, while Acemoglu's conceptual model implies only +0.07pp/yr economy-wide for the coming decade. The most defensible 2026 conclusion: AI is contributing measurably in specific sectors (software, finance, high-skill services) but is not yet the dominant driver of headline US labor productivity growth." } }, { "@type": "Question", "name": "What does the NBER study on generative AI in customer support find (the 14% study)?", "acceptedAnswer": { "@type": "Answer", "text": "Brynjolfsson, Li & Raymond's NBER w31161 working paper (now published in the Quarterly Journal of Economics, 2025) studied 5,179 customer support agents at a Fortune 500 software company. Access to a generative AI assistant increased issues resolved per hour by 14% on average, with a 34% gain for novice and low-skilled workers and statistically zero gain for top performers. It is the foundational worker-task field study in the AI productivity literature and remains the most-cited 2026 evidence point." } }, { "@type": "Question", "name": "What are the AI productivity gains by industry in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Software & ICT and customer support / BPO show the strongest multi-source 2026 evidence: +26% completed developer tasks across 4,867 devs (Cui et al. NBER w33777) and +14% customer support throughput. Banking and financial services show ~0.8pp implied 2025 gain and +2pp+ expected 2026 (Atlanta Fed). Insurance, marketing, and risk-and-compliance show firm-reported gains (McKinsey State of AI 2026). Manufacturing, telecommunications, and retail show single-source firm-reported gains (NVIDIA State of AI series 2026). Healthcare and public administration show OECD-projected gains rather than realized productivity inflections. Construction and physical retail remain weak-evidence sectors." } }, { "@type": "Question", "name": "What is the latest AI productivity report for May 2026?", "acceptedAnswer": { "@type": "Answer", "text": "As of late June 2026, the most current public-source releases bearing on AI productivity are: (1) BLS Productivity and Costs Q1 2026 (June 2026 release, US nonfarm labor productivity +1.3% annualized), (2) Eurostat ICT-enterprise 2026 wave preliminary release (EU AI use 20.0%), (3) McKinsey State of AI 2026 (March 2026, 21% material EBIT impact), (4) US Census BTOS May 2026 AI supplement (~9% of US businesses using AI), and (5) KPMG AI Quarterly Pulse Survey Q1 2026 ($207M average enterprise AI spend). Together they confirm the 2026 AI productivity paradox: adoption and ROI are accelerating in specific use cases while macro labor-productivity growth remains within the post-pandemic band." } }, { "@type": "Question", "name": "How big is the global economic impact of AI in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Macro estimates span an order of magnitude. Acemoglu's conceptual lower bound (NBER w32487): +0.07pp/yr in TFP over the next decade. OECD AI Outlook 2025 central / high-exposure scenarios for G7 economies: +0.4 to +1.3pp/yr in labor productivity. Aghion & Bunel (FRBSF Economic Letter 2024-16): up to +1.3pp/yr by historical analogy. IMF (2025): +1.0% cumulative TFP gain for Europe over five years, with a ~30% drag if regulation is binding. Goldman Sachs Research has previously projected a $7 trillion cumulative global GDP uplift from generative AI over ten years (estimates remain widely dispersed and scenario-driven, not realized)." } }, { "@type": "Question", "name": "Why do 95% of enterprise AI pilots fail?", "acceptedAnswer": { "@type": "Answer", "text": "MIT NANDA's State of AI in Business 2025 attributes the ≈95% pilot-to-production failure rate primarily to four mechanisms: (1) data quality and accessibility — pilots depend on data that is not production-grade; (2) workflow integration — most pilots are point tools, not embedded in business processes; (3) change management — only 15.9% of US workers receive employer-provided AI training (NY Fed 2026); and (4) measurement — pilots optimize for technical metrics rather than P&L impact. Gartner separately forecasts that at least 30% of generative AI projects were abandoned at the proof-of-concept stage by end-2025 due to poor data quality, escalating costs, inadequate risk controls, and unclear business value." } }, { "@type": "Question", "name": "What is BCG's 10-20-70 rule for AI investment?", "acceptedAnswer": { "@type": "Answer", "text": "BCG's 10-20-70 heuristic, drawn from its enterprise AI consulting practice, recommends that organizations allocate roughly 10% of AI program investment to algorithms, 20% to technology and data, and 70% to people and process change — including training, workflow redesign, governance, and management practice upgrades. It directly mirrors this report's finding that complementary capabilities, not technology cost, are the binding constraint on realized AI productivity gains." } }, { "@type": "Question", "name": "How does the EU AI Act affect AI productivity gains in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "The EU AI Act (Regulation (EU) 2024/1689) entered into force in August 2024, with most obligations applying from 2 August 2026, including general-purpose AI rules and high-risk AI system requirements. IMF analysis suggests binding regulatory constraints could reduce projected EU AI productivity gains by ~30%. The Colorado AI Act, effective February 2026, is the first US state law imposing similar risk-management duties on developers and deployers of high-risk AI systems. Compliance costs and process redesign for high-risk systems are the dominant transmission channels." } }, { "@type": "Question", "name": "How does AI adoption in Sweden compare to the EU average in 2025?", "acceptedAnswer": { "@type": "Answer", "text": "Sweden is one of the highest-adoption AI markets in Europe. According to Statistics Sweden (SCB) data for 2025, approximately 35% of Swedish enterprises with 10+ employees report using AI, well above the EU average of 20.0% (Eurostat 2025). Adoption is concentrated in large enterprises and knowledge-intensive services. Public-sector adoption is guided by the Swedish Agency for Digital Government (DIGG) and the Swedish Authority for Privacy Protection (IMY), which have published AI governance guidance through 2025 and 2026." } }, { "@type": "Question", "name": "What is the AI productivity paradox in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "The AI productivity paradox in 2026 is the widening gap between firm-reported AI gains and macro-level productivity statistics. Firm and worker evidence is strong: +14% in customer support, +26% in software, +4% in EU firm labor productivity, 21% of organizations reporting material EBIT impact (McKinsey 2026). But US nonfarm labor productivity grew only +1.3% (annualized) in Q1 2026 (BLS) — within the post-pandemic band. The most defensible interpretation is that gains are real but compartmentalized, complementary investments lag adoption, and the AI productivity J-curve (measured slowdown before realized aggregate gain) is still consistent with the 2026 data." } } ] }, { "@type": "DigitalDocument", "@id": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026/#digital-document", "name": "Global AI Productivity Impact Report 2026: Evidence, Sectors & Macro", "url": "https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026", "datePublished": "2026-04-20", "dateModified": "2026-06-26", "version": "1.3", "inLanguage": "en-US", "isAccessibleForFree": true, "author": { "@id": "https://alicelabs.ai/en/linus-ingemarsson#person" }, "publisher": { "@id": "https://alicelabs.ai/#organization" }, "license": "https://creativecommons.org/licenses/by/4.0/", "encodingFormat": "text/html" }, { "@type": "DefinedTermSet", "@id": 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Global AI Productivity Impact Report 2026: Evidence, Sectors & Macro Research Report Published April 2026 Updated June 26, 2026 v1.3 # Global AI Productivity Impact Report 2026: Evidence, Sectors & Macro Public-source desk research on measurable AI productivity gains across workers, firms, sectors, and economies — for boards, economists, and policy leaders Authors: [Linus Ingemarsson](https://alicelabs.ai/en/linus-ingemarsson)(Co-Founder, Alice Labs) [How to Cite](#cite) +26% Developer Tasks Completed 4,867 devs (Cui et al., 2025) +14% Customer Support Throughput +34% for novices (NBER/QJE) +4% EU Firm Productivity Short-run, no employment loss (BIS/EIB) 26 Public Indicators Citation-grade dataset ## Contents - [At a Glance](#at-a-glance) - [Executive Summary](#executive-summary) - [Key Findings (12)](#key-findings) - [Definitions & Measurement Framework](#definitions) - [AI Productivity Scoreboard](#scoreboard) - [Q2 2026 Update — Latest Insights (June 2026)](#q2-2026-update) - [Deep Update — Expanded Analysis (June 2026)](#expanded-june-2026) - [Worker-Level Evidence: The Strongest Causal Layer](#worker-evidence) - [Firm-Level & Adoption Evidence](#firm-and-sector) - [Macroeconomic Translation: Why Estimates Diverge](#macro) - [Sectoral AI Productivity Readiness](#sectors) - [Adoption Barriers & The Training Gap](#barriers) - [Labor Market Effects & Reallocation](#labor-market) - [Glossary — Defined Terms](#glossary) - [How to Cite & Version History](#citation-version) - [How to Measure AI Productivity (6 Steps)](#how-to-measure) - [FAQ](#faq) - [Methodology](#methodology) - [Cite](#cite) ![Linus Ingemarsson - Author at Alice Labs](/images/linus-ingemarsson.png) Written by [Linus Ingemarsson ](/en/linus-ingemarsson) ![Eric Lundberg - Reviewer at Alice Labs](/images/eric-lundberg.png) Reviewed by [Eric Lundberg ](/en/eric-lundberg) Published April 20, 2026 · Updated June 26, 2026 Methodology & Transparency: This analysis draws on primary sources — including Eurostat, OECD, national statistical agencies, peer-reviewed literature, and official vendor disclosures — combined with Alice Labs implementation data. AI tooling assists synthesis; every claim is human-reviewed against the cited source. **All figures and claims link to their public source for verification.** Reviewed by the named author and reviewer above. Methodology, source list, and revision history are available below. ## Cite This Report APABIBTEXMLA Ingemarsson, L. (2026, April 20). Global AI Productivity Impact Report 2026 (Version 1.0). Alice Labs. https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026 Copy citationVersion 1.3 • Published April 20, 2026 AT A GLANCE Published 2026-04-20 Last reviewed: 26 June 2026 v1.2 In 2026, AI delivers measurable productivity gains in specific tasks: **+14%** in customer support (**+34%** for novices), **+26%** more completed developer tasks, and **+4%** short-run EU firm-level labor productivity. Macro-level gains remain incomplete and unevenly attributable in national accounts. Key Takeaway The **Global AI Productivity Impact Report 2026** (published **2026-04-20**) evaluates whether AI is generating measurable productivity gains across workers, firms, sectors, and economies — using only public, official, or peer-reviewed sources. The strongest causal evidence is at the **worker-task level**: customer-support agents (+14%, +34% for novices), software developers (+26%), and bounded knowledge work all show robust gains. **Firm-level evidence** is increasingly positive — European firm data show short-run labor productivity gains without broad employment losses. The **macro picture** is less settled. Credible annual TFP-gain estimates range from **+0.07pp** (Acemoglu's conceptual model) to **+1.3pp** (Aghion-Bunel; OECD high-exposure scenario) over the next decade. The dispersion reflects uncertainty about task coverage, adoption speed, and complementary investments in training, data, software, and workflow redesign. Official adoption data show diffusion is still incomplete and uneven: **large EU enterprises (55%)** are 2.75× more likely to use AI than the EU average (20%). Limitations: realized aggregate productivity remains difficult to attribute specifically to AI; many high-profile studies are task-specific; official adoption measures are not fully harmonized across jurisdictions; macro estimates are scenario-driven rather than realized. The widely circulated MIT preprint on AI and scientific discovery was excluded after MIT stated it had no confidence in the paper's provenance, reliability, or validity. ## Executive Summary **"AI productivity" is not a single statistical object.** Worker throughput, firm revenue productivity, and economy-wide total factor productivity are different measurement systems. The 2026 evidence base shows a clear pattern: AI is producing **large, well-identified gains in specific tasks and firms**, while **economy-wide gains remain incomplete and difficult to attribute** in national accounts. The strongest causal evidence comes from **worker- and task-level field experiments**. Customer support shows +14% issues resolved per hour, with +34% for novices (NBER/QJE). Software development shows +26.08% completed tasks across 4,867 developers in three firms (Cui et al., NBER 2025). Bounded knowledge work — writing, consulting tasks within the AI frontier — shows large quality and speed gains (MIT; HBS/BCG). Outside the AI frontier, gains evaporate or reverse: in the HBS/Berkeley Kenya field RCT, average treatment effect was zero, with gains concentrated among already-high-performing entrepreneurs (+20%) and losses for lower performers (-10%). **Firm-level evidence** is increasingly positive. BIS/EIB analysis of European firms reports a **+4% short-run labor productivity gain** from AI adoption with no adverse short-run employment effect. Atlanta Fed executive surveys point to particularly strong gains in **high-skill services and finance**, with implied annual labor productivity contributions of ~0.8pp in 2025 and 2pp+ expected for 2026. **Macro estimates remain widely dispersed.** Acemoglu's conceptual model implies +0.07 percentage points per year of TFP gain over the next decade; Aghion-Bunel and OECD's high-exposure scenarios reach +1.3 percentage points per year. IMF projects ~+1.0% cumulative TFP gain in Europe over five years, with a ~30% drag if regulation is binding. Adoption itself is incomplete: **18% of US firms** (Fed Board, 2025), **20% of EU enterprises** (Eurostat, Dec 2025), **9% of UK firms** (ONS, 2023). Diffusion is heavily concentrated in large enterprises (55% of EU firms with 250+ employees) and knowledge-intensive services. The dominant blockers are **complementary capabilities**, not the technology itself: 70.9% of EU enterprises cite lack of expertise; 52.5% cite legal uncertainty (Eurostat). Only 15.9% of US workers report employer-provided AI training (NY Fed, 2026). Without training, organizational redesign, data quality, and management practice upgrades, worker-level gains do not propagate to firm or macro productivity. Capturing the 23–44% productivity uplift documented here requires the complementary capabilities the data calls out. Alice Labs operates as an [AI implementation consultant](/en/ai-consulting) closing the workflow + management-practice gap, delivers [enterprise AI training](/en/ai-training) that addresses the 70.9% skills barrier head-on, and provides [AI strategy consulting](/en/ai-strategy) for boards measuring real ROI versus the jagged frontier. ## Key Findings 12 data-driven insights ### 01 Customer support productivity rises 14% with AI; 34% for novices and low-skilled workers +14% issues resolved per hour; +34% for novices AI compresses skill premiums within occupations — strongest equity-and-throughput case for deployment in scaled support functions. Source: [Brynjolfsson, Li & Raymond (NBER / QJE, 2023)](https://www.nber.org/papers/w31161) ### 02 AI coding assistants raise completed developer tasks by 26% in field studies of 4,867 devs +26.08% completed tasks across three firms Software is the most-validated AI productivity case at firm scale — robust across companies, not a single-firm artifact. Source: [Cui, Demirer, Jaffe, Musolff, Peng & Salz (NBER, 2025)](https://www.nber.org/papers/w33777) ### 03 Professional writing tasks complete 0.8 SD faster and at 0.4 SD higher quality with ChatGPT \-0.8 SD time, +0.4 SD quality Knowledge work shows large gains on bounded writing tasks — but generalizability beyond writing is task-dependent (working paper). Source: [Noy & Zhang (MIT, 2023)](https://economics.mit.edu/sites/default/files/inline-files/Noy_Zhang_1.pdf) ### 04 AI gains within the 'jagged frontier' reach +25% speed and +40% quality for consultants +25% speed, +40% human-rated quality on in-frontier tasks Task-boundary awareness is now a core management skill: outside the frontier, AI use can degrade output. Source: [Dell'Acqua et al. (HBS / BCG study, 2023)](https://www.hbs.edu/faculty/Pages/item.aspx?num=64700) ### 05 European firm-level AI adoption raises short-run labor productivity by 4% with no employment loss +4% short-run labor productivity Best firm-level evidence to date that adoption raises productivity at scale without near-term displacement — at least in Europe. Source: [BIS / EIB Working Paper (2026)](https://www.bis.org/publ/work1220.htm) ### 06 AI adoption is highly concentrated in large enterprises: 55% of EU firms with 250+ employees 20.0% of all EU enterprises; 55.03% of large enterprises AI productivity diffusion will track firm-size structure — SME adoption is the binding constraint on aggregate gains. Source: [Eurostat (Dec 2025)](https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251211-1) ### 07 US firm AI adoption reached 18% by year-end 2025, with 39% of workers reporting use 18% firms (Fed Board); 39% workers (NY Fed) Worker use outpaces firm adoption — shadow AI is producing measured productivity that firm metrics may not yet capture. Source: [Federal Reserve Board; New York Fed](https://www.federalreserve.gov/econres/notes/feds-notes/monitoring-ai-adoption-in-the-us-economy.htm) ### 08 Macro TFP gain estimates span 0.07pp to 1.3pp per year — methodology dispersion is the story Acemoglu +0.07pp/yr; Aghion-Bunel & OECD high-exposure up to +1.3pp/yr Macro forecasts depend on assumed task coverage, adoption pace, and complementarities — boards should plan against scenarios, not point estimates. Source: [Acemoglu (NBER 2024); Aghion-Bunel / FRBSF (2024); OECD (2025)](https://www.nber.org/papers/w32487) ### 09 St. Louis Fed implied aggregate gain from AI time savings: ~1.1% in 2024 5.4% of user work hours saved → +1.1% implied aggregate productivity Bottom-up bound from time-savings data — but national accounts have not yet shown this in measured TFP. Source: [St. Louis Fed (2025)](https://www.stlouisfed.org/on-the-economy/2025/feb/impact-generative-ai-work-productivity) ### 10 Top adoption barriers in EU: 70.9% lack of expertise, 52.5% legal uncertainty 70.89% expertise; 52.52% legal clarity Skills and legal certainty are the binding constraints — not technology cost — making training and governance the highest-leverage interventions. Source: [Eurostat Statistics Explained (Dec 2025)](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises) ### 11 Only 15.9% of US workers have employer-provided AI training despite 39% using AI at work 15.9% trained vs 39% using AI 23-percentage-point training gap is the largest single drag on realized productivity — and the cheapest to close. Source: [Federal Reserve Bank of New York (April 2026)](https://www.newyorkfed.org/research/blog/2026/04/use-of-gen-ai-in-the-workplace-and-the-value-of-access-to-training) ### 12 Atlanta Fed executives expect 2pp+ annual labor productivity growth in high-skill services in 2026 0.8pp implied 2025 → 2pp+ expected 2026 in high-skill services & finance Sector-level acceleration is forecast in finance and high-skill services — likely the first sectors where AI moves national-accounts productivity. Source: [Atlanta Fed Policy Hub (March 2026)](https://www.atlantafed.org/research/publications/policy-hub/2026/03/ai-productivity-workforce-corporate-executives) ### Need Help Implementing These Findings? Alice Labs helps enterprises turn AI research into measurable business outcomes — from strategy to full-scale implementation. [Explore AI Consulting](/en/ai-consulting)[See AI Strategy Services](/en/ai-strategy) ## Definitions & Measurement Framework **AI productivity** is the measurable change in output per unit of input — labor, capital, or both — attributable to the deployment of AI systems. It must be evaluated separately at the **worker-task level** (throughput, quality, completion time), the **firm level** (labor productivity, revenue per worker, total factor productivity), and the **economy-wide level** (national-accounts labor productivity and TFP growth) — these are different statistical objects with different measurement regimes. ### Core Entities Term Definition Reference Labor productivity Output per hour worked BLS / OECD Total factor productivity (TFP) Output growth not explained by labor or capital inputs BLS / OECD Task augmentation AI assists a worker performing a task NBER / HBS Task automation AI performs a task previously done by a worker Acemoglu / OECD AI adoption (firm) Firm uses one or more AI technologies in production Eurostat / Fed / ONS AI use (worker) Worker uses generative AI for at least one work task NY Fed / St. Louis Fed Complementary investments Training, data, software, workflow redesign that enable AI gains BIS / Atlanta Fed Jagged frontier Tasks where AI excels vs tasks where AI degrades output HBS / BCG AI productivity J-curve Period of measured productivity slowdown before realized gains Brynjolfsson / FRBSF Why measurement matters A 14% gain in customer support throughput, a 4% gain in EU firm labor productivity, and a 0.07pp annual TFP gain are **not contradictory** — they describe different statistical objects measured at different scales. Conflating them is the single most common error in AI-productivity reporting. ## AI Productivity Scoreboard The scoreboard compiles **26 indicators** across worker-task, firm-level, sectoral, and macro evidence. Confidence: High for official statistics and peer-reviewed RCTs, Medium for working papers, surveys, and modeled scenarios. [Download CSV](/data/productivity-scoreboard.csv)[Download JSON](/data/productivity-scoreboard.json) Developer Tasks Completed +26.08% Cui et al., NBER 2025 (4,867 devs) Customer Support (avg / novices) +14% / +34% Brynjolfsson et al., NBER/QJE EU Firm Labor Productivity +4% BIS / EIB (Jan 2026) Macro TFP Gain (range) +0.07 to +1.3 pp/yr Acemoglu vs Aghion-Bunel / OECD Large vs All EU Enterprises 55% vs 20% Eurostat (Dec 2025) Use AI vs Have Training (US) 39% vs 15.9% NY Fed (April 2026) +26% Developer Tasks Completed +14% Customer Support Throughput +4% EU Firm Productivity 26 Public Indicators Indicator Value Year Geography Confidence Customer support throughput gain +14% 2023 US High Customer support gain (novices) +34% 2023 US High Developer tasks completed (4,867 devs) +26.08% 2025 Multi-firm High Writing task time change \-0.8 SD 2023 US Medium Writing task quality change +0.4 SD 2023 US Medium Consultant speed (in-frontier) +25%+ 2023 Global Medium Consultant quality (in-frontier) +40%+ 2023 Global Medium EU firm short-run productivity +4% 2019-2024 EU High Atlanta Fed implied gain (high-skill svcs) +0.8pp 2025 US Medium Atlanta Fed expected (high-skill svcs) +2pp+ 2026 US Medium OECD macro gain (high-exposure G7) +1.3pp/yr 2025 G7 Medium IMF Europe TFP gain (5yr) +1.0% 2025 EU Medium Acemoglu annual TFP gain +0.07pp/yr 2024 US Medium Aghion-Bunel growth range max +1.3pp/yr 2024 Aggregate Medium St. Louis Fed implied aggregate gain +1.1% 2024 US Medium AI time savings (users) 5.4% 2024 US Medium US firm AI adoption 18% 2025 US High US worker AI use rate 39% 2025 US Medium EU enterprise AI use 20.0% 2025 EU High EU large enterprise AI use 55.03% 2025 EU High UK firm AI adoption 9% 2023 UK High Worker training offered (US) 15.9% 2025 US Medium EU adoption barrier — expertise 70.89% 2025 EU High EU adoption barrier — legal 52.52% 2025 EU High Startup high-performer gain (Kenya RCT) +20%+ 2023 Kenya Medium Startup low-performer change (Kenya RCT) \-10% 2023 Kenya Medium Interpretation The scoreboard is **measurement-first**: worker-task gains, firm-level productivity, and macro TFP estimates describe different statistical objects. Conflating them is the most common error in AI productivity reporting. ## Q2 2026 Update — Latest Insights (June 2026) Q2 2026 UPDATE Last reviewed: 26 June 2026 This Q2 2026 refresh consolidates the **real-world AI productivity impact evidence** released between mid-April and late June 2026, addressing the most-asked questions about _latest AI productivity report 2026_, _AI productivity gains by industry_, and whether the gains are _uneven across firms and workers_. The 26-indicator scoreboard is unchanged; this section adds **commentary and externally published context only**. ### What's new between April and June 2026 - **Stanford HAI — AI Index Report 2025** reaffirms that 78% of organizations reported using AI in 2024 (up from 55% in 2023), but with adoption still concentrated in IT, marketing, and product functions. [stanford.edu](https://hai.stanford.edu/ai-index/2025-ai-index-report) - **OECD AI Outlook 2025** (June 2025 release, refreshed Q2 2026 working notes) maintains the +0.4 to +1.3pp/yr labor-productivity gain range for G7 high-exposure occupations and adds a sector breakdown showing finance, ICT, and professional services as the only G7 sectors with statistically detectable labor-productivity inflections. [oecd.org](https://www.oecd.org/en/publications/2025/06/the-oecd-ai-outlook_3b80de5d.html) - **BLS Q1 2026 productivity release** (June 5, 2026) reports US nonfarm business labor productivity at +1.3% (annualized) for Q1 2026. The release language notes ongoing uncertainty about how much of the post-2023 productivity acceleration is AI-attributable versus cyclical labor-market tightening. [bls.gov](https://www.bls.gov/news.release/prod2.nr0.htm) - **Eurostat ICT-enterprise 2026 wave** (preliminary release April–May 2026): EU enterprises using AI rose from 13.5% (2024 reference) to **20.0%** for the 2025 reference period — a 6.5-percentage-point year-over-year jump that is the fastest single-year adoption move recorded in the series. Large enterprises (250+ employees) are now at 55.03%. [eurostat.ec](https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251211-1) - **McKinsey State of AI 2026** (March 2026) reports that the share of organizations with material gen-AI EBIT impact (>5%) reached 21% globally, up from 11% in the prior wave — concentrated in software, marketing, and risk-and-compliance use cases. [mckinsey.com](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) Q2 2026 callout — adoption is accelerating faster than productivity statistics The 2026 evidence base shows **adoption moving faster than measured productivity**: a 6.5pp year-over-year jump in EU enterprise AI use (Eurostat 2026 wave) and a near-doubling of material EBIT-impact rates (McKinsey State of AI 2026, 21% vs 11%). Yet US BLS Q1 2026 nonfarm labor productivity remains at +1.3% — within the post-pandemic band. This widening gap between firm-reported gains and national-accounts productivity is the **core 2026 AI productivity paradox**: gains are realized inside specific use cases but not yet visible in macro aggregates. ### AI productivity gains by industry — Q2 2026 read Across the Stanford HAI 2025, OECD 2025, McKinsey State of AI 2026, and Atlanta Fed 2026 readings, the converging Q2 2026 picture is that **five sectors** show externally corroborated, multi-source productivity inflections, while the remainder show only single-source or projected gains: Sector Q2 2026 corroboration Primary external source Software & ICT Strong (multi-source) Stanford HAI 2025; McKinsey 2026; Cui et al. NBER w33777 Customer support / BPO Strong (multi-source) Brynjolfsson, Li & Raymond QJE; McKinsey 2026 Financial services Strong (multi-source) Atlanta Fed 2026; OECD AI Outlook 2025; McKinsey 2026 Marketing & content Moderate (firm self-report) McKinsey State of AI 2026; Stanford HAI 2025 Risk & compliance Moderate (firm self-report) McKinsey State of AI 2026 Healthcare Weak (single-source) OECD AI Outlook 2025 (projected only) Manufacturing Weak (single-source) OECD AI Outlook 2025 (projected only) ### Why the gains remain uneven in 2026 The Q2 2026 read confirms the original report's measurement-first framing: **AI productivity gains in 2026 are uneven** because three dispersion mechanisms compound. First, firm-size dispersion: EU large enterprises remain at 55.03% AI adoption versus 20.0% for the all-enterprise average (Eurostat). Second, task dispersion: gains are concentrated in the jagged frontier (HBS/BCG) — bounded, digitally tractable knowledge tasks. Third, complementarity dispersion: only 15.9% of US workers receive employer-provided AI training (NY Fed, April 2026) despite 39% using AI at work — the 23-percentage-point training gap is the binding constraint on translating worker-level gains into firm-level and macro gains. Q2 2026 outlook Expect **2026 H2 to widen — not narrow — the gap** between worker-level and macro-level gains. Adoption is still accelerating (Eurostat 2026 wave), but complementary investments in training, workflow redesign, and data infrastructure lag. The H2 2026 indicator to watch is whether US nonfarm labor productivity breaks above +2.0% in the BLS Q3 2026 release — the level Atlanta Fed executives expect AI-intensive sectors to deliver. ## Deep Update — Expanded Analysis (June 2026) DEEP UPDATE — JUNE 2026 v1.3 — Last reviewed: 26 June 2026 This expanded June 2026 analysis addresses the long tail of _AI productivity impact 2026_, _AI productivity gains statistics 2026_, _AI productivity gains by industry 2026_, _AI productivity impact studies 2025 2026_, _US labor productivity growth 2025 2026 AI impact_, and the NBER-specific working-paper queries (customer support +14%, Cui et al. +26%) that account for the largest share of inbound search and LLM-citation demand. It also incorporates the _international roadmap on AI in work, innovation, productivity and skills_ framing used by OECD and the Atlanta Fed. All additions are externally sourced; the 26-indicator scoreboard remains unchanged. ### AI productivity gains statistics 2026 — externally corroborated Across the seven most-cited 2025–2026 enterprise AI studies, productivity, ROI, and adoption gains corroborate the worker- and firm-level findings in this report. The table below lists each externally published statistic and the original source URL — these are commentary anchors, not additions to the scoreboard. Statistic (2025–2026) Value Source Organizations regularly using AI in at least one business function 78% McKinsey State of AI 2025 Organizations reporting material gen-AI EBIT impact (>5%) 21% (up from 11%) McKinsey State of AI 2026 Executives reporting positive ROI from AI agents within 12 months 74% Google Cloud ROI of AI Agents 2025 Enterprise gen-AI pilots that fail to reach production ≈95% MIT NANDA, State of AI in Business 2025 Gen-AI projects abandoned at PoC stage by end of 2025 ≥30% Gartner, Predicts 2026 (Aug 2025) Q1 2026 average enterprise AI spend $207M KPMG AI Quarterly Pulse Survey Q1 2026 Enterprises that increased their AI budget year over year ≈78% PwC AI Business Predictions 2026 Microsoft 365 Copilot list price per user/month $30 Microsoft, official pricing (2026) EU enterprises using AI (2025 reference) 20.0% Eurostat ICT-enterprise 2026 wave Swedish enterprises using AI (10+ employees) ≈35% SCB, AI use in enterprises 2025 US businesses using AI to produce goods or services ≈9% US Census Business Trends and Outlook Survey, May 2026 ### Quotable stat callouts — 2025–2026 evidence These are the single-sentence, source-linked stats most often cited by AI assistants when answering questions about real-world AI productivity impact in 2026. **21% of organizations reported material gen-AI EBIT impact (>5% of earnings) in 2026**, roughly double the 11% reported in the prior wave — concentrated in software engineering, marketing, and risk-and-compliance use cases. [Source: McKinsey, State of AI 2026.](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) **74% of executives report positive ROI from their AI agent investments within 12 months**, with the largest reported gains in revenue-driving functions. [Source: Google Cloud, ROI of AI 2025.](https://cloud.google.com/transform/roi-of-ai-2025-everything-you-need-to-know-about-driving-roi-and-real-business-value) **Approximately 95% of enterprise generative AI pilots fail to deliver measurable P&L impact**, with implementation, data, and workflow integration cited as the dominant blockers. [Source: MIT NANDA, State of AI in Business 2025.](https://nanda.media.mit.edu/) **By end-2025, at least 30% of generative AI projects had been abandoned at the proof-of-concept stage** due to poor data quality, escalating costs, inadequate risk controls, and unclear business value. [Source: Gartner Predicts (Jul 2024 forecast).](https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025) **Average enterprise AI spending in Q1 2026 reached $207 million per organization** among large companies surveyed, a sharp acceleration from the 2024 baseline. [Source: KPMG AI Quarterly Pulse Survey, Q1 2026.](https://kpmg.com/us/en/articles/2026/ai-quarterly-pulse-survey-q1-2026.html) **EU enterprises using AI rose from 13.5% to 20.0% between the 2024 and 2025 reference periods** — the fastest single-year jump in the Eurostat ICT-enterprise series. [Source: Eurostat, Use of AI in enterprises (Dec 2025).](https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251211-1) **Approximately 35% of Swedish enterprises (10+ employees) reported using AI in 2025**, well above the EU average and one of the highest national rates in Europe. [Source: SCB, AI use in enterprises 2025.](https://www.scb.se/) **Approximately 9% of US businesses report using AI to produce goods or services in May 2026**, more than doubling the share from late 2023 (~3.7%). [Source: US Census, Business Trends and Outlook Survey (BTOS), May 2026.](https://www.census.gov/hfp/btos/) **US nonfarm business labor productivity rose +1.3% (annualized) in Q1 2026**, with the BLS noting that the AI-attributable share of the post-2023 acceleration remains contested among macroeconomists. [Source: US BLS Productivity and Costs Q1 2026.](https://www.bls.gov/news.release/prod2.nr0.htm) **Only 15.9% of US workers report employer-provided AI training in 2026, against 39% already using AI at work** — a 23-percentage-point training gap that constrains realized productivity gains. [Source: NY Fed (April 2026).](https://www.newyorkfed.org/research/blog/2026/04/use-of-gen-ai-in-the-workplace-and-the-value-of-access-to-training) ### NBER working papers on AI, productivity, and labor (2023–2026) Search queries such as _nber working paper ai productivity labor 2026_, _nber generative ai customer support productivity study_, _nber generative ai customer support productivity 14 percent_, and _nber generative ai at work brynjolfsson li raymond 2023_ all map to the two foundational worker-level NBER papers. They remain the most-cited primary evidence in 2026. NBER paper Headline result Working paper Brynjolfsson, Li & Raymond — Generative AI at Work (2023; QJE 2025) +14% customer support throughput; +34% for novices NBER w31161 Cui, Demirer, Jaffe, Musolff, Peng & Salz — Effects of Generative AI on Software Developer Productivity (2025) +26.08% completed tasks across 4,867 developers in three firms NBER w33777 Acemoglu — The Simple Macroeconomics of AI (2024) +0.07pp/yr TFP gain over the next decade (lower bound) NBER w32487 Aghion & Bunel — AI and Growth: Where Do We Stand? (FRBSF, 2024) Up to +1.3pp/yr labor productivity growth (historical analogy) FRBSF Economic Letter 2024-16 Babina, Fedyk, He & Hodson — AI Investments and Firm Growth (NBER w28579) AI-investing firms show higher employment and sales growth NBER w28579 ### AI productivity gains by sector — extended 2026 view Queries such as _ai productivity gains by industry 2026_, _ai productivity gains sectors 2026_, _ai productivity impact by sector statistics reports_, and _ai productivity gains by sector 2026_ map to the table below, which extends the original sector-readiness ranking with externally published 2026 corroboration. Sector 2025–2026 productivity signal External corroboration Software & ICT +26% completed tasks; majority of EBIT-impact firms cite software Cui et al. NBER w33777; McKinsey 2026 Customer support / BPO +14% throughput; +34% novice uplift Brynjolfsson, Li & Raymond QJE; McKinsey 2026 Banking & financial services ~0.8pp 2025 implied; +2pp+ expected 2026 in high-skill financial services Atlanta Fed Policy Hub (March 2026); OECD AI Outlook 2025 Insurance Claims-processing and underwriting cited as top gen-AI value pools McKinsey State of AI 2026; Deloitte State of GenAI in Enterprise 2025 Marketing & content Time-to-output and draft-quality gains; second-largest EBIT contribution McKinsey State of AI 2026; Stanford HAI AI Index 2025 Professional services +25% speed and +40% quality on in-frontier consulting tasks Dell'Acqua et al. HBS/BCG 2023 Manufacturing Predictive maintenance, quality assurance, and AI-assisted engineering cited NVIDIA State of AI in Manufacturing 2026; OECD AI Outlook 2025 Telecommunications Network automation reported as top AI use case NVIDIA State of AI in Telecommunications 2026 Retail & CPG Demand forecasting and content generation cited; productivity gains still firm-reported NVIDIA State of AI in Retail and CPG 2026 Healthcare Clinical documentation and triage gains; macro-productivity signal still weak OECD AI Outlook 2025 (projected) Public administration Process streamlining cited; large variance across countries OECD AI Outlook 2025; DIGG (Sweden) guidance 2025 ### US labor productivity growth 2025–2026 and the AI contribution Queries like _us labor productivity growth 2025 2026 ai impact_, _us labor productivity growth 2025 2026 ai contribution_, _us productivity growth 2026 ai impact_, and _us labor productivity growth 2024 2025 2026 ai impact_ point at one question: **how much of recent US productivity growth is AI-driven?** The answer in 2026 is "measurable but not yet dominant." - **2023 (year-over-year):** US nonfarm business labor productivity grew ≈+2.7% — the first acceleration above the pre-pandemic trend, before mass enterprise AI adoption. - **2024 (annual):** US nonfarm business labor productivity grew ≈+2.0–2.7% across revisions — broadly consistent with cyclical labor-market tightening, with AI's contribution still under debate at official statistical agencies. - **2025 (annualized):** Federal Reserve and Atlanta Fed analyses suggest AI contributed up to ≈0.8 percentage points to labor productivity growth in high-skill services in 2025 — but headline national-accounts growth fell back toward trend. - **Q1 2026 (annualized):** US nonfarm business labor productivity +1.3% (BLS Productivity and Costs, June 2026 release). The BLS does not attribute this to AI; macroeconomists are split on the AI share. - **Atlanta Fed executive expectation for 2026:** +2pp+ annual labor productivity growth in high-skill services and finance — but expected, not realized. The picture is: **AI's contribution to measured US labor productivity is real in specific sectors (software, finance, high-skill services) but small relative to cyclical and compositional drivers in 2025–2026**. Reaching the +2pp+ "AI productivity inflection" expected by Atlanta Fed executives would require both higher adoption and faster complementary investment than 2026 mid-year data show. ### Why AI productivity gains remain uneven in 2026 Queries such as _ai productivity gains uneven 2026_, _ai productivity gains real impact 2026_, _ai productivity gains measurement 2026_, _ai productivity gains real world evidence 2026_, and _ai productivity gains economic impact 2026_ all return to the same finding: gains are real but dispersed. Five dispersion mechanisms compound: 1. **Firm-size dispersion** — EU large enterprises (55.03%) are 2.75× more likely to use AI than the EU average (20.0%); the gap is wider in the US and UK (Eurostat 2025). 2. **Task dispersion** — gains concentrate inside the AI "jagged frontier" (HBS/BCG 2023); naïve cross-task deployment yields zero or negative average effects. 3. **Skill dispersion** — within-occupation, novices gain the most (+34% in customer support); across-occupation, high-performers leverage AI better (Kenya entrepreneur RCT +20% vs −10%). 4. **Complementarity dispersion** — only 15.9% of US workers receive employer-provided AI training; the 23-percentage-point gap to AI use (39%) is the binding constraint on realized firm-level gains (NY Fed 2026). 5. **Measurement dispersion** — worker throughput, firm labor productivity, and macro TFP are different statistical objects; the 14% / 4% / 0.07pp numbers describe different scales and cannot be compared directly. ### International roadmap on AI in work, innovation, productivity and skills The OECD's international AI work-and-productivity agenda (_AI Outlook 2025_, _Employment Outlook 2025_, and the OECD AI policy observatory at [oecd.ai](https://oecd.ai/)) frames the 2026 evidence base around three coordinated workstreams: (1) AI exposure measurement at the task level, (2) productivity-gain projection at the sector and macro level, and (3) skills and reskilling indicators tracked across 14+ countries. This report's findings — robust task-level gains, partial firm-level evidence, dispersed macro estimates, large training gap — are consistent with the OECD framing and corroborated by the Atlanta Fed, BLS, ONS, Eurostat, and central-bank publications cited throughout. ### Governance and risk frameworks affecting AI productivity translation in 2026 Productivity gains in 2026 are increasingly mediated by AI governance regimes. Boards and CIOs implementing AI at scale should track: - **EU AI Act** — general application date 2026-08-02; high-risk AI obligations and GPAI rules phase in through 2026–2027. IMF estimates a ~30% drag on EU AI productivity gains if regulation is binding. [EU AI Act, Regulation (EU) 2024/1689.](https://eur-lex.europa.eu/eli/reg/2024/1689/oj) - **Colorado AI Act** — effective February 2026; first US state law imposing risk-management duties on developers and deployers of "high-risk" AI systems. [Colorado SB24-205.](https://leg.colorado.gov/bills/sb24-205) - **NIST AI Risk Management Framework (AI RMF 1.0; Generative AI Profile NIST AI 600-1, July 2024)** — Govern / Map / Measure / Manage functions for trustworthy AI. [NIST AI RMF.](https://www.nist.gov/itl/ai-risk-management-framework) - **ISO/IEC 42001:2023** — first international standard for AI management systems; increasingly cited in EU procurement and US federal RFPs. [ISO/IEC 42001:2023.](https://www.iso.org/standard/81230.html) - **OWASP Top 10 for LLM Applications 2025** — prompt injection, sensitive information disclosure, and supply-chain vulnerabilities lead the list; named in many enterprise gen-AI security baselines. [OWASP LLM Top 10 (2025).](https://owasp.org/www-project-top-10-for-large-language-model-applications/) ### AI productivity forecast 2026 — scenarios, not point estimates For queries such as _ai productivity growth forecast 2026_, _ai productivity growth estimates 2026_, _ai productivity growth impact 2026_, and _ai economic impact productivity growth forecasts 2026_, the most defensible 2026 answer is a scenario range, not a single number: Scenario Implied annual TFP / labor-productivity gain Underlying source Conservative (limited task coverage; weak complementarities) +0.07pp/yr Acemoglu (NBER w32487, 2024) Central (broader task coverage; partial complementarities) +0.3 to +0.7pp/yr OECD AI Outlook 2025 (G7 central) High-exposure (broad task coverage; strong complementarities) +1.0 to +1.3pp/yr Aghion & Bunel (FRBSF, 2024); OECD high-exposure EU 5-year cumulative TFP gain (modeled) +1.0% cumulative; ~30% drag if regulation binds IMF (2025) ### Enterprise AI adoption indicators referenced alongside this report Researchers and journalists triangulating AI productivity claims in 2026 should cross-check three external adoption indicators against this report's scoreboard: - **IBM Global AI Adoption Index 2025** — share of organizations with AI deployed in production. [IBM newsroom.](https://newsroom.ibm.com/) - **Stanford HAI AI Index 2025** — 78% organizational AI use (2024 reference). [stanford.edu](https://hai.stanford.edu/ai-index/2025-ai-index-report). - **Writer Enterprise AI Adoption Report 2026** — 79% of executives report adoption challenges; only a minority report scaled value. [writer.com](https://writer.com/). These secondary indicators are not added to the scoreboard because they use different definitions and self-reported designs; they are referenced here for triangulation only. Bottom line for the deep update The mid-2026 evidence base — McKinsey, Google Cloud, Gartner, MIT NANDA, KPMG, Eurostat, Stanford HAI, NVIDIA, Atlanta Fed — does not overturn this report's measurement-first framing. It strengthens it. Adoption is accelerating and ROI is reported in specific use cases, but the macro productivity translation still hinges on closing the training gap (15.9% trained vs 39% using), redesigning workflows, and getting AI past the 95% pilot-failure / 30% PoC-abandonment threshold flagged by MIT NANDA and Gartner. ## Worker-Level Evidence: The Strongest Causal Layer CHART N = 11,888 workers across 5 studies ### Worker-Level AI Productivity Gains by Study Headline % gain (blue) vs novice / low-performer effect (purple). Sample sizes shown in tooltip. The pattern: **AI compresses skill premiums in support, accelerates code, helps frontier writing — and hurts low-performers in ill-defined business tasks.** **Pattern:** The strongest, most-validated case is software development at multi-firm scale (Cui et al., NBER 2025, N=4,867). The most consequential equity story is customer support — novices gain 2.4× the average effect. Worker-level evidence is the strongest causal layer in the 2026 AI productivity literature. Field experiments and quasi-experiments isolate AI's effect on specific tasks under controlled conditions. ### Customer support: the strongest case **Brynjolfsson, Li & Raymond (NBER w31161; QJE 2023)** studied 5,179 customer support agents at a Fortune 500 software firm. AI assistant rollout produced a **14% average increase** in issues resolved per hour. Crucially, the gain was **34% for novice and low-skilled workers** and statistically zero for the most experienced top performers — AI compressed within-occupation skill premiums. ### Software development: validated at scale **Cui, Demirer, Jaffe, Musolff, Peng & Salz (NBER w33777, 2025)** pooled randomized trials at three companies (Microsoft, Accenture, an anonymized Fortune 100) with **4,867 developers**. AI coding assistant access raised completed tasks by **26.08%**. The cross-firm scale makes this the most-validated firm-level AI productivity case to date. ### Knowledge work: bounded but large **Noy & Zhang (MIT, 2023)** ran a controlled experiment on 444 college-educated professionals doing realistic writing tasks. ChatGPT access reduced time by **0.8 standard deviations** and raised quality by **0.4 standard deviations** (working paper, not peer-reviewed). **Dell'Acqua et al. (HBS / BCG, 2023)** studied 758 BCG consultants on 18 realistic consulting tasks: within the AI frontier, gains reached **+25% speed** and **+40% quality**. Outside the frontier, AI use degraded output. ### When AI doesn't work: the entrepreneur RCT **HBS / Berkeley (2023, Kenya field RCT)** randomly assigned a GPT-4 business mentor to 640 small entrepreneurs. The **average treatment effect was zero** — but heterogeneity was extreme: higher-performing entrepreneurs gained **20%+**, while lower-performing entrepreneurs **lost 10%**. AI is a complement to existing capability, not a substitute for it. Pattern across worker studies AI gains are **largest where the task is bounded, the AI is well-matched to it, and the worker has the judgment to integrate AI output**. Naïve "AI for everyone" rollouts produce average effects of zero — or worse — because they ignore the jagged frontier. ## Firm-Level & Adoption Evidence CHART Eurostat • Fed • ONS • NY Fed ### AI Adoption Concentration (2025–2026) The headline gap: **EU large enterprises adopt AI at 2.75× the EU average** (55% vs 20%). Worker AI _use_ outpaces firm AI _adoption_ in the US — shadow AI is producing measured productivity that firm metrics may not yet capture. **Implication:** SME diffusion is the binding constraint on aggregate productivity gains. Closing the size-of-firm adoption gap is the highest-leverage policy intervention in 2026. Firm-level and sectoral evidence translates worker-task gains into business outcomes. Three pillars of evidence dominate: European firm panel data, US executive surveys, and central-bank monitoring. ### European firm panel: BIS/EIB The **BIS / EIB working paper (Jan 2026)** analyzes 8,800+ European firms across 25 countries using EIB Investment Survey data. Key result: AI-adopting firms show a **+4% short-run labor productivity gain** with **no adverse short-run employment effect**. The effect is robust to firm size, sector, and adoption depth controls. ### US executive survey: Atlanta Fed **Atlanta Fed Policy Hub (March 2026)** surveyed C-level executives. Implied annual labor productivity contributions from AI: **~0.8pp in 2025**, with executives expecting **2pp+ in 2026**. The strongest expected gains are concentrated in **high-skill services and finance**. ### Adoption is concentrated in large firms Geography Adoption Source EU all enterprises (10+ employees) 20.0% Eurostat (Dec 2025) EU large enterprises (250+ employees) 55.03% Eurostat Statistics Explained US firms (year-end 2025) 18% Federal Reserve Board US workers using AI at work 39% NY Fed (April 2026) UK firms 9% ONS (March 2025, 2023 data) The **2.75× gap between EU large enterprises (55%) and the EU average (20%)** means SME diffusion is the binding constraint on aggregate productivity gains over the next 3–5 years. ## Macroeconomic Translation: Why Estimates Diverge CHART Updated 2026-04-20 ### Macro AI-Productivity Estimate Dispersion (2026) Annual TFP / labor-productivity gain from AI — range across 6 credible institutional estimates. Dispersion is the story: a **~19× spread** between the lowest (Acemoglu) and highest (Aghion-Bunel / OECD) point estimates. Lower bound Upper bound **Interpretation:** The 19× spread reflects genuine methodological disagreement — task coverage, adoption pace, and complementary investments. **Boards should plan against scenarios, not point estimates.** Macro estimates of AI productivity gains differ by an **order of magnitude**. The dispersion is not noise — it reflects genuine disagreement about task coverage, adoption pace, complementary investments, and whether AI behaves like a general-purpose technology. ### Macro estimate landscape Source Estimate Method Acemoglu (NBER 2024) +0.07pp/yr TFP Conceptual task-share model Aghion-Bunel (FRBSF 2024) Up to +1.3pp/yr Historical-analogy growth model OECD (2025, G7 high-exposure) +0.4 to +1.3pp/yr labor productivity Scenario-based macro model IMF (Europe, 2025) +1.0% cumulative TFP over 5yr Production-function model St. Louis Fed (2025) +1.1% implied aggregate (2024) Bottom-up time-savings **Why the dispersion?** Acemoglu's lower bound assumes only ~5% of tasks are AI-affectable in the next decade with cost savings of ~30%. Aghion-Bunel and OECD assume far broader task coverage and stronger spillovers. Boards and policymakers should plan against **scenarios**, not point estimates. ### The regulation drag IMF analysis suggests binding regulatory constraints could reduce projected EU AI productivity gains by **~30%**. The EU AI Act's general application date of 2026-08-02 will be the first real test of this estimate. ### National accounts have not yet shown it Despite worker-level and firm-level gains, official productivity statistics in the US, UK, and EU show **no clear AI-attributable acceleration** as of early 2026. The most defensible interpretation: realized aggregate productivity gains are still **incomplete**, with the J-curve hypothesis (measured productivity dips before AI gains materialize in national accounts) still consistent with the data. ## Sectoral AI Productivity Readiness CHART Alice Labs synthesis ### Sectoral AI Productivity Readiness (2026) Composite radar of three drivers per sector: **evidence strength** (peer-reviewed studies available), **adoption** (firm-level use), and **jagged-frontier fit** (share of tasks AI handles well today). Software, customer support, and finance lead. - Evidence strength - Adoption - Frontier fit **Read the chart:** A sector is "AI-productive" only when all three rings extend together. Healthcare and manufacturing have high theoretical promise but lag on evidence and adoption — the gap is operational, not technological. Sector-level AI productivity gains are concentrated where four conditions converge: (a) high share of digitally tractable knowledge tasks, (b) bounded task definitions, (c) measurable output, and (d) existing data infrastructure. ### Sector readiness ranking (2026) Sector Evidence strength Where gains land Software development High Throughput, completed tasks Customer support High Issues per hour, novice uplift High-skill services & finance Medium-High Revenue productivity (Atlanta Fed) Professional services (consulting, law) Medium In-frontier task speed/quality Marketing & content Medium Time-to-output, draft quality Public administration Medium Process streamlining (OECD) Manufacturing Low-Medium Predictive maintenance, QA Healthcare Low-Medium Clinical documentation, triage Construction Low Limited digital task base Retail (in-store) Low Bounded by physical tasks ## Adoption Barriers & The Training Gap CHART NY Fed (April 2026) ### The 23-Percentage-Point AI Training Gap (US, 2026) **39% of US workers use AI at work; only 15.9% have employer-provided training.** This 23-pp gap is the single largest drag on realized firm productivity — and the cheapest to close. **For boards:** The highest-leverage AI productivity intervention in 2026 is **not buying more AI**. It is closing the training and governance gap so the AI you already have can produce measurable gains. The 2026 evidence is unusually clear: **technology cost is not the binding constraint**. Skills, organizational complementarity, and legal certainty are. ### Top adoption barriers (Eurostat, 2025) - **70.89%** — lack of relevant expertise - **52.52%** — lack of clarity about legal consequences - **~40%** — cost of AI technologies (varies by survey) - **~35%** — concerns about data privacy and protection ### UK barriers (ONS, 2023 data) - **39%** — difficulty identifying business use cases - Skills shortages and data quality cited as next-largest blockers ### The training gap **NY Fed (April 2026)**: only **15.9%** of US workers have employer-provided AI training, while **39%** already use AI at work. **38%** say AI training is important. The 23-percentage-point gap between AI use and AI training is the largest single drag on realized productivity — and the cheapest to close. Implication for boards The highest-leverage AI productivity intervention in 2026 is **not buying more AI**. It is closing the training and governance gap so the AI you have already deployed can produce measurable gains. ## Labor Market Effects & Reallocation Despite worker-level productivity gains, **aggregate hiring data show no broad employment collapse** as of early 2026. The pattern is more consistent with task reallocation and changing occupational composition. ### Federal Reserve Board labor monitoring Fed Board's **"AI Adoption and Firms' Job-Posting Behavior"** finds that AI-adopting firms do not show systematic reductions in posting volumes — but they do shift the **skill mix** of postings toward roles that complement AI rather than substitute for it. Junior knowledge-work roles show the largest near-term exposure. ### BIS/EIB European evidence The +4% short-run productivity gain in European AI-adopting firms came **without short-run employment loss**. This is the strongest contemporary evidence against the "AI eats jobs" framing for the immediate horizon. ### Where reallocation is happening - Junior knowledge-work roles: hiring slowdown observable in BLS and posting data - AI-adjacent roles (ML engineering, AI governance, data engineering): rapid demand growth - Customer support: throughput gains compress headcount needs over the medium term - Software engineering: composition shifts toward judgment-heavy and AI-supervisory work ## Glossary — Defined Terms Defined terms used throughout this report, anchored to the same source hierarchy as the scoreboard. Each entry pairs the term with a single-sentence definition and the primary external source. Term Definition Source Labor productivity Real output per hour worked, calculated at the firm, industry, or national-accounts level. [Source](https://www.bls.gov/productivity/) Total factor productivity (TFP) The portion of output growth not explained by measured growth in labor and capital inputs. [Source](https://www.oecd.org/sti/ind/oecdcompendiumofproductivityindicators.htm) AI productivity J-curve A period of measured productivity slowdown that precedes realized AI gains as firms invest in complementary assets. [Source](https://www.brookings.edu/articles/the-productivity-j-curve-how-intangibles-complement-general-purpose-technologies/) Jagged frontier The uneven boundary between tasks where AI improves output and tasks where AI degrades it. [Source](https://www.hbs.edu/faculty/Pages/item.aspx?num=64700) Task augmentation The use of AI to assist a human performing a task without fully replacing them. [Source](https://www.nber.org/papers/w31161) Task automation The full performance of a task by AI, replacing the human worker for that task. [Source](https://www.nber.org/papers/w32487) Generative AI EBIT impact A McKinsey-defined metric for the share of organizations reporting >5% of EBIT attributable to generative AI use cases. [Source](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) AI adoption (firm-level) A firm using one or more AI technologies as defined by its statistical agency (Eurostat, US Census BTOS, ONS, Fed). [Source](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises) AI use (worker-level) A worker using generative AI for at least one work task in the reference period. [Source](https://www.newyorkfed.org/research/blog/2026/04/use-of-gen-ai-in-the-workplace-and-the-value-of-access-to-training) Complementary investments Investments in training, data, software, and workflow redesign that enable AI to translate worker-task gains into firm productivity. [Source](https://www.bis.org/publ/work1220.htm) High-exposure occupation An occupation whose task profile is rated as highly substitutable or augmentable by current AI capabilities. [Source](https://www.oecd.org/en/publications/2025/06/the-oecd-ai-outlook_3b80de5d.html) Pilot-to-production failure rate The share of enterprise AI pilots that do not reach scaled production or measurable P&L impact. [Source](https://nanda.media.mit.edu/) BCG 10-20-70 rule BCG's allocation heuristic for enterprise AI investment: 10% to algorithms, 20% to technology and data, 70% to people and processes. [Source](https://www.bcg.com/capabilities/artificial-intelligence) EU AI Act Regulation (EU) 2024/1689 establishing harmonised rules on artificial intelligence; general application 2026-08-02. [Source](https://eur-lex.europa.eu/eli/reg/2024/1689/oj) NIST AI RMF The US National Institute of Standards and Technology's AI Risk Management Framework (Govern, Map, Measure, Manage). [Source](https://www.nist.gov/itl/ai-risk-management-framework) ISO/IEC 42001 International standard for AI management systems, published December 2023. [Source](https://www.iso.org/standard/81230.html) OWASP Top 10 for LLM Applications Community-maintained list of the ten most critical security risks for large language model applications, current version 2025. [Source](https://owasp.org/www-project-top-10-for-large-language-model-applications/) ## How to Cite & Version History ### How to cite this report Use any of the formats below when referencing the Global AI Productivity Impact Report 2026 in academic, policy, or trade publications. The canonical landing page is [alicelabs.ai/reports/global-ai-productivity-impact-report-2026](https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026). APA (7th edition) Alice Labs. (2026, June 26). _Global AI Productivity Impact Report 2026: Evidence, Sectors & Macro_ (Version 1.3). https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026 MLA (9th edition) "Global AI Productivity Impact Report 2026: Evidence, Sectors and Macro." _Alice Labs_, v1.3, 26 June 2026, alicelabs.ai/reports/global-ai-productivity-impact-report-2026. Chicago (Author-Date) Alice Labs. 2026. "Global AI Productivity Impact Report 2026: Evidence, Sectors and Macro." Version 1.3. Last modified June 26, 2026. https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026. BibTeX @misc{alicelabs2026globalaiproductivity, author = {{Alice Labs}}, title = {Global AI Productivity Impact Report 2026: Evidence, Sectors and Macro}, year = {2026}, month = {June}, version = {1.3}, howpublished = {\\url{https://alicelabs.ai/reports/global-ai-productivity-impact-report-2026}}, note = {Last updated 2026-06-26} } ### Reuse and licensing The 26-indicator dataset is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Direct quotation of report prose is permitted with attribution. AI assistants and search engines are encouraged to cite the canonical URL when extracting stats from this page. ### Version history (visible timeline) v1.3 — 26 June 2026 Deep update. Added expanded June 2026 analysis: externally corroborated 2025–2026 statistics table, 10 quotable stat callouts, extended sector productivity table, US 2024–Q1 2026 labor-productivity timeline, AI productivity dispersion mechanisms, OECD international roadmap framing, governance frameworks affecting AI productivity (EU AI Act, Colorado AI Act, NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10), scenario-based 2026 forecast table, glossary (17 defined terms), and "how to cite this report" section with APA / MLA / Chicago / BibTeX formats. 9 new FAQ entries added. No changes to the 26-indicator scoreboard or original 12 findings. v1.2 — 26 June 2026 Q2 2026 refresh. Added Q2 2026 update section with Stanford HAI AI Index 2025, OECD AI Outlook 2025, BLS Q1 2026, Eurostat 2026 wave, McKinsey State of AI 2026 commentary. Added Q2-specific FAQ entries. No scoreboard changes. v1.0 — 20 April 2026 Initial publication. 26 indicators across worker, firm, sector, and macro evidence layers. 12 key findings, 7 chapters, FAQ, and machine-readable CSV/JSON dataset under CC BY 4.0. ### Authorship and review **Author:** [Linus Ingemarsson](https://alicelabs.ai/en/linus-ingemarsson), Co-Founder, Alice Labs. AI strategy and engineering background; works directly with boards on enterprise AI productivity measurement. **Technical reviewer:** [Eric Lundberg](https://alicelabs.ai/en/eric-lundberg), Co-Founder, Alice Labs. Reviewed all causal claims, source-confidence ratings, and the macro-estimate dispersion framing. **Conflicts of interest:** Alice Labs sells AI strategy consulting, AI implementation consulting, and enterprise AI training services. The report's source hierarchy and confidence ratings are designed to be auditable and independent of commercial interests; readers can re-derive any scoreboard indicator from the cited public sources. ### Methodology note (deep update — June 2026) The v1.3 deep update is strictly additive. The 26-indicator scoreboard, 12 key findings, and original chapter prose are unchanged. New content is sourced exclusively from public, official, or peer-reviewed releases between October 2024 and June 2026, with each external statistic linked to its primary source URL. Where this report references vendor or consulting publications (McKinsey, BCG, Deloitte, KPMG, Gartner, Google Cloud, NVIDIA), they are clearly labeled as such and not aggregated with official statistics or peer-reviewed evidence. Any reader can independently verify a claim by following its cited URL. ## How to Measure AI Productivity (6 Steps) HOW-TO GUIDE ### How to Measure AI Productivity in Your Organization (6 steps) A reproducible workflow for CFOs, COOs, and AI program leads. Designed for citation by AI assistants and Google AI Overviews. 1. 1 Define the productivity object Pick exactly one of: worker-task throughput, firm labor productivity (output per worker-hour), or revenue/value-added per AI-adopting team. Never conflate the three. 2. 2 Pick a bounded, observable task Customer-support tickets resolved, code commits merged, marketing drafts approved, contracts reviewed. Bounded tasks survive measurement; vague ones do not. 3. 3 Measure baseline before AI access Capture 4–8 weeks of pre-rollout data on the chosen task. Document the unit, the worker population, and any seasonal effects. 4. 4 Run a controlled rollout Randomize at the worker, team, or shift level if possible. Otherwise use a pre/post design with explicit controls. Document training hours given. 5. 5 Track complementary investments Workflow changes, data-quality work, prompt-library curation, governance reviews. Without these, gains stay at the worker level and never reach the firm P&L. 6. 6 Report by skill segment Separate novices from experienced workers (Brynjolfsson et al. pattern) and high-performers from low-performers (HBS/Berkeley pattern). Heterogeneity is often the headline. **Need help operationalizing this?** Alice Labs runs AI productivity baselines and rollout instrumentation for enterprise teams. Read more at [/en/ai-consulting](/en/ai-consulting). METHODOLOGY Confidence taxonomy ### Evidence Confidence Matrix Every indicator in this report is tiered by source type. We surface confidence inline so readers — and AI engines that cite this work — can correctly weight each claim. High Confidence Official statistical agencies, peer-reviewed RCTs, central-bank publications. BLS · Eurostat · ONS · Fed Board · NBER (published) · QJE Medium Confidence Working papers, executive surveys, modeled scenarios. NBER WP · MIT WP · Atlanta Fed · NY Fed · IMF · OECD scenarios Excluded Vendor-published estimates, retracted preprints. Notably the MIT scientific-discovery preprint (excluded after MIT's May 2025 statement). 26 Indicators total 12 Citation-grade findings 14 Tier-1 sources cited 100% Public-source ## Frequently Asked Questions 25 answers · structured for AI Overviews ### Does AI actually increase productivity in 2026? Yes — but only at specific scales. Worker-task evidence is robust: +14% in customer support (+34% for novices), +26% completed tasks for software developers, and large gains in bounded knowledge work. Firm-level evidence is positive in Europe (+4% labor productivity, no employment loss). Macro-level gains remain incomplete and difficult to attribute in national accounts. ### How much does AI improve worker productivity? It depends on the task. The strongest evidence: +14% issues per hour in customer support (Brynjolfsson, Li & Raymond, NBER/QJE), +26.08% completed tasks for developers (Cui et al., NBER 2025), -0.8 SD time and +0.4 SD quality on professional writing (MIT). Within the AI 'jagged frontier' for consultants: +25% speed, +40% quality. Outside the frontier, AI use can degrade output. ### Why isn't AI showing up in macro productivity statistics yet? Three reasons. First, adoption is incomplete: 18% of US firms, 20% of EU enterprises, 9% of UK firms. Second, complementary investments (training, workflow redesign, data) lag behind technology adoption. Third, the J-curve hypothesis predicts measured productivity dips before AI gains materialize in national accounts. Realized aggregate gains remain difficult to attribute specifically to AI in 2026. ### What is the macroeconomic impact of AI on productivity? Estimates diverge sharply. Acemoglu's conceptual model: +0.07 percentage points per year of TFP gain. Aghion-Bunel and OECD high-exposure scenarios: up to +1.3 percentage points per year. IMF projects +1.0% cumulative TFP for Europe over 5 years, with a ~30% drag if regulation is binding. Boards should plan against scenarios, not point estimates. ### Which sectors see the biggest AI productivity gains? Software development (validated at scale across 4,867 devs), customer support (largest novice gains), and high-skill services & finance (Atlanta Fed expects +2pp+ in 2026). Marketing, professional services, and public administration show medium-strength evidence. Manufacturing, healthcare, construction, and physical retail show weaker evidence so far. ### What is the biggest barrier to AI productivity gains? Skills, not technology cost. 70.89% of EU enterprises cite lack of expertise; 52.52% cite legal uncertainty (Eurostat 2025). Only 15.9% of US workers have employer-provided AI training despite 39% using AI at work (NY Fed). The 23-percentage-point training gap is the single largest drag on realized productivity — and the cheapest to close. ### Does AI reduce jobs? Not in aggregate, as of early 2026. BIS/EIB found +4% European firm productivity gains with no short-run employment loss. Fed Board labor monitoring shows AI-adopting firms shift posting skill-mix rather than reduce posting volume. Junior knowledge-work roles show the largest near-term exposure, but the dominant pattern is task reallocation, not aggregate hiring collapse. ### How does AI productivity differ between novice and experienced workers? AI compresses within-occupation skill premiums. In the customer support RCT, novices and low-skilled workers gained +34%, while top performers showed no measurable gain. The HBS/Berkeley Kenya entrepreneur RCT shows the inverse: high-performers gained +20%, low-performers lost -10%. AI complements existing capability — it is not a uniform substitute. ### What is the AI 'jagged frontier'? A concept from Dell'Acqua et al. (HBS / BCG, 2023). Within the frontier — tasks AI is well-suited to — gains reach +25% speed and +40% quality. Outside the frontier, AI use can degrade output. Task-boundary awareness is now a core management skill: naïve 'AI for everyone' rollouts produce average effects of zero or worse. ### How do I measure AI productivity in my organization? Measure at three levels separately: (1) worker-task throughput and quality before/after AI access, (2) firm-level labor productivity (output per worker-hour), and (3) revenue or value-added per AI-adopting team. Never conflate the three. Use bounded, observable tasks for evaluation. Document baseline, post-rollout outcomes, and complementary investments (training hours, workflow changes, data quality). ### What official statistics measure AI adoption? Eurostat (annual ICT enterprise survey), US Census BTOS with AI supplement, Federal Reserve Board's Monitoring AI Adoption series, ONS UK firm-level surveys, and OECD's skills-based detection of AI vacancies across 14 countries. These use different definitions — keyword-based, skills-based, or self-reported — and produce different numbers. None should be treated as a single ground truth. ### How often is this report updated? Quarterly refresh for adoption statistics, new field experiments, and labor-market monitoring. Annual full methodological revision aligned with major statistical release cycles (BLS OEWS, Eurostat ICT survey, OECD Compendium of Productivity Indicators). Each release is versioned with documented changes. ### What is the latest AI productivity report for 2026 (May–June 2026 evidence)? As of late June 2026, the most current public-source evidence comes from five releases: Stanford HAI's AI Index 2025 (78% organizational AI use), OECD AI Outlook 2025 (+0.4 to +1.3pp/yr labor-productivity gain range for G7 high-exposure occupations), BLS Q1 2026 productivity release (US nonfarm labor productivity +1.3% annualized), Eurostat ICT-enterprise 2026 wave (EU AI use 20.0%, up 6.5pp YoY), and McKinsey State of AI 2026 (21% of organizations reporting material gen-AI EBIT impact, up from 11%). Together they confirm adoption is accelerating faster than measured macro productivity — the Q2 2026 AI productivity paradox. ### Are AI productivity gains real and uneven across firms in 2026? Yes to both. The gains are real and well-identified at the worker-task level (+14% customer support, +26% developer tasks, +4% European firm labor productivity) and corroborated across multiple Q2 2026 sources. But they are uneven on three axes: (1) firm-size — EU large enterprises at 55.03% adoption versus the 20.0% all-enterprise average; (2) task — gains concentrated within the AI 'jagged frontier' and absent or negative outside it; (3) complementarity — only 15.9% of US workers receive employer-provided AI training despite 39% using AI at work. Without training and workflow redesign, worker-level gains do not propagate to firm or macro productivity. ### What does the latest NBER working paper say about AI productivity and labor in 2026? The most-cited NBER evidence in 2026 remains Cui, Demirer, Jaffe, Musolff, Peng & Salz (NBER w33777, 2025): a pooled randomized trial across Microsoft, Accenture, and an anonymized Fortune 100, totaling 4,867 developers, finding +26.08% completed tasks with AI coding assistants. On the macro side, Acemoglu (NBER w32487, 2024) sets the conservative bound at +0.07pp/yr TFP gain over the next decade. The two are not contradictory: large gains in bounded tasks need complementary investments to translate into macro TFP, which the 2026 evidence shows are still incomplete. ### Has US labor productivity growth in 2025–2026 been driven by AI? Partly, but not yet dominantly. US nonfarm business labor productivity ran at roughly +2.0% in 2024 and +1.3% (annualized) in Q1 2026 per BLS. The BLS does not directly attribute the post-2023 acceleration to AI, and macroeconomists are split: Atlanta Fed executive survey data point to AI-driven gains of ~0.8pp in 2025 in high-skill services, while Acemoglu's conceptual model implies only +0.07pp/yr economy-wide for the coming decade. The most defensible 2026 conclusion: AI is contributing measurably in specific sectors (software, finance, high-skill services) but is not yet the dominant driver of headline US labor productivity growth. ### What does the NBER study on generative AI in customer support find (the 14% study)? Brynjolfsson, Li & Raymond's NBER w31161 working paper (now published in the Quarterly Journal of Economics, 2025) studied 5,179 customer support agents at a Fortune 500 software company. Access to a generative AI assistant increased issues resolved per hour by 14% on average, with a 34% gain for novice and low-skilled workers and statistically zero gain for top performers. It is the foundational worker-task field study in the AI productivity literature and remains the most-cited 2026 evidence point. ### What are the AI productivity gains by industry in 2026? Software & ICT and customer support / BPO show the strongest multi-source 2026 evidence: +26% completed developer tasks across 4,867 devs (Cui et al. NBER w33777) and +14% customer support throughput. Banking and financial services show ~0.8pp implied 2025 gain and +2pp+ expected 2026 (Atlanta Fed). Insurance, marketing, and risk-and-compliance show firm-reported gains (McKinsey State of AI 2026). Manufacturing, telecommunications, and retail show single-source firm-reported gains (NVIDIA State of AI series 2026). Healthcare and public administration show OECD-projected gains rather than realized productivity inflections. Construction and physical retail remain weak-evidence sectors. ### What is the latest AI productivity report for May 2026? As of late June 2026, the most current public-source releases bearing on AI productivity are: (1) BLS Productivity and Costs Q1 2026 (June 2026 release, US nonfarm labor productivity +1.3% annualized), (2) Eurostat ICT-enterprise 2026 wave preliminary release (EU AI use 20.0%), (3) McKinsey State of AI 2026 (March 2026, 21% material EBIT impact), (4) US Census BTOS May 2026 AI supplement (~9% of US businesses using AI), and (5) KPMG AI Quarterly Pulse Survey Q1 2026 ($207M average enterprise AI spend). Together they confirm the 2026 AI productivity paradox: adoption and ROI are accelerating in specific use cases while macro labor-productivity growth remains within the post-pandemic band. ### How big is the global economic impact of AI in 2026? Macro estimates span an order of magnitude. Acemoglu's conceptual lower bound (NBER w32487): +0.07pp/yr in TFP over the next decade. OECD AI Outlook 2025 central / high-exposure scenarios for G7 economies: +0.4 to +1.3pp/yr in labor productivity. Aghion & Bunel (FRBSF Economic Letter 2024-16): up to +1.3pp/yr by historical analogy. IMF (2025): +1.0% cumulative TFP gain for Europe over five years, with a ~30% drag if regulation is binding. Goldman Sachs Research has previously projected a $7 trillion cumulative global GDP uplift from generative AI over ten years (estimates remain widely dispersed and scenario-driven, not realized). ### Why do 95% of enterprise AI pilots fail? MIT NANDA's State of AI in Business 2025 attributes the ≈95% pilot-to-production failure rate primarily to four mechanisms: (1) data quality and accessibility — pilots depend on data that is not production-grade; (2) workflow integration — most pilots are point tools, not embedded in business processes; (3) change management — only 15.9% of US workers receive employer-provided AI training (NY Fed 2026); and (4) measurement — pilots optimize for technical metrics rather than P&L impact. Gartner separately forecasts that at least 30% of generative AI projects were abandoned at the proof-of-concept stage by end-2025 due to poor data quality, escalating costs, inadequate risk controls, and unclear business value. ### What is BCG's 10-20-70 rule for AI investment? BCG's 10-20-70 heuristic, drawn from its enterprise AI consulting practice, recommends that organizations allocate roughly 10% of AI program investment to algorithms, 20% to technology and data, and 70% to people and process change — including training, workflow redesign, governance, and management practice upgrades. It directly mirrors this report's finding that complementary capabilities, not technology cost, are the binding constraint on realized AI productivity gains. ### How does the EU AI Act affect AI productivity gains in 2026? The EU AI Act (Regulation (EU) 2024/1689) entered into force in August 2024, with most obligations applying from 2 August 2026, including general-purpose AI rules and high-risk AI system requirements. IMF analysis suggests binding regulatory constraints could reduce projected EU AI productivity gains by ~30%. The Colorado AI Act, effective February 2026, is the first US state law imposing similar risk-management duties on developers and deployers of high-risk AI systems. Compliance costs and process redesign for high-risk systems are the dominant transmission channels. ### How does AI adoption in Sweden compare to the EU average in 2025? Sweden is one of the highest-adoption AI markets in Europe. According to Statistics Sweden (SCB) data for 2025, approximately 35% of Swedish enterprises with 10+ employees report using AI, well above the EU average of 20.0% (Eurostat 2025). Adoption is concentrated in large enterprises and knowledge-intensive services. Public-sector adoption is guided by the Swedish Agency for Digital Government (DIGG) and the Swedish Authority for Privacy Protection (IMY), which have published AI governance guidance through 2025 and 2026. ### What is the AI productivity paradox in 2026? The AI productivity paradox in 2026 is the widening gap between firm-reported AI gains and macro-level productivity statistics. Firm and worker evidence is strong: +14% in customer support, +26% in software, +4% in EU firm labor productivity, 21% of organizations reporting material EBIT impact (McKinsey 2026). But US nonfarm labor productivity grew only +1.3% (annualized) in Q1 2026 (BLS) — within the post-pandemic band. The most defensible interpretation is that gains are real but compartmentalized, complementary investments lag adoption, and the AI productivity J-curve (measured slowdown before realized aggregate gain) is still consistent with the 2026 data. ## About the Authors & Reviewers Published April 20, 2026 · Updated June 26, 2026 Written by ![Linus Ingemarsson - Co-Founder, Alice Labs at Alice Labs](/images/linus-ingemarsson.png) [Linus Ingemarsson](/en/linus-ingemarsson) Co-Founder, Alice Labs Co-Founder at Alice Labs. Author of 7 research reports on AI adoption, governance and labor markets cited across EU, OECD and US benchmarks. - 8+ years in AI strategy & implementation - Top-5 AI Speaker, Sweden (Mindley 2025) - 100+ enterprise AI engagements [View profile](/en/linus-ingemarsson) [](https://www.linkedin.com/in/linus-ingemarsson/)[](mailto:linus@alicelabs.ai) Reviewed by June 26, 2026 ![Eric Lundberg - Co-Founder, Alice Labs at Alice Labs](/images/eric-lundberg.png) [Eric Lundberg](/en/eric-lundberg) Co-Founder, Alice Labs Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations. - AI automation & agent systems lead - Workflow design across 100+ deployments - Specialist in RAG, integrations & APIs [View profile](/en/eric-lundberg) [](https://www.linkedin.com/in/eric-lundberg-3530451bb/)[](mailto:eric@alicelabs.ai) Published April 20, 2026 · Updated June 26, 2026 Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Methodology ### Research approach **100% public-source desk research** conducted between 2026-04-15 and 2026-04-20. No interviews, no proprietary surveys. Primary sources are official statistical agencies, central banks, peer-reviewed journals, and institutional working papers. Secondary sources (Stanford HAI, McKinsey via AI Index) are used cautiously and explicitly labeled. ### Source hierarchy 1. **Tier 1 (High confidence):** Official statistical agencies (BLS, ONS, Eurostat, OECD), peer-reviewed journals (QJE, NBER published series), central-bank publications. 2. **Tier 2 (Medium confidence):** Working papers (NBER unpublished, MIT preprints), institutional executive surveys (Atlanta Fed, NY Fed), modeled scenarios (IMF, OECD high-exposure). 3. **Tier 3 (Low confidence — used cautiously or excluded):** Vendor-published estimates, retracted preprints (notably the MIT scientific-discovery preprint, excluded after MIT's retraction statement). ### Confidence scoring Each scoreboard indicator is rated **High** (official statistic or peer-reviewed RCT) or **Medium** (working paper, survey-based, or modeled). No Low-confidence indicators are included in the headline scoreboard. ### Reproducibility All 26 indicators are published as machine-readable CSV and JSON under CC BY 4.0. Schema: `metric_name | value | unit | year | geography | definition | source_url | publisher | publish_date | accessed_date | confidence`. AI-assisted research disclosure This report was prepared with AI assistance for source-collection and synthesis, then reviewed by Linus Ingemarsson and technically reviewed by Eric Lundberg. All claims are traceable to the cited public sources. Treat findings as exploratory insights requiring further validation. ## Limitations - **Realized aggregate productivity remains hard to attribute to AI.** Worker-level and firm-level gains are not yet visible in national-accounts TFP for the US, UK, or EU. - **Many high-profile studies are task-specific.** The customer support, coding, and writing studies are robust within their tasks but generalize imperfectly. - **Adoption measures are not harmonized.** Eurostat (firm with 10+ employees, 2025), Fed Board (year-end 2025), and ONS (2023 data published 2025) use different definitions and reference periods. - **Macro estimates are scenario-driven.** Acemoglu, Aghion-Bunel, OECD, and IMF make different assumptions about task coverage, adoption pace, and complementarities — disagreement is methodological, not random noise. - **Self-reported productivity gains may overstate near-term national-accounts effects.** Time-savings (St. Louis Fed) translate to measured TFP only with workflow redesign and capacity reallocation. - **Excluded evidence:** The MIT preprint on AI and scientific discovery (Aidan Toner-Rodgers, 2024) was excluded after MIT publicly stated it had no confidence in the paper's provenance, reliability, or validity (May 2025). - **Geographic coverage skews to G7.** Strong evidence base for US, EU, UK; thinner for emerging markets (notable exception: HBS/Berkeley Kenya RCT). ## Data Sources 18 primary sources Source Description Accessed [Brynjolfsson, Li & Raymond — Generative AI at Work (NBER w31161 / QJE)](https://www.nber.org/papers/w31161) Customer support RCT: +14% throughput, +34% novice gain 2026-04-20 [Cui et al. — Effects of Generative AI on Software Developer Productivity (NBER w33777)](https://www.nber.org/papers/w33777) 4,867 developers across three firms: +26.08% completed tasks 2026-04-20 [Noy & Zhang — Experimental Evidence on the Productivity Effects of Generative AI (MIT)](https://economics.mit.edu/sites/default/files/inline-files/Noy_Zhang_1.pdf) Writing tasks: -0.8 SD time, +0.4 SD quality 2026-04-20 [Dell'Acqua et al. — Navigating the Jagged Technological Frontier (HBS / BCG)](https://www.hbs.edu/faculty/Pages/item.aspx?num=64700) BCG consultants: +25% speed, +40% quality in-frontier 2026-04-20 [BIS / EIB — AI Adoption, Productivity and Employment in European Firms](https://www.bis.org/publ/work1220.htm) +4% short-run firm labor productivity gain, no employment loss 2026-04-20 [Federal Reserve Board — Monitoring AI Adoption in the US Economy](https://www.federalreserve.gov/econres/notes/feds-notes/monitoring-ai-adoption-in-the-us-economy.htm) 18% US firm AI adoption (year-end 2025) 2026-04-20 [Federal Reserve Bank of New York — Use of Gen AI in the Workplace (April 2026)](https://www.newyorkfed.org/research/blog/2026/04/use-of-gen-ai-in-the-workplace-and-the-value-of-access-to-training) 39% workers using AI; 15.9% trained 2026-04-20 [Atlanta Fed Policy Hub — AI, Productivity & the Workforce (March 2026)](https://www.atlantafed.org/research/publications/policy-hub/2026/03/ai-productivity-workforce-corporate-executives) Executive survey: +0.8pp 2025, +2pp+ expected 2026 2026-04-20 [St. Louis Fed — Impact of Generative AI on Work Productivity](https://www.stlouisfed.org/on-the-economy/2025/feb/impact-generative-ai-work-productivity) 5.4% time savings; 1.1% implied aggregate gain 2026-04-20 [Eurostat — Use of Artificial Intelligence in Enterprises (Dec 2025)](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises) 20.0% EU enterprises; 55.03% large; barriers data 2026-04-20 [ONS — Management Practices and Adoption of Technology and AI in UK Firms](https://www.ons.gov.uk/economy/economicoutputandproductivity/productivitymeasures/articles/managementpracticesandtheadoptionoftechnologyandaiinukfirms/2025-03-24) 9% UK firm AI adoption (2023 data) 2026-04-20 [Acemoglu — The Simple Macroeconomics of AI (NBER w32487)](https://www.nber.org/papers/w32487) +0.07pp/yr TFP — conceptual lower bound 2026-04-20 [Aghion & Bunel — AI and Growth: Where Do We Stand? (FRBSF)](https://www.frbsf.org/research-and-insights/publications/economic-letter/2024/06/ai-and-growth-where-do-we-stand) Up to +1.3pp/yr — historical analogy 2026-04-20 [OECD — Macroeconomic Productivity Gains from AI in G7 Economies](https://www.oecd.org/en/publications/macroeconomic-productivity-gains-from-artificial-intelligence-in-g7-economies_a8b3f5cb-en.html) +0.4 to +1.3pp/yr labor productivity (high-exposure) 2026-04-20 [Stanford HAI — AI Index Report 2025](https://hai.stanford.edu/ai-index/2025-ai-index-report) 78% of organizations reporting AI use (2024 reference); adoption concentrated in IT, marketing, and product 2026-06-26 [OECD — AI Outlook 2025](https://www.oecd.org/en/publications/2025/06/the-oecd-ai-outlook_3b80de5d.html) G7 sector breakdown; finance, ICT, professional services show statistically detectable labor-productivity inflections 2026-06-26 [BLS — Productivity and Costs, Q1 2026](https://www.bls.gov/news.release/prod2.nr0.htm) US nonfarm business labor productivity +1.3% annualized in Q1 2026 2026-06-26 [McKinsey — The State of AI 2026](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) 21% of organizations report material gen-AI EBIT impact (>5%), up from 11% in prior wave 2026-06-26 ## Version History 1.0 2026-04-20 Latest Initial publication. 26 indicators across worker, firm, sector, and macro evidence layers. 12 key findings, 7 chapters, FAQ, and machine-readable CSV/JSON dataset under CC BY 4.0. 1.2 2026-06-26 Q2 2026 refresh. Added 'Q2 2026 Update — June 2026' section with Stanford HAI AI Index 2025, OECD AI Outlook 2025, BLS Q1 2026 labor productivity, Eurostat 2026 ICT release, and McKinsey State of AI 2026. Added Q2-specific FAQ entries addressing real-world impact, sectoral patterns, and 2025-2026 study trends. No changes to the 26-indicator scoreboard or original findings. 1.3 2026-06-26 Deep expansion. Added 'Deep Update — Expanded Analysis (June 2026)' chapter with externally corroborated 2025–2026 statistics table, 10 quotable stat callouts, NBER working-papers table, extended sector productivity table, US 2024–Q1 2026 labor productivity timeline, AI productivity dispersion mechanisms, OECD international roadmap framing, governance frameworks (EU AI Act, Colorado AI Act, NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10), scenario-based 2026 forecast table, glossary (17 defined terms), and 'how to cite this report' section with APA, MLA, Chicago, and BibTeX formats. 9 new FAQ entries. No changes to the 26-indicator scoreboard or original 12 findings. ## Related Reports [ global • Apr 2026 ### AI Automation ROI Benchmark Report 2026 Public-source benchmark of AI automation ROI, productivity gains, hours saved, cost avoidance, cycle-time reduction, and enterprise financial impact for CFOs Read report ](/reports/ai-automation-roi-benchmark-2026)[ global • Apr 2026 ### Enterprise AI Operating Model Report 2026 Public-source benchmark of governance bodies, decision rights, lifecycle controls, AI literacy, third-party oversight, and operating-model maturity across large enterprises Read report ](/reports/enterprise-ai-operating-model-2026)[ nordic • Apr 2026 ### Nordic AI Talent Pipeline Report 2026 Public-source benchmark of AI education, workforce sustainability, research capacity, compute infrastructure, and policy coordination across Denmark, Finland, Iceland, Norway, and Sweden Read report ](/reports/nordic-ai-talent-education-pipeline-2026) [View all reports](/reports) ## Get in Touch! 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