Experimental AI Research (Beta): This report was generated with AI assistance as part of our ongoing exploration of AI-powered research and analysis. The content has been reviewed and edited by humans, but may contain errors or inaccuracies.
Please verify critical data points independently. All claims cite public sources for transparency and reproducibility. This is not peer-reviewed academic research – treat findings as exploratory insights requiring further validation.
Cite This Report
Alice Labs Research (2026). Global AI Adoption Index 2026. Alice Labs. Version 1.0. https://alicelabs.ai/reports/global-ai-adoption-index-2026
Executive Summary
The Global AI Adoption Index (GAIAI) 2026 is a reproducible set of official, publicly accessible indicators that benchmark enterprise AI adoption across five major statistical systems. It measures adoption rates, size gaps (SME vs large), use-case purposes, barriers, and governance signals — relying exclusively on official statistics and documented public sources.
AI adoption measurement is maturing, but it remains fragmented. In the most comprehensive official enterprise system, Eurostat reports that 19.95% of EU enterprises used AI technologies in 2025, with steep size differences (55.03% for large enterprises vs 17% for small). At the OECD level, 20.2% of firms used AI in 2025, up from 8.7% in 2023 — a 132% increase in two years. Canada's official business survey reports 12.2% of businesses used AI in the last 12 months (Q2 2025 reference), with 14.5% planning to adopt within the next 12 months (Q3 2025). In the U.S., high-frequency BTOS-based measurement shows 3.7% rising to 5.4% (Sep 2023 → Feb 2024), with expected use rising to 6.6% for early Fall 2024. The UK's ONS reports 9% of firms adopted AI in 2023, with an intention-based projection to 22% in 2024.
The consistent pattern across systems: adoption is not primarily blocked by lack of interest alone. Barriers cluster around skills/expertise (EU: 70.89%), perceived irrelevance (Canada: 78.1% of non-planners), and difficulty identifying use cases (UK: 39%), depending on survey wording and population.
- 19.95% of EU enterprises used AI in 2025 — up 6.47 pp from 2024
- 20.2% of OECD firms reported AI use in 2025 — more than doubled since 2023
- 12.2% of Canadian businesses used AI (Q2 2025) — doubled from 6.1% a year earlier
- 70.89% of EU enterprises that considered AI but didn't adopt cite lack of expertise
- 55% vs 17% — large vs small enterprise adoption gap in the EU (38 pp)
- 57.3% of OECD ICT firms use AI — highest sectoral concentration
GAIAI v1.0 is a benchmark family rather than a single scalar ranking. Measures differ across sources — cross-geo comparisons must be interpreted within each system's definitional context. This report contains no interviews or anecdotes.
Key Findings
15 data-driven insights
01EU enterprise AI adoption reached ~1 in 5 enterprises in 2025
19.95% of EU enterprises (10+ employees) used AI technologies in 2025
The EU is one of the few regions with annual, cross-country official enterprise AI adoption statistics — it anchors global benchmarking.
02The EU shows a large SME–large adoption gap
55.03% (large) vs 17% (small) enterprises used AI in 2025 — gap ≈ 38 pp
Adoption policies and vendor strategies must be segmented by firm size; 'average adoption' hides structural divides.
03Text-mining/NLP is the most common AI technology type among EU enterprises
11.75% of EU enterprises used text mining in 2025
Early adoption is concentrated in language/data processing use cases, shaping ROI expectations and governance needs.
04EU enterprises most often use AI for commercial functions
34.70% of AI-using enterprises used AI for marketing or sales (2025)
The 'first wave' business value is customer-facing and revenue-adjacent rather than deep operational automation.
05Skills/expertise is the #1 EU barrier among enterprises that considered AI
70.89% cite lack of relevant expertise (2025; among 'considered' non-adopters)
Workforce capability and support ecosystems are core adoption constraints — not only capital expenditure.
06OECD firm AI adoption more than doubled in two years (2023→2025)
8.7% (2023) → 14.2% (2024) → 20.2% (2025) firms using AI
Adoption acceleration is measurable in official sources, providing a basis for scenario planning and infrastructure investment.
07OECD adoption remains uneven by firm size
52.0% of large firms vs 17.4% of small firms use AI (2025)
Similar to the EU, scale advantages and capacity constraints shape adoption outcomes across the OECD.
08OECD AI adoption is most concentrated in ICT firms
57.3% of ICT firms used AI (OECD, 2025); professional/scientific services 36.8%
Sectoral concentration implies that diffusion to 'lagging sectors' is the key frontier for productivity impact.
09Canada's official business AI use rate doubled year-over-year
6.1% (Q2 2024) → 12.2% (Q2 2025) businesses using AI in last 12 months
Rapid diffusion in a large, advanced economy using a clear survey instrument.
10Canada shows a large 'non-adoption majority' driven mainly by perceived irrelevance
66.7% report no plans to adopt AI; 78.1% of non-planners cite 'AI not relevant'
Adoption isn't just constrained by cost or regulation; many businesses still see limited applicability.
11U.S. high-frequency BTOS measures show fast growth from a low base
3.7% AI use rate in Sep 2023 → 5.4% in Feb 2024 (previous two weeks)
Provides one of the most time-sensitive official indicators, useful for detecting inflection points.
12UK ONS reports 9% firm AI adoption in 2023, with planned adoption projected to 22% in 2024
9% used AI in 2023; projected 22% in 2024 based on plans
Demonstrates how 'adoption' depends on whether measurement is realized use vs planned use.
13UK firms' top AI barrier is 'identifying activities/use cases'
39% cite difficulty identifying activities/use cases; 21% cite cost; 16% cite AI expertise
Adoption barriers can be 'deployment design' problems, not just technology availability.
14Formal governance frameworks are converging but not yet measurable as adoption statistics
ISO/IEC 42001 exists as AI management system standard (edition 1, 2023-12)
Standards create common control language even before certification metrics mature.
15EU AI Act creates new compliance requirements for adoption planning
Regulation (EU) 2024/1689, published in Official Journal 12 July 2024
Compliance requirements shift adoption toward governance, documentation, and risk controls — especially for 'high-risk' systems.
Definitions and Scope
The Global AI Adoption Index 2026 is a reproducible, desk-research-based, cross-geography benchmark that measures enterprise and SME AI adoption and adoption-adjacent operational readiness using only publicly accessible sources.
Core Entity Definitions
| Term | Definition in This Report |
|---|---|
| Artificial Intelligence (AI) | Refers to the operational definitions used by each official source. Eurostat operationalizes AI as use of listed technologies including text mining, speech recognition, NLG, machine learning, and AI-based automation. |
| Enterprise / firm / business | Treated as defined by each statistical system. Eurostat uses enterprise units with 10+ employees. U.S. BTOS covers nonfarm employer businesses. |
| Adoption / use | Source-specific. U.S. BTOS: use in prior two weeks. Canada: over the last 12 months. Eurostat: used at least one AI technology. |
| SME | Not universal. Eurostat small = 10–49 employees; medium = 50–249; large = ≥250. |
| Implementation vs pilot | ONS defines "adopters" as firms using AI as part of methods/processes, distinguishing from testing. Other systems do not make this distinction explicitly. |
Measurement Time Windows Compared
| System | Time Window | Population Frame | Statistical Maturity |
|---|---|---|---|
| EU (Eurostat) | "Used at least one AI technology" | Enterprises 10+ employees | Official |
| OECD | ICT Access & Usage framework | Firms (country definitions vary) | Official |
| Canada (StatsCan) | "Last 12 months" | Businesses (CSBC) | Official |
| U.S. (Census BTOS) | "Previous two weeks" | Nonfarm employer businesses | Experimental |
| UK (ONS MES) | "Used as part of methods/processes" | MES survey frame | In development |
Important: Different time windows (last 2 weeks vs last 12 months vs "used in year"), different population frames (10+ employees vs all employers), and different statistical maturity levels mean these measures are not directly numerically comparable without adjustment. GAIAI v1.0 presents them as a benchmark family.
Inclusions: Official enterprise adoption statistics; official barrier/use-case distributions; official trend data.
Exclusions: Private vendor adoption claims without transparent methodology; unsourced global "% of companies use AI" statements; non-public datasets.
Verification Principle
If a claim cannot be tied to a publicly accessible source with publisher + publish date (when available) + access date, it is excluded. Where computations are presented (e.g., "gap in percentage points"), they are arithmetic transforms of official published values, not new estimates.
GAIAI Scoreboard (Core Indicators)
The GAIAI Scoreboard compiles 20 core indicators from official statistical systems. Each metric includes confidence levels: High for official statistics, Medium-High for experimental/working paper sources, and Medium for projections based on reported plans.
19.95%
EU Enterprise Adoption
20.2%
OECD Firm Adoption
38 pp
EU Size Gap
70.9%
Skills Barrier (#1)
| Indicator | Value | Year | Confidence |
|---|---|---|---|
| Enterprise AI adoption rate | 19.95% | 2025 | High |
| Large-enterprise AI adoption | 55.03% | 2025 | High |
| Small-enterprise AI adoption | 17.00% | 2025 | High |
| Medium-enterprise AI adoption | 30.36% | 2025 | High |
| Most used AI tech type | 11.75% text mining | 2025 | High |
| Top AI use purpose (adopters) | 34.70% marketing/sales | 2025 | High |
| AI pipeline: 'considered using' | 14.21% | 2025 | High |
| Top barrier (EU considerers) | 70.89% lack expertise | 2025 | High |
| Firm AI adoption rate | 20.2% | 2025 | High |
| Firm AI adoption trend | 8.7% → 14.2% → 20.2% | 2023–25 | High |
| Large vs small firm adoption | 52.0% vs 17.4% | 2025 | Medium-High |
| ICT sector adoption | 57.3% | 2025 | High |
| Prof. services sector adoption | 36.8% | 2025 | High |
| Business AI use (last 12 months) | 12.2% | 2025 | High |
| Canada AI pipeline (plans) | 14.5% | Q3 2025 | High |
| Canada non-adoption majority | 66.7% | Q3 2025 | High |
| U.S. high-frequency AI usage | 5.4% | 2024 | Medium-High |
| U.S. expected AI use | 6.6% | Fall 2024 | Medium-High |
| UK firm AI adoption | 9% | 2023 | High |
| UK projected adoption (plans) | 22% | 2024 | Medium |
Interpretation
GAIAI v1.0 is a benchmark family, not a single global adoption rate. EU/OECD systems show ~20% adoption, but this reflects broader definitions and longer reference periods than the U.S. BTOS (5.4%, two-week window). The 38-point EU size gap (large vs small) and the 70.89% skills barrier are the most actionable findings for policy and strategy.
Adoption Levels and Growth Trajectories
Official statistical systems show consistent acceleration in enterprise AI adoption across all major economies measured.
Global AI Adoption At A Glance
Five official statistical systems, latest available data
EU
19.95%
2025
+6.47 pp
OECD
20.2%
2025
2.3× since '23
Canada
12.2%
Q2 2025
2× YoY
USA
5.4%
Feb 2024
+46% in 5mo
UK
9%
2023
→22% planned
⚠️ These rates use different definitions, time windows, and population frames — they are not directly comparable. See Verification Notes for details.
EU Enterprise AI Adoption Trend
% of EU enterprises (10+ employees) using AI technologies
Source: Eurostat Statistics Explained. +6.47 pp acceleration in 2024→2025.
OECD Firm AI Adoption Trend
% of firms reporting AI use, 2023–2025
Source: OECD ICT Access & Usage Database, Jan 2026 announcement
AI Adoption by Geography
Latest official enterprise/firm adoption rates (not directly comparable)
⚠️ Different time windows and definitions — see Verification Notes
Cross-Geography Adoption Comparison
| Geography | Latest Rate | Year | Previous | Growth | Source |
|---|---|---|---|---|---|
| European Union | 19.95% | 2025 | 13.5% (2024) | +6.47 pp | Eurostat |
| OECD | 20.2% | 2025 | 8.7% (2023) | +132% | OECD |
| Canada | 12.2% | Q2 2025 | 6.1% (Q2 2024) | +100% | StatCan |
| United States | 5.4% | Feb 2024 | 3.7% (Sep 2023) | +46% | Census |
| United Kingdom | 9% | 2023 | — | — | ONS |
Key Insight: All five systems show acceleration, but levels are not directly comparable due to different time windows (EU: "used at least one AI technology"; US BTOS: "previous two weeks"; Canada: "last 12 months"). The EU and OECD show the highest rates (~20%), reflecting broader definitions and longer reference periods.
Size Divide and SME Diffusion
Two independent official systems highlight large SME–large gaps. "Mainstream" diffusion is primarily an SME enablement challenge.
Enterprise AI Adoption by Size Class
EU (Eurostat) vs OECD, 2025 — 38 pp gap between large and small
- EU (Eurostat)
- OECD
Source: Eurostat 2025, OECD Jan 2026 announcement. OECD medium not separately reported.
Enterprise AI Adoption by Size Class
| Size Class | EU (Eurostat) | OECD | Gap vs Large |
|---|---|---|---|
| Large enterprises | 55.03% | 52.0% | — |
| Medium enterprises | 30.36% | — | −24.7 pp (EU) |
| Small enterprises | 17.00% | 17.4% | −38 pp (EU) / −34.6 pp (OECD) |
These gaps imply that "mainstream" diffusion is primarily an SME enablement challenge. Without targeted interventions — accessible tools, structured training, and governance frameworks scaled for smaller organizations — the adoption divide will widen.
U.S. size patterns: Census BTOS research also examines size-class effects, suggesting possible non-linear ("U-shaped") patterns where very small and very large firms may exhibit different adoption dynamics than mid-size businesses. Further BTOS releases may clarify this pattern.
Use-Case, Technology Mix, and Sector Concentration
Eurostat's EU-wide technology-type distribution indicates that language/data processing dominates early AI adoption, and OECD data reveals strong sectoral concentration in ICT and professional services.
AI Technology Types Used (EU Enterprises)
% of all EU enterprises (10+ employees), 2025
Source: Eurostat 2025. Text mining leads at 11.75%; generative media tools follow.
Purpose of AI Use Among EU Adopters
% of AI-using enterprises by use case, 2025
Source: Eurostat 2025. Denominator: enterprises using AI technologies.
OECD AI Adoption by Sector
% of firms using AI by industry, 2025
Source: OECD Jan 2026 announcement
AI Technology Types Used (EU Enterprises, 2025)
| Technology Type | Share of Enterprises |
|---|---|
| Text mining / NLP | 11.75% |
| Image/video/audio generation | Prominent (Eurostat detail) |
| NLG / speech synthesis | Prominent (Eurostat detail) |
| Machine learning | Listed (Eurostat AI technology list) |
| AI-based automation | Listed (Eurostat AI technology list) |
Purpose of AI Use Among EU Adopters (2025)
| Purpose | Share of AI-Using Enterprises |
|---|---|
| Marketing or sales | 34.70% |
| Business administration / management | 31.05% |
OECD Sector Breakdown (2025)
| Sector | AI Adoption Rate | Implication |
|---|---|---|
| ICT | 57.3% | Highest-adopting sector; AI-native tools widely used |
| Professional / scientific services | 36.8% | Knowledge-intensive; NLP/analytics applications |
| Other sectors | <20% | Diffusion to lagging sectors is the key frontier |
Canada application mix: Canada's planned application data shows virtual agents/chatbots and AI analytics as leading expected application classes among businesses planning to adopt AI, indicating similar patterns across North America. Among Canadian businesses already using AI, common operational responses include staff training, workflow adjustments, and cloud infrastructure purchases.
Barriers and Governance-Adjacent Constraints
Across EU, UK, and Canadian measurement, the highest-frequency barriers are not purely financial. Skills, legal clarity, and perceived relevance dominate — but the specific #1 barrier differs by market.
#1 Barrier to AI Adoption by Geography
Different markets face different primary constraints
Among enterprises that considered AI but did not adopt
Among businesses with no plans to adopt AI
Among all firms surveyed by ONS MES
Sources: Eurostat 2025, Statistics Canada Q3 2025, UK ONS MES 2023. ⚠️ Denominators differ across surveys — see Verification Notes.
Barrier Comparison Across Official Systems
| Geography | #1 Barrier | #2 Barrier | #3 Barrier | Source |
|---|---|---|---|---|
| EU | Lack of expertise (70.89%) | Legal clarity / consequences | Data protection / privacy | Eurostat |
| UK | Identifying use cases (39%) | Cost (21%) | AI expertise / skills (16%) | ONS |
| Canada | "AI not relevant" (78.1%) | Lack of knowledge | Privacy / security concerns | StatCan |
Cross-System Pattern: The barrier taxonomy reveals that adoption constraints are not purely financial. Skills/expertise (EU), use-case identification (UK), and perceived relevance (Canada) consistently rank above cost. This implies that policy interventions and vendor strategies should prioritize capability building, use-case demonstration, and relevance framing — not just subsidy or cost reduction.
Adoption Pipeline and Forward Indicators
Official sources reveal a significant pool of enterprises at the "consideration" or "planning" stage — a measurable conversion pipeline that, if activated, could substantially increase measured adoption.
Canada AI Adoption Pipeline (Q2–Q3 2025)
StatsCan business survey: current use, planned use, and non-adoption
12.2%
Currently using AI
14.5%
Plan to use (next 12mo)
66.7%
No plans to adopt
Source: Statistics Canada, Canadian Survey on Business Conditions, Q2–Q3 2025
Forward Indicators Across Systems
| Indicator | Value | Geography | Source |
|---|---|---|---|
| Enterprises that "considered using AI" | 14.21% | EU | Eurostat 2025 |
| Businesses planning to use AI (next 12mo) | 14.5% | Canada | StatCan Q3 2025 |
| Expected AI use (early Fall 2024) | 6.6% | United States | Census BTOS |
| Projected adoption based on plans | 22% | United Kingdom | ONS MES 2024 projection |
Pipeline Insight: Forward indicators consistently show that a significant pool of enterprises is at the "consideration" stage. In the EU, 14.21% of non-adopters have considered AI — a conversion pool that, if activated, could substantially increase measured adoption in 2026. This report treats "considered using" and "plans to use" as pipeline indicators, kept separate from realized adoption to avoid mixing intent with behavior.
Governance Environment and Compliance Signals
Regulatory and standards milestones shape enterprise decision-making, even though governance frameworks are not yet consistently measurable as adoption statistics. Enterprises face accountability pressures (regulatory, customer, investor), and standards create common control language.
Key Governance Frameworks
| Framework | Publisher | Date | Scope |
|---|---|---|---|
| EU AI Act | European Union | Jul 2024 | Binding regulation; risk-based approach |
| NIST AI RMF 1.0 | NIST (U.S.) | Jan 2023 | Voluntary, cross-sector framework |
| ISO/IEC 42001 | ISO | Dec 2023 | AI management system requirements |
| NIST GenAI Profile | NIST (U.S.) | Jul 2024 | Companion resource for generative AI risks |
| OECD AI Principles | OECD | May 2019 | Intergovernmental AI principles |
| UNESCO AI Ethics | UNESCO | Nov 2021 | Member-state ethics recommendation |
| Hiroshima Process | G7 / Japan MOFA | Oct 2023 | G7 guiding principles for AI |
Note: ISO/IEC 42001 certificate counts are not yet published as a stable global adoption indicator. The standard exists (published Dec 2023), but globally comparable AI management system certification metrics remain in development. The ISO Survey now redirects to IAF CertSearch for certification data.
Outlook Scenarios (2026–2028)
Important: Scenarios are projections (not forecasts) derived from observed official trends and known policy/measurement trajectories. No unsourced numeric forecasts are introduced.
Accelerated Diffusion
If SME capability gaps are addressedSME adoption rises faster due to expanded low-cost tool availability, stronger training uptake, and clearer governance tooling. Consistent with the observation that a large fraction of non-adopters cite capability/skills and use-case clarity — targeted interventions could amplify adoption.
Baseline Continuation
Current trajectory persistsAdoption continues rising in EU/OECD systems but remains uneven, with SMEs lagging. Barriers remain primarily skills/use-case identification and relevance. EU adoption at 19.95% and OECD firms at 20.2% provide the starting baseline.
Constrained Adoption
If barriers persist or intensifyAdoption growth slows as organizations encounter ROI uncertainty, governance burden, or capability constraints. Official barrier data show persistent constraints — expertise shortages and legal/privacy uncertainty in the EU; relevance and knowledge gaps in Canada.
Verification Notes
Conflicts and Non-Comparabilities
Different adoption questions and time windows
U.S. BTOS measures use in the previous two weeks (3.7% Sep 2023 → 5.4% Feb 2024). Canada's CSBC measures use over the last 12 months (12.2% Q2 2025). EU Eurostat measures whether enterprises used at least one listed AI technology (19.95% in 2025). These are not directly numerically comparable without adjustment.
Different population frames
Eurostat uses enterprises with 10+ employees and self-employed persons with specified coverage of economic activities; size classes defined as small (10–49), medium (50–249), large (≥250). The U.S. BTOS is representative of all nonfarm employer businesses with additional exclusions and design features. UK ONS uses the Management and Expectations Survey frame.
"Statistics in development / experimental" status
UK ONS describes its MES as official statistics in development. U.S. BTOS is positioned as an experimental product family. Confidence is high but not equivalent to long-established annual surveys.
Conflicting adoption levels across surveys
Different definitions yield different rates. This report maintains multiple sources to explain definitional differences rather than cherry-picking a single "global rate." The GAIAI benchmark family approach avoids false precision.
Confidence Scoring Rationale
| Level | Criteria | Sources |
|---|---|---|
| High | Official statistical agencies with clear numeric values and definitions | Eurostat, OECD announcement, Statistics Canada |
| Medium-High | Official but "experimental/working paper" sources or where thresholds not fully specified | U.S. Census BTOS working paper, OECD size-class shares |
| Medium | Projections based on reported plans, not realized adoption | UK ONS MES projected adoption |
Data Gaps
- Global coverage gap: Official firm-level AI adoption statistics are concentrated in OECD/EU systems. Many countries do not publish comparable ICT/AI business usage series.
- Governance maturity gap: Globally comparable "enterprise AI governance maturity" metrics are not yet published as standardized official datasets specific to AI.
- Sector granularity: OECD reports top sectors (ICT 57.3%, professional services 36.8%), but deeper NACE/NAICS-level analysis requires dataset-level access not available in press releases.
- Global South gap: Evidence from Microsoft's AI Diffusion report suggests geographic divides in genAI usage, but this is a secondary source and not comparable to official enterprise adoption measures.
- Uncertainty quantification: Sample sizes and standard errors are available for some sources (Census working paper, StatCan methodology) but not consistently published across all systems.
Data Dictionary (GAIAI-CI v1.0)
The GAIAI-CI v1.0 dataset follows a consistent schema designed for reproducible ingestion and future updates. All fields are documented below.
| Field | Type | Description |
|---|---|---|
| metric_name | string | Stable identifier (snake_case) for the indicator |
| value | number | Numeric value (float) |
| unit | string | Unit descriptor (e.g., percent_of_enterprises) |
| year | integer | Reference year (or year of the reference period) |
| geography | string | Geographic scope (EU, OECD, Canada, US, UK) |
| definition | string | Indicator definition + denominator |
| source_url | string | Public URL to primary publisher page |
| publisher | string | Publishing organization |
| publish_date | string | Publication date if available (ISO 8601 or descriptive) |
| accessed_date | string | Access date used for reproducibility |
| notes | string | Known caveats (time window, denominator subsets, experimental status) |
| confidence | string | Qualitative confidence (High / Medium-High / Medium / Low) |
Versioning: GAIAI follows semantic versioning. Minor versions (1.1, 1.2) add indicators or update values within the same framework. Major versions (2.0) indicate structural changes to indicators, definitions, or systems covered. The canonical URI scheme is: alicelabs.ai/reports/global-ai-adoption-index-{year}
Update Cadence
| System | Expected Refresh | Cadence |
|---|---|---|
| EU (Eurostat) | December 2026 (planned article update noted by Eurostat) | Annual |
| OECD | Announcement tied to ICT Access and Usage Database refresh | Annual |
| Canada (StatsCan) | New CSBC AI modules/analyses | Quarterly cycles observed |
| U.S. (Census BTOS) | Quarterly rollups from BTOS outputs | Biweekly underlying; quarterly analysis |
| UK (ONS) | Next MES wave or ONS release schedule | Periodic |
Frequently Asked Questions
What is the global AI adoption rate in 2025?
There is no single global AI adoption rate. The most comprehensive official measures show: 19.95% of EU enterprises (Eurostat, 2025), 20.2% of OECD firms (OECD, 2025), 12.2% of Canadian businesses (Statistics Canada, Q2 2025), 5.4% of U.S. firms (Census BTOS, Feb 2024), and 9% of UK firms (ONS, 2023). These are not directly comparable due to different definitions and time windows.
Why are AI adoption rates so different across countries?
Different statistical systems measure different things: the U.S. BTOS asks about AI use in the previous two weeks, Canada asks about the last 12 months, and Eurostat asks whether enterprises used at least one listed AI technology. Broader definitions and longer time windows produce higher reported rates. Population frames also differ (enterprises 10+ employees vs all employers).
What is the biggest barrier to AI adoption?
The #1 barrier varies by market. In the EU, 70.89% of enterprises that considered AI cite lack of expertise. In Canada, 78.1% of non-planners say AI is "not relevant." In the UK, 39% of firms struggle to identify appropriate use cases. Skills, relevance perception, and use-case identification are consistently the top barriers across systems.
How large is the SME–large enterprise AI adoption gap?
In the EU, 55.03% of large enterprises use AI vs 17% of small enterprises — a gap of approximately 38 percentage points. The OECD shows a similar pattern: 52.0% large vs 17.4% small (34.6 pp gap). This size divide is the most consistent structural finding across official systems.
Which industries have the highest AI adoption?
OECD data (2025) shows ICT firms at 57.3% adoption and professional/scientific services at 36.8%. Eurostat data confirms that marketing/sales (34.70%) and business administration (31.05%) are the most common use cases among AI-adopting EU enterprises. Lagging sectors remain well below 20%.
What is the GAIAI dataset and how can I use it?
GAIAI-CI v1.0 is a machine-readable dataset containing 20 core indicators from five official statistical systems. It is available in CSV and JSON formats for download. Each indicator includes the metric name, value, unit, year, geography, definition, source URL, publisher, and confidence level. The dataset is designed for reproducible analysis and benchmarking. License: CC BY 4.0.
How fast is AI adoption growing?
OECD firm AI adoption more than doubled in two years: 8.7% (2023) → 14.2% (2024) → 20.2% (2025). EU enterprise adoption rose 6.47 pp in one year (13.5% to 19.95%). Canada saw a 100% year-over-year increase (6.1% to 12.2%). All major official systems show consistent acceleration.
How does this index compare to McKinsey or other surveys?
GAIAI v1.0 relies exclusively on official statistical sources (Eurostat, OECD, Statistics Canada, U.S. Census Bureau, UK ONS) — not private vendor surveys. Private surveys (McKinsey, Microsoft, etc.) are referenced only as secondary context. This ensures reproducibility, transparency, and traceability to publicly accessible data.
When will the next GAIAI update be published?
GAIAI follows a quarterly review / semi-annual update cadence. EU data refreshes annually (Eurostat planned update: December 2026). OECD updates are tied to ICT Access and Usage Database releases. Canada's StatCan publishes quarterly. The next GAIAI minor version update is expected when new Eurostat or OECD data becomes available.
How should I cite this report?
APA: Alice Labs Research (2026). Global AI Adoption Index 2026. Alice Labs. Version 1.0. https://alicelabs.ai/reports/global-ai-adoption-index-2026. See the "Cite This Report" section for BibTeX and MLA formats.
Methodology
Index construction (GAIAI v1.0): GAIAI v1.0 is a benchmark family rather than a single scalar world ranking, because official adoption series are not globally complete and are not definitionally identical across systems. Therefore, GAIAI v1.0 publishes a core indicator set (GAIAI-CI) and supports comparisons within each system's definitional context.
Data collection: 100% desk research (no interviews), leveraging publicly accessible open data, official statistics, institutional reports, and documented sources. Access date for all sources: 2026-02-16.
Confidence scoring: High confidence = official statistical agencies (Eurostat, OECD, Statistics Canada). Medium-High = official but "experimental/working paper" sources (U.S. Census BTOS) or where thresholds are not fully specified. Medium = projections based on reported plans.
Verification principle: If a claim cannot be tied to a publicly accessible source with publisher + publish date (when available) + access date, it is excluded.
Where computations are presented (e.g., "gap in percentage points"), they are arithmetic transforms of official published values, not new estimates.
Source reliability legend: High = official statistics / primary law / standards body; Medium = reputable intergovernmental/corporate technical report with methods; Low = secondary commentary or media summaries (used only for context, not core metrics).
Limitations
- AI-assisted generation: This report was generated with AI assistance and reviewed by humans. While we strive for accuracy and cite all sources, AI-generated content may contain errors, hallucinations, or misinterpretations.
- Not peer-reviewed: This is exploratory research, not academic peer-reviewed work. Treat findings as insights requiring further validation.
- Not a single global rate: GAIAI v1.0 does not produce a single "world AI adoption percentage" because official definitions, time windows, and population frames differ across surveys.
- Coverage limitations: Official enterprise AI statistics are concentrated in OECD/EU systems. Many regions lack comparable data.
- Sectoral granularity: Detailed NACE/NAICS-level sector analysis is limited by press-release-level data availability; deeper analysis requires dataset-level access.
- Governance measurement gap: AI management system certification metrics (e.g., ISO/IEC 42001 counts) are not yet available as a stable global indicator.
Data Sources
23 primary sources
Version History
Initial release. 20 core indicators across 5 official statistical systems (EU, OECD, Canada, U.S., UK). Includes verification notes, data dictionary, 3 outlook scenarios, 10 FAQs, interactive charts, and LLM-extractable summary block.