White paper · English edition
Global AI in the Real Economy: From Adoption Hype to Industrial Value
15 countries and territories · 12 major industries A six-layer measurement framework and three-stage maturity model
20.0%
EU-27 enterprise AI use (2025, 10+ employees)
88%
Organisational adoption in executive surveys (2025)
23%
Agentic systems scaled in the enterprise (2025)
542,076
industrial robots installed worldwide (2024)
What is inside
Six-layer measurement framework · three-stage maturity model · 15 country tables 12 industry tables · value conversion and productivity evidence · physical AI and robotics data Industrial-grade AI thresholds · OpenAI hardware fact-check · clickable sources on every page
InsightBridge Global LLC / Dr. Tong Yin
For corporate executives, government bodies, industrial investors and AI product leaders Evidence rule: every number links to the page that states it
Prepared August 2026 · Data cut-off 15 August 2026
Public release · EN
Copyright, methodology and citation rules
How this white paper was assembled and how it should be quoted
1. Publisher and attribution
This white paper was prepared and published by InsightBridge Global LLC and is attributed to InsightBridge Global LLC / Dr. Tong Yin. It was prepared in August 2026 and draws exclusively on material publicly available as at 15 August 2026.
2. Six evidence rules
— Single evidence base. Only figures verified in the underlying research file are used. No new numbers are introduced and no market-size forecasts are made.
— Unverifiable means n.a. Where no verifiable official figure could be obtained, the field reads n.a. Consumer data, telemetry estimates and industry guesses are never substituted.
— Never merge different bases. Enterprise adoption rates, population-level diffusion rates and industry surveys are three non-interchangeable measurement systems. They are presented in separate chapters and are never summed, divided or ranked together.
— Sources on every page. Numbered superscripts in the body text correspond to full, clickable URLs at the foot of the same page, so any figure can be checked page by page.
— Conflicts of interest are labelled. Vendor and vendor-commissioned research (NVIDIA, Thomson Reuters, Itron, Trimble, IBM–NRF and others) is flagged inside the tables and must not be read as an industry average.
— Self-assessment is distinguished from audit. All financial and productivity outcomes based on respondent judgement are labelled as self-reported and are not treated as audited financial data.
3. Comparability grades (A / B / C)
Table 1 Definition of comparability grades
Grade Meaning Applies in this white paper to A
Follows the Eurostat ICT enterprise survey framework (enterprises with 10 or more employees, NACE Rev.2 C–J, L–N and group 95.1, an itemised technology
list) and is therefore directly comparable1
Germany, France, Denmark, EU-27
B
Official national statistics whose size threshold, recall period, sector coverage or technology list differs from grade A; usable for orders of magnitude and trends, not for ranking United States, United Kingdom, Canada, Korea, Singapore, Brazil
C
Authoritative institutional surveys, industry-body surveys or telemetry estimates with self-selected samples or a non-enterprise base; must not be ranked alongside official statistics China (enterprise adoption), Japan, India, United Arab Emirates, Saudi Arabia
4. Copyright and permitted use
© 2026 InsightBridge Global LLC. All rights reserved. Tables and charts may be quoted provided the source, the definitional caveats and the disclaimer are retained in full. Individual figures must not be extracted for cross-country rankings or investment marketing. This white paper is not investment, legal or regulatory advice; the full disclaimer appears on the back cover.
1. Eurostat Statistics Explained: Use of artificial intelligence in enterprises — https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises
Copyright and method
Contents
Table of contents
Copyright, methodology and citation rules Executive summary, part 1: a two-speed reality Executive summary, part 2: the next threshold is physical Chapter 1 — A six-layer measurement framework Chapter 1 continued — Maturity model and six warnings Chapter 2 — Country comparison, part 1: official enterprise adoption Chapter 2 continued — The structural gap between large firms and SMEs Chapter 2 continued — Country data table 1 of 5 Chapter 2 continued — Country data table 2 of 5 Chapter 2 continued — Country data table 3 of 5 Chapter 2 continued — Country data table 4 of 5 Chapter 2 continued — Country data table 5 of 5 Chapter 2 continued — Two cross-country series (population and organisation) Chapter 3 — Sector comparison, part 1: a single consistent benchmark Chapter 3 continued — Sector comparison, part 2: divergence in official statistics Chapter 3 continued — Sector data table 1 of 4 Chapter 3 continued — Sector data table 2 of 4 Chapter 3 continued — Sector data table 3 of 4 Chapter 3 continued — Sector data table 4 of 4 Chapter 3 continued — Four kinds of "use" that must be separated Chapter 4 — The adoption-to-value gap Chapter 4 continued — Value conversion table 1 of 3 Chapter 4 continued — Value conversion table 2 of 3
Contents
Contents, continued
Table of contents
Chapter 4 continued — Value conversion table 3 of 3 Chapter 4 continued — Productivity evidence: seven studies that cannot be averaged Chapter 4 continued — Five datasets and the caveats Chapter 5 — Physical AI and hardware integration, part 1 Chapter 5 continued — Division of labour from simulation to the edge Chapter 5 continued — Deployed enterprise and factory cases Chapter 5 continued — Global industrial robot data Chapter 5 continued — Installations and density (Figures 6 and 7) Chapter 5 continued — Three structural facts Chapter 6 — Product thresholds for industrial-grade AI (Figure 8) Chapter 6 continued — First-hand evidence for each threshold Chapter 6 continued — A threshold checklist for procurement Chapter 6 continued — The engineering turn Chapter 7 — OpenAI hardware fact-check, part 1 of 3 Chapter 7 continued — OpenAI hardware fact-check, part 2 of 3 Chapter 7 continued — OpenAI hardware fact-check, part 3 of 3 Chapter 7 continued — The corrected formulation Chapter 8 — Where the value pools are moving Chapter 8 continued — Implications for corporates and government Chapter 8 continued — Implications for start-ups and investors Chapter 9 — Data limitations and rules of use Chapter 10 — Conclusions: eight findings
Contents
Executive summary, part 1: a two-speed reality
Diffusion is fast; organisational value capture is not
Today's debate about AI rests on a category error: personal exposure, employee usage, corporate procurement, production deployment and auditable financial return are all described as "adoption". This white paper separates them into six measurable layers, covers 15 countries and territories and 12 major industries, and treats the spread of software tools separately from industrial-grade, hardware-integrated physical AI.
20.0%
EU-27 enterprise AI use (2025, 10+ employees)
88%
Organisational adoption in executive surveys (undefined)
23%
Agentic systems scaled somewhere in the enterprise
36%
Self-reported improvement in profitability
1. Diffusion is genuinely faster than value capture, but "diffusion" means different things
On an official statistical basis (the enterprise as the unit of observation, 10 or more employees, an itemised list of AI technologies), 20.0% of EU-27 enterprises used AI in 20252. In the same period, an executive sample reported that 88% of organisations use AI regularly in at least one business function3. The fourfold gap is not a contradiction but a difference of base and definition: the first is a census-style probability sample, the second a self-selected executive panel in which the term "adoption" is explicitly "left undefined"4.
2. Scaling and financial value run far behind adoption
In the same 2025 McKinsey survey, only 23% of organisations had scaled agentic systems anywhere in the enterprise, defined as "expanded deployment and adoption within at least one business function"3. BCG's global study finds 5% of companies are "future-built", 35% are scaling and 60% obtain "almost no material value"5. The Stanford AI Index 2026, citing McKinsey, reports that 36% of respondents see improved profitability and 33% improved organic revenue growth6.
3. Firm size is the most stable and most reproducible stratifying variable
Denmark: 75% of enterprises with 250 or more employees versus 37% of those with 10–49 (2025)7. France: 58% versus 15% (2025)8. Germany: 57% versus 23% (2025)9. Korea: 66.9% versus 30.6% (2024)10. Singapore: non-SMEs 62.5% versus SMEs
14.5% (2024)11.
2. Eurostat: Use of AI in enterprises, 2025 — https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251211-2
3. McKinsey, State of AI: Global Survey 2025 — https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
4. McKinsey, The State of AI (PDF, methodology) — https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/the%20state%20of%20 ai/2025/the-state-of-ai-how-organizations-are-rewiring-to-capture-value_final.pdf
5. BCG, The Widening AI Value Gap — https://www.bcg.com/publications/2025/are-you-generating-value-from-ai-the-widening-gap
6. Stanford AI Index 2026, chapter 4 (Economy) — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
7. Danish Agency for Digital Government, ITAV fact sheet 2023–2025 — https://digst.dk/media/5vkpv2uj/faktaark-virksomhedernes-anvendelse-af-ai-2023-2025-itav.pdf
8. INSEE Premiere no. 2120 — https://www.insee.fr/fr/statistiques/9025878
9. Destatis (Federal Statistical Office of Germany) — https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Unternehmen/IKT-in-Unternehmen-IKT-Branche/Tabellen/ikti-unternehmen-kuenstliche-intelligenz.html
10. NIA, 2025 Enterprise Informatisation Statistics (via Etnews) — https://www.etnews.com/20260120000071
11. IMDA, Singapore Digital Economy Report 2025 — https://www.imda.gov.sg/-/media/imda/files/about/resources/corporate-publications/annual-report/imda-sgde-report-fy2024-2025.pdf
Executive summary
Executive summary, part 2: the next threshold is physical and engineering-led
Strategy has moved to physical AI; enterprise penetration has not
4. The shift to sector-specific, industrial-grade, hardware-integrated AI is documented
On 16 March 2026 NVIDIA's founder Jensen Huang stated that "physical AI has arrived — every industrial company will become a robotics company". FANUC, ABB Robotics, YASKAWA and KUKA (with more than two million robots installed worldwide) are integrating Omniverse libraries and the Isaac simulation framework into virtual commissioning, and are embedding Jetson modules in controllers for real-time inference at the edge12. Foxconn (Fii) used Omniverse digital twins to cut "time to market, factory build and factory planning by roughly 50%" and PhysicsNeMo to accelerate CFD thermal simulation by 150 times, from hours to minutes13.
5. Yet penetration of the physical layer remains in single digits
Among German enterprises that use AI, only 6% use technologies for autonomous machine movement14. Denmark recorded 2% in both 2023 and 2024 — the only AI category with no growth15. Brazil recorded 7% in 202516. The strategic narrative and enterprise-level penetration must be presented together, or the reader will draw opposite conclusions.
6. Engineering reliability can decide usability without any change of model
CAICT reports that across 11 Chinese MaaS platforms the average call success rate for the DeepSeek-R1 service rose from
87.01% in February 2025 to 99.36% in September, output tokens per second rose from 17.86 to 26.76, and time to first token fell from 3.07 seconds to 1.02 seconds17. This is the clearest official evidence in this study of industrial-grade reliability metrics replacing benchmark scores.
The core finding
"Personal exposure and tool-level usage are close to mainstream. Organisational workflow redesign and auditable financial returns remain a minority achievement. The gap does not lie in model capability but in depth of deployment, the data and governance foundation, and process change that can be booked in the profit and loss account."
The central conclusion of this white paper; the supporting evidence is set out in chapter 4.
Three rules for readers
First, never place 88% and 20% on the same chart. Second, never use population-level diffusion rates to rank countries for policy purposes. Third, treat outcome figures from vendor surveys as sample experience, not industry averages. All three caveats are set out in full in chapter 9, Data limitations and rules of use.
12. NVIDIA Newsroom, 16 March 2026 — https://nvidianews.nvidia.com/news/nvidia-and-global-robotics-leaders-take-physical-ai-to-the-real-world
13. NVIDIA case study: Foxconn (Fii) — https://www.nvidia.com/en-us/case-studies/foxconn-develops-physical-ai-enabled-smart-factories-with-digital-twins/
14. Destatis (Federal Statistical Office of Germany) — https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Unternehmen/IKT-in-Unternehmen-IKT-Branche/Tabellen/ikti-unternehmen-kuenstliche-intelligenz.html
15. Statistics Denmark (Danmarks Statistik) — https://www.dst.dk/nytpdf/55352
16. Cetic.br, TIC Empresas 2025 — https://cetic.br/media/analises/TIC-Empresas-2025-lancamento.pdf
17. CAICT, AI Industry Development Research Report 2025 — https://www.caict.ac.cn/kxyj/qwfb/bps/202602/P020260202487301304903.pdf
Executive summary
Chapter 1 — A six-layer measurement framework
Each layer must always be quoted together with its base, or errors of an order of magnitude follow
Table 2 The six-layer framework: definitions, bases and risks of misreading
Layer Name Definition Base Representative data Risk of misreading
L1 Exposure / individual use An individual has used a generative AI product in a given period Population, or working-age population (15–64) Microsoft AI Economy Institute basis: roughly one in six people globally in H2 2025; 24.7% of the working-age population in the global north, 14.1% in the global south18 Never to be read as enterprise adoption. Built from aggregated anonymised telemetry adjusted for operating-system share, internet penetration and population; not a survey L2 Enterprise adoption The enterprise answers whether it uses at least one AI technology from a list All enterprises (typically a threshold of 10 or more employees) EU-27 20.0% in 202519; US BTOS 19.8% for the collection period ending 3 May 202620 Different technology lists make figures incomparable; changes in question wording create breaks in series
L3 Piloting The share of organisations in experimentation or pilots Surveyed organisations India: 23% at the pilot stage21; commercial real estate: 88% of investors and 92% of occupiers have launched AI pilots22 A high pilot count is not capability; JLL notes implementation is broad but "most initiatives remain experimental with limited scaling" L4 Production deployment and scaling Entry into real production processes or enterprise-wide rollout Surveyed organisations or use cases India: 47% have multiple use cases live in production21; construction: only 1% have scaled AI across projects23; agents: 23% scaled24 "Production" has no common definition; vendor surveys systematically overstate it
L5 Financial value conversion The share reporting revenue, cost, EBIT, ROI or productivity improvement Organisations using AI Cost 38%, profitability 36%, organic revenue growth 33%, market share 25%25; Denmark: seven in ten AI-using firms report more efficient workflows26 All figures are self-assessed perceptions, not audited financials; the same chart notes that as many respondents report improvement as report no impact L6 Physical deployment AI embedded in robots, edge devices, industrial control and smart terminals All enterprises, or manufacturing employment Germany: only 6% of AI-using enterprises use autonomous machine movement27; Denmark 2% for two consecutive years28; global robot density 132 units per 10,000 employees in 202429 Robot density measures the automation stock, not AI; the IFR describes it as a common basis for relating robot numbers to the size of an economy measured by its workforce
18. Microsoft AI Economy Institute, Global AI Adoption 2025 — https://www.microsoft.com/en-us/corporate-responsibility//topics/ai-economy-institute/reports/global-ai-adoption-2025/
24. McKinsey, State of AI 2025 — https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
19. Eurostat: Use of AI in enterprises, 2025 — https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251211-2
25. Stanford AI Index 2026, chapter 4 (Economy) — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
20. US Census Bureau, BTOS — https://www.census.gov/library/stories/2026/05/ai-use-businesses.html
26. Danish Agency for Digital Government, news release April 2025 — https://digst.dk/nyheder/nyhedsarkiv/2025/april/syv-ud-af-ti-virksomheder-er-blevet-mere-effektive-ved-brug-af-ai/
21. EY–CII survey of Indian enterprises, November 2025 — https://www.ey.com/en_in/newsroom/2025/11/india-s-ai-shift-fro m-pilots-to-performance-47-percent-of-enterprises-have-multiple-ai-use-cases-live-in-production-ey-cii-report
27. Destatis (Federal Statistical Office of Germany) — https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Unternehme n/IKT-in-Unternehmen-IKT-Branche/Tabellen/ikti-unternehmen-kuenstliche-intelligenz.html
22. JLL, 2025 Global Real Estate Technology Survey — https://www.jll.com/en-us/newsroom/real-estates-ai-reality-check-companies-piloting-only-achieved-all-ai-goals
28. Statistics Denmark (Danmarks Statistik) — https://www.dst.dk/nytpdf/55352
23. RICS, AI in Construction 2025 — https://www.rics.org/news-insights/optimism-high-for-ai-in-construction-but-skills-short ages-and-integration-challenges-adoption
29. IFR density data (via The Robot Report) — https://www.therobotreport.com/ifr-reports-robot-density-increase-across-europe-asia-americas/
Chapter 1 — Framework
Chapter 1 continued — Three-stage maturity and six methodological warnings
The model to use consistently, and the caveats that must travel with it
1.2 The three-stage maturity model
Table 3 Three-stage maturity model
Stage Content Layers First-hand evidence
Stage 1 Tool access and experimentation Employees use general-purpose assistants individually; no process redesign L1 and part of L2 In BCG's five-level model of employee adoption, more than 85% of employees remain at levels two and three30
Stage 2 Workflow integration and scaling AI enters existing business processes and spreads across functions L3 → L4 → L5 More than two-thirds of organisations use AI in more than one function and half in three or more31
Stage 3 Industrial / physical AI Determinism, latency, safety certification and edge compute become the gating factors L6 NVIDIA's own term is "production-scale physical AI", delivered through "an open, integrated platform to design, train, test and deploy"32
1.3 Six warnings that must be published with any figure
— The definition of "using AI" differs by country. The 2024 Eurostat questionnaire listed seven technologies and produced 13.48% for the EU-2733; in 2025 image, video and audio generation was added, taking France from seven to eight technologies34. Extending the list raises the adoption rate by construction.
— US BTOS contains a documented break. The original question asked about AI use "in producing goods or services in the last two weeks"; from November 2025 it asks about use "in any business function"3536. Figures before and after cannot be joined into one trend line.
— One country at one point in time can show radically different rates depending on weighting. Late 2025 in the United States: BTOS firm-weighted about 18%; the Atlanta Fed's SBU employment-weighted about 78%, with an equal-weighted estimate of 69.4%. The Federal Reserve itself describes 78% as "a good upper bound on the range of workplace access to AI tools"36.
— The UK statistical office warns explicitly about its headline measure. The ONS states that the indicator "treats all reported use as equivalent and does not distinguish light use, user-level use and more deeply embedded production-grade adoption"37.
— France separates organised use from sporadic personal use. The INSEE survey asks about AI used "in an organised way within the enterprise's departments" and "in principle excludes sporadic, personal use by employees"34.
— Uncalibrated country rankings are not comparable. This white paper therefore assigns an A/B/C comparability grade to every country; see chapter 2.
30. BCG, The AI Adoption Puzzle — https://www.bcg.com/publications/2025/ai-adoption-puzzle-why-usage-up-impact-not
31. McKinsey, State of AI 2025 — https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
32. NVIDIA Newsroom, 16 March 2026 — https://nvidianews.nvidia.com/news/nvidia-and-global-robotics-leaders-take-physical-ai-to-the-real-world
33. Eurostat Statistics Explained — https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises
34. INSEE Premiere no. 2120 — https://www.insee.fr/fr/statistiques/9025878
35. US Census Bureau, BTOS — https://www.census.gov/library/stories/2026/05/ai-use-businesses.html
36. Federal Reserve, FEDS Notes: monitoring AI adoption — https://www.federalreserve.gov/econres/notes/feds-notes/monitoring-ai-adoption-in-the-u-s-economy-20260403.html
37. Office for National Statistics (ONS) — https://www.ons.gov.uk/businessindustryandtrade/business/businessservices/articles/artificialintelligenceinukbusinesses/2023to2026
Chapter 1 — Framework
Chapter 2 — Country comparison, part 1: official enterprise adoption
Only enterprise-based official statistics, graded for comparability
The chart below includes only official statistics that use the enterprise as the unit of observation, each labelled A or B for comparability. Grade A countries share the Eurostat framework and can be compared directly; grade B countries differ in threshold, recall period or sector coverage and support only judgements of magnitude. China, Japan, India, the United Arab Emirates and Saudi Arabia are excluded because no verifiable enterprise-based figure exists.
Figure 1 Enterprise AI adoption on an official statistical basis, latest available year
Three facts that belong together
— The three largest advanced economies cluster tightly between 19% and 20%: the United States at 19.8%38, Canada at
19.2%39 and the EU-27 at 20.0%40.
— Dispersion within the European Union exceeds dispersion between continents: in 2025 Denmark led at 42.0%, followed by Finland at 37.8% and Sweden at 35.0%, while Romania recorded 5.2%, Poland 8.4% and Bulgaria 8.5%40.
— The growth itself is real: the EU-27 moved from 8.1% in 2023 and 13.5% in 2024 to 20.0% in 2025; the United Kingdom rose from about 12% in September 2023 to about 35% in June 202641; Canada moved from 6.1% in Q2 2024 through
12.2% in 2025 to 19.2% in Q2 202639.
38. US Census Bureau, BTOS — https://www.census.gov/library/stories/2026/05/ai-use-businesses.html
39. Statistics Canada, CSBC Q2 2026 — https://www150.statcan.gc.ca/n1/pub/11-621-m/11-621-m2026010-eng.htm
40. Eurostat: Use of AI in enterprises, 2025 — https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251211-2
41. Office for National Statistics (ONS) — https://www.ons.gov.uk/businessindustryandtrade/business/businessservices/articles/artificialintelligenceinukbusinesses/2023to2026
Chapter 2 — Country comparison
Chapter 2 continued — The structural gap between large firms and SMEs
The most reproducible finding in the entire dataset
Figure 2 Enterprise AI adoption, large firms versus small firms and SMEs, latest available year
The gap is widening, not converging
French official data show that between 2023 and 2025 the gap between enterprises with fewer than 50 employees and those with 250 or more widened from 16 to 43 percentage points42. In Denmark, firms with 10–49 employees moved from 12% to 37% over three years while those with 250 or more moved from 51% to 75%43. The cause is not appetite: in Korea the leading reason for not using AI is "the burden of economic cost" at 39.6%, followed by "lack of infrastructure and staff" at
34.9% and "no AI that meets our needs" at 26.8%44; in Canada 40.0% of businesses simply consider AI "not relevant" to them, rising to 41.4% among firms with 1–4 employees45.
A structural point: AI-using firms already account for most economic activity
French official data show that in 2025 enterprises using AI accounted for 66% of turnover and 59% of total employment within the survey scope, up from 49% and 40% in 2024. INSEE states explicitly that 59% "does not mean that 59% of French employees directly use AI". This is why the employment-weighted US figure (about 78%) and the firm-weighted figure (about 18%) can both be correct.
42. INSEE Premiere no. 2120 — https://www.insee.fr/fr/statistiques/9025878
43. Danish Agency for Digital Government, ITAV fact sheet 2023–2025 — https://digst.dk/media/5vkpv2uj/faktaark-virksomhedernes-anvendelse-af-ai-2023-2025-itav.pdf
44. NIA, 2025 Enterprise Informatisation Statistics (via Etnews) — https://www.etnews.com/20260120000071
45. Statistics Canada, CSBC Q2 2026 — https://www150.statcan.gc.ca/n1/pub/11-621-m/11-621-m2026010-eng.htm
Chapter 2 — Country comparison
Chapter 2 continued — Country data table 1 of 5
United States, China, United Kingdom
Table 4 Country data table 1 of 5
Country / territory Latest enterprise AI adoption (year, unit, definition) Generative AI Large firms vs SMEs Production / scaling Financial or productivity outcome Leading sectors / main use cases Grade
United States
19.8% for the collection period ending 3 May 2026 ("used AI in any business function in the last two weeks", BTOS)46; about 18% firm-weighted in December 2025 and about 78% employment-weighted in November 2025 (Atlanta Fed SBU)47 Workforce level: about 41% of workers used generative AI at work in November 2025 (RPS); about 54% of firms had adopted LLMs (SBU, employment-weighted)47 250+ employees 37%; 100–249 32%; 4 or fewer below 20%46 n.a. (BTOS does not separate pilots from production) No official rate of financial conversion Information, finance and insurance, and professional, scientific and technical services all above 30% in late 2025; information firms with 250+ employees about 73% in early 202648 B
China n.a. No verifiable official enterprise adoption rate was found; the 57th CNNIC report gives no enterprise rate and CAICT publishes industry scale and company counts rather than adoption49 Individual basis: 602 million generative AI users as at December 2025, up 141.7% on end-2024, a penetration rate of 42.8% — this is L1 and cannot substitute for enterprise adoption49 n.a.; CAICT describes lightweight, modular and distributed architectures rather than adoption rates50 Industry-side proxy: as at Q1 2025 China had more than 30,000 basic-level, over 1,200 advanced-level and over 230 excellence-level smart factories, covering more than 80% of manufacturing categories51 Smart-factory basis: R&D cycles shortened by 28.4% and production efficiency up 22.3% on average (the report does not state that AI is the sole cause)51; MaaS call success rate 87.01% → 99.36%50 Large models are being deployed fastest in electronics, raw materials and consumer goods, covering R&D, pilot validation, production and operations51; public-cloud model calls concentrate in text processing, role play, assistants, search, coding and marketing, together more than half50 C (industry
-side indicators are B)
United Kingdom About 35% in June 2026 (BICS wave 159, enterprises with 10+ employees reporting use of at least one AI technology; about 12% in September 2023)52 n.a. (BICS does not publish a separate generative AI headline) 250+ employees 49% versus 0–9 employees 28% (June 2026)52 Wave 159 added questions on the breadth of AI use within the firm and the share of staff using AI in daily work; the ONS states the headline does not distinguish production-grade adoption52 n.a. (no official rate of financial conversion) Information and communication 58% versus construction 13% (June 2026)52; for financial services see chapter 3 (Bank of England / FCA 75%) B (the BICS sample excludes finance and insurance)
46. US Census Bureau, BTOS — https://www.census.gov/library/stories/2026/05/ai-use-businesses.html
50. CAICT, AI Industry Development Research Report 2025 — https://www.caict.ac.cn/kxyj/qwfb/bps/202602/P020260202487301304903.pdf
47. Federal Reserve, FEDS Notes — https://www.federalreserve.gov/econres/notes/feds-notes/monitoring-ai-adoption-in-the-u-s-economy-20260403.html
51. CNNIC, 56th report — https://www.cnnic.net.cn/NMediaFile/2025/0730/MAIN1753846666507QEK67ZS9DH.pdf
48. PIIE analysis of BTOS by sector — https://www.piie.com/blogs/realtime-economics/2026/adoption-ai-industrial-sectors
52. Office for National Statistics (ONS) — https://www.ons.gov.uk/businessindustryandtrade/business/businessservices/articles/ artificialintelligenceinukbusinesses/2023to2026
49. CNNIC, 57th Statistical Report on Internet Development in China — https://cnnic.cn/n4/2026/0304/c88-11549.html
Chapter 2 — Country comparison
Chapter 2 continued — Country data table 2 of 5
Germany, France, Denmark
Table 5 Country data table 2 of 5
Country / territory Latest enterprise AI adoption (year, unit, definition) Generative AI Large firms vs SMEs Production / scaling Financial or productivity outcome Leading sectors / main use cases Grade
Germany 26% in 2025 (share of all enterprises; the EU enterprise concept applies from the 2025 reporting year)53 Destatis does not use the term generative AI; among AI-using enterprises, generation of natural language including program code 35% and generation of images, video or audio 52%53 250+ employees 57%; 50–249 36%; 10–49 23%53 n.a. (the table does not cover pilots, production or scaling) n.a. (the table does not cover financial or productivity outcomes) Most common technologies among AI users: text mining 52%, image, video or audio generation 52%, speech recognition 42%; autonomous machine movement only 6%53 A
France 18% (reference period Q1 2025, enterprises with 10+ employees in the main market sectors, excluding agriculture and financial services; 8 points above 2024 and 12 points above 2023)54 Image, video and audio generation was added in 2025, taking the list from seven to eight technologies54 250+ employees 58%; 50–249 31%; 10–49 15%; the gap widened from 16 to 43 percentage points54 n.a. Structural fact: AI-using enterprises accounted for 66% of turnover and 59% of total employment in scope (49% and 40% in 2024); INSEE states 59% does not mean 59% of employees use AI54 n.a. (the INSEE page retrieved does not publish a sector ranking) A
Denmark 42% in 2025 (enterprises with 10+ employees in private non-financial urban industries; 15% in 2023 and 28% in 2024)55; Eurostat published Denmark as the EU's highest at 42.0%56 In 2024 the most common uses were generating written or spoken language 18% and text analysis 17%57 250+ 75%; 100–249 62%; 50–99 56%; 10–49 37% (N=4,071)55 n.a. (ITAV does not separate pilots from production) Official basis: among AI-using enterprises, seven in ten report more efficient workflows, more than half see improved products or services and one third report higher earnings58 2025: information and communication 79%; business services 51%; industry 39%; trade and transport 38%; construction 24%55 A
53. Destatis (Federal Statistical Office of Germany) — https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Unterne hmen/IKT-in-Unternehmen-IKT-Branche/Tabellen/ikti-unternehmen-kuenstliche-intelligenz.html
56. Eurostat: Use of AI in enterprises, 2025 — https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251211-2
54. INSEE Premiere no. 2120 — https://www.insee.fr/fr/statistiques/9025878
57. Statistics Denmark (Danmarks Statistik) — https://www.dst.dk/nytpdf/55352
55. Danish Agency for Digital Government, ITAV 2025 — https://digst.dk/media/5vkpv2uj/faktaark-virksomhedernes-anvendelse-af-ai-2023-2025-itav.pdf
58. Danish Agency for Digital Government, news release April 2025 — https://digst.dk/nyheder/nyhedsarkiv/2025/april/syv-ud-af-ti-virksomheder-er-blevet-mere-effektive-ved-brug-af-ai/
Chapter 2 — Country comparison
Chapter 2 continued — Country data table 3 of 5
Canada, Japan, Korea
Table 6 Country data table 3 of 5
Country / territory Latest enterprise AI adoption (year, unit, definition) Generative AI Large firms vs SMEs Production / scaling Financial or productivity outcome Leading sectors / main use cases Grade
Canada
19.2% in Q2 2026 ("used AI in producing goods or delivering services over the past 12 months", CSBC; sample 21,105, responses 9,251)59; 12.2% in Q2 2025 and 6.1% in Q2 2024 n.a. (the release does not publish a separate generative AI headline) 100+ employees 27.8%; 1–4 employees 19.9%59 n.a. Official micro research links AI adoption and productivity using pooled SDTIU 2019 and 2021 data — the data years are 2019 and 2021 and cannot support conclusions about 2025–202660
40.0% (two in five) of businesses say AI is not relevant to them: 41.4% of firms with 1–4 employees and 21.3% of those with 100 or more59 B (includes micro firms of 1–4 emp loyees; 12-month recall)
Japan n.a. No official rate using the enterprise as the unit of observation, comparable with Eurostat, was found. The available official figure is 55.2% of respondents answering that generative AI is used in some part of their work61
55.2% as above; assistance with email, minutes and document preparation 47.3%61 Qualitative: about half of SMEs report no clearly defined policy; the white paper states SMEs lag large firms61 n.a. (this section covers no pilot, production or scaling shares) n.a. (no revenue, cost, productivity or ROI figures). Qualitatively, Japanese respondents most often cite efficiency gains and relief of labour shortages, whereas the other three countries cite business expansion, new customers and innovation61 Enterprises with a policy of active or targeted use: 49.7% in FY2024 and 42.7% in FY2023; the white paper notes this remains persistently below other countries61 C (the base is informed employee s, not ente rprises; no sample size published)
Korea
32.9% (reference year 2024, national establishments with 10+ employees, population of 211,615 units, "use of AI technologies and services"; 30.3% the previous year)62 n.a. (the compendium does not publish a separate generative AI rate) 250+ employees 66.9%; 10–49 30.6% (27.4% the previous year); incorporated firms 34.0% versus sole proprietorships 28.7%62 Mode of use as a depth proxy: freeware 65.1%, purchased commercial software 21.8%, contracts with external suppliers 11.1% — a predominance of free tools indicates stage one62 Digital-industry basis: 24.9% apply AI in decision-making and business activities (15.5% in
2023); 43.5% developed or acquired AI over the past three years (10,323 firms)63 2024: finance and insurance 63.1%, ICT
56.0%, education services 41.4%; construction 15.9% and real estate 16.1% trail62 B (the ques tionnaire draws on Eurostat64)
59. Statistics Canada, CSBC Q2 2026 — https://www150.statcan.gc.ca/n1/pub/11-621-m/11-621-m2026010-eng.htm
62. NIA, 2025 Enterprise Informatisation Statistics (via Etnews) — https://www.etnews.com/20260120000071
60. Statistics Canada, AI and productivity study, 22 April 2026 — https://www150.statcan.gc.ca/n1/pub/36-28-0001/2026004/article/00002-eng.htm
63. Ministry of Science and ICT (MSIT), Korea — https://www.msit.go.kr/eng/bbs/view.do?sCode=eng&mPid;=2&mId;=4&bbsSeqNo;=42&nttSeqNo;=1283
61. MIC Japan, Information and Communications White Paper 2025 — https://www.soumu.go.jp/johotsusintokei/whitepaper/ja/r07/html/nd112220.html
64. Dong-A Science: Korea aligns its survey with Eurostat — https://m.dongascience.com/en/news/63090
Chapter 2 — Country comparison
Chapter 2 continued — Country data table 4 of 5
Singapore, India, United Arab Emirates
Table 7 Country data table 4 of 5
Country / territory Latest enterprise AI adoption (year, unit, definition) Generative AI Large firms vs SMEs Production / scaling Financial or productivity outcome Leading sectors / main use cases Grade
Singapore Overall rate n.a. (IMDA publishes no single figure for all enterprises). By segment: non-SMEs 62.5% (44.0% in 2023); SMEs 14.5% (4.2% in 2023)65 Among AI-using enterprises, 84% use off-the-shelf generative AI tools, 52% adopt digital solutions with AI features and 44% have implemented custom or proprietary AI (pulse survey of about 500 firms)65 As shown: 62.5% versus
14.5%; SME growth is driven mainly by micro and small firms65 Depth proxy: average number of functions covered — SMEs 3, non-SMEs 565 n.a. (the quantified benefits section of the report was not retrieved). Macro: the digital economy reached 18.6% of GDP in 2024, from 14.9% in 201966 2024: information and communication
35.9%, professional services 25.7%, finance and insurance 22.6%, manufacturing 18.0%, retail 8.9%, accommodation and food 4.7%; functions: IT 49%, customer service 43%, finance and accounting 40%65 B (the AI definition is broader than Eurostat)
India Official n.a. Authoritative survey: 47% of enterprises have multiple AI or generative AI use cases live in production and 23% are piloting (EY–CII; 200 organisations across more than 20 industries; C-suite and senior executives, 16 November 2025)67 n.a. (AI and generative AI adoption are not separated) n.a. 47% in production and 23% piloting67 Input constraint: more than 95% of organisations allocate less than 20% of the IT budget to AI and only 4% cross the 20% threshold; 64% report selective workforce transformation and 59% a persistent shortage of AI talent67 91% of leaders cite speed of deployment as the leading factor in build-versus-buy; nearly 60% co-create with start-ups and 78% use a hybrid model67; the NASSCOM index page publishes no adoption figure68 C (n=200, s elf-selecte d C-suite sample)
United Arab Emirates n.a. No official enterprise rate was found. At the individual level, 70.1% of the working-age population (15–64) has used a generative AI product (Q1 2026; 59.4% in H1 2025 and 64.0% in H2), described as the first economy to pass the 70% threshold69
70.1% as above (use of generative AI products) n.a. n.a. n.a. n.a. (no official sector or use-case breakdown) C (a teleme try-derived population diffusion rate, not enterprise adoption7
65. IMDA, Singapore Digital Economy Report 2025 — https://www.imda.gov.sg/-/media/imda/files/about/resources/corporat e-publications/annual-report/imda-sgde-report-fy2024-2025.pdf
68. NASSCOM AI Adoption Index — https://www.nasscom.in/knowledge-center/publications/nasscom-ai-adoption-index
66. IMDA press release: Singapore digital economy — https://www.imda.gov.sg/resources/press-releases-factsheets-and-spe eches/press-releases/2025/singapore-digital-economy
69. Aletihad, citing the Microsoft AI Economy Institute — https://en.aletihad.ae/news/uae/4664430/uae-continues-to-top-world-in-ai-adoption-with-70-1--usage-r
67. EY–CII, November 2025 — https://www.ey.com/en_in/newsroom/2025/11/india-s-ai-shift-from-pilots-to-performance-47percent-of-enterprises-have-multiple-ai-use-cases-live-in-production-ey-cii-report
70. Microsoft AI Economy Institute definition — https://www.microsoft.com/en-us/corporate-responsibility//topics/ai-economy-institute/reports/global-ai-adoption-2025/
Chapter 2 — Country comparison
Chapter 2 continued — Country data table 5 of 5
Saudi Arabia, Brazil, EU-27 total
Table 8 Country data table 5 of 5
Country / territory Latest enterprise AI adoption (year, unit, definition) Generative AI Large firms vs SMEs Production / scaling Financial or productivity outcome Leading sectors / main use cases Grade
Saudi Arabia Enterprise basis n.a. Government basis: about 39% of participating entities currently use AI or are actively experimenting with it and about 56% plan future adoption (SDAIA readiness and adoption survey, July 2024, n=80)71 n.a. n.a. 55% are developing AI solutions, 43% procure commercial solutions and 38% integrate AI into existing applications and operations (n=80)71 Among entities using or piloting AI, 81% report significantly improved service delivery and 61% improved decision-making (n=80)71 Leading use cases: optimising services and processes 71%, data analytics and BI 55%, forecasting 48%, chatbots and user support 45%, document processing 42%, content generation 16%71 C (n=80, go vernment entities only, July
Brazil 17% in 2025 (active enterprises with 10+ employees using some form of artificial intelligence, TIC Empresas 2025, sample 4,174)72. A second official basis covers industry: 41.9% in 2024 (10,167 extractive and manufacturing firms with 100+ employees; 16.9% in 2022)73 — the two bases are entirely different and must not be merged Cetic.br does not use the term generative AI; among AI users, natural language generation rose from 20% in 2024 to 30% in 202572 2025: large firms 50%, medium 32%, small 15% (38%, 29% and 10% in
2024)7274 n.a. (Cetic.br states that pilot, production and scaling data are not provided)74 n.a. (no revenue, cost, productivity or ROI data). On the industrial side, AI used in production fell from 56.4% in 2022 to 52.0% in 2024, while management reached 87.9%, commercialisation 75.2% and product or process development 73.1%73 2025: information and communication 49%, professional activities 24%, services 19%, trade 17%, industry 14%, construction 14%, accommodation and food 10%; by type, workflow automation 68% and autonomous machine movement 7%72 B (CATI survey; see the metho dology note)
EU-27 total
20.0% in 2025 (enterprises with 10+ employees, NACE Rev.2 C–J, L–N and group 95.1; 13.5% in 2024, 8.1% in 2023 and 7.7% in 2021)75 2025: image, video or audio generation 9.5%; generation of written or spoken language 8.8%75 2024: large enterprises
41.17% versus 13.48% for all; of 1.54 million enterprises about 83% are small, 14% medium and 3% large, with a sample of 157,00076 n.a. (the questionnaire does not measure pilots, production or scaling) n.a. (the questionnaire does not measure financial outcomes) 2025 most common uses: text mining 11.8%, image, video or audio generation 9.5%, generation of written or spoken language
8.8%, converting speech to machine-readable format 7.2%75 A (the reference basis)
71. SDAIA, State of AI in Saudi Arabia — https://sdaia.gov.sa/en/MediaCenter/KnowledgeCenter/ResearchLibrary/StateofAIinSaudiArabia.pdf
74. Cetic.br indicator H9 (methodology note) — https://cetic.br/pt/tics/pesquisa/2024/empresas/H9/
72. Cetic.br, TIC Empresas 2025 — https://cetic.br/media/analises/TIC-Empresas-2025-lancamento.pdf
75. Eurostat: Use of AI in enterprises, 2025 — https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251211-2
73. IBGE, PINTEC Semestral — https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/445 51-de-2022-a-2024-percentual-de-empresas-industriais-utilizando-inteligencia-artificial-subiu-de-16-9-para-41-9
76. Eurostat Statistics Explained — https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises
Chapter 2 — Country comparison
Chapter 2 continued — Two cross-country series that must be kept separate
Population-level diffusion and organisation-level survey series
Chapter 4 of the Stanford AI Index 2026 reproduces the Microsoft AI Economy Institute's population-level AI diffusion rate. It is currently the only time series covering a large number of countries on a single basis, but it measures individual use, not enterprise adoption77.
Table 9 Population-level AI diffusion, 13 of the top 30 economies
Economy H1 2025 H2 2025 Change
United Arab Emirates
59.40%
64.00% +4.60%
Singapore
58.60%
60.90% +2.30%
Norway
45.30%
46.40% +1.10%
Ireland
41.70%
44.60% +2.90%
France
40.90%
44.00% +3.10%
United Kingdom
36.40%
38.90% +2.50%
Canada
33.50%
35.00% +1.50%
Sweden
31.20%
33.30% +2.10%
Denmark
26.60%
28.70% +2.10%
Germany
26.50%
28.60% +2.10%
United States
26.30%
28.30% +2.10%
Korea
25.90%
30.70% +4.80%
Finland
25.60%
27.30% +1.70%
Table 10 Organisation-level series by region (McKinsey survey)
Region
North America 61% 77% 91% Europe 58% 75% 90% Asia-Pacific 48% 78% 88% Greater China 57% 80% 82% Developing markets 49% 72% 88% All regions 49% 78% 88%
Risk of misreading
Table 9 must never appear on the same ranking chart as Tables 4 to 8. France ranks fifth on population diffusion at
44.0%, yet its official enterprise adoption rate is only 18%, below the EU-27 figure of 20.0%7879. The AI Index makes the same point in reverse: "despite leading in AI investment and model development, the United States fell to 24th with a population-level adoption rate of 28.3%", and it finds a strong, statistically significant positive correlation between diffusion and GDP per capita77. Table 10 is an executive-survey series; the AI Index notes in the same place that China and Europe posted the largest year-on-year gains, at 13 and 11 percentage points respectively.
77. Stanford AI Index 2026, chapter 4 (Economy) — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
78. INSEE Premiere no. 2120 — https://www.insee.fr/fr/statistiques/9025878
79. Eurostat, 11 December 2025 — https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251211-2
Chapter 2 — Country comparison
Chapter 3 — Sector comparison, part 1: a single consistent benchmark
One source, one sample, one basis for cross-sector comparison
The most common error in sector comparison is placing figures from different surveys side by side. This section therefore begins with the only single-source, single-sample sector benchmark available: the McKinsey global survey of 1,491 participants fielded from 16 to 31 July 2024, measuring regular use of generative AI in at least one function80. The sample dates from July 2024, is not contemporaneous with the 88% headline for 2025, and cannot be ranked alongside official enterprise statistics.
Figure 3 Generative AI adoption by sector on a single consistent sample, July 2024
Why sector divergence matters more than country divergence
— Within the consistent sample the sector spread is 29 percentage points (technology 88% versus energy and materials 59%), whereas on official bases the spread can exceed 60 percentage points (Danish information and communication 79% versus Singaporean accommodation and food 4.7%)8182.
— Sector ceilings determine product strategy: finance and information and communication are the only two sectors in the high-adoption band, with 75% of UK regulated financial firms already using AI83.
— The functional pattern is consistent across sectors: functions tied to information processing, software, customer interaction and internal knowledge work adopt fastest, while strategy and corporate finance and risk and compliance "remain low in most industries" — with financial services the sole exception84.
80. McKinsey, The State of AI (PDF) — https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/the%20state%20of%20ai/2025/the-st ate-of-ai-how-organizations-are-rewiring-to-capture-value_final.pdf
81. Danish Agency for Digital Government, ITAV 2025 — https://digst.dk/media/5vkpv2uj/faktaark-virksomhedernes-anvendelse-af-ai-2023-2025-itav.pdf
82. IMDA, Singapore Digital Economy Report 2025 — https://www.imda.gov.sg/-/media/imda/files/about/resources/corporate-publications/annual-report/imda-sgde-report-fy2024-2025.pdf
83. Bank of England and FCA, AI in UK financial services 2024 — https://www.bankofengland.co.uk/report/2024/artificial-intelligence-in-uk-financial-services-2024
84. Stanford AI Index 2026, chapter 4 — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
Chapter 3 — Sector comparison
Chapter 3 continued — Sector comparison, part 2: divergence in official statistics
Lower levels, identical ordering
Figure 4 Sector AI adoption in four national statistical systems; comparable within panels only
Absolute levels on official bases are far below executive surveys, but the ordering is highly consistent: information and communication leads, construction and accommodation and food trail. That ordering recurs across four mutually independent official statistical systems — Denmark85, Korea86, Singapore87 and Brazil88 — which makes it the most robust sector conclusion in this white paper. Sector taxonomies, size thresholds and AI definitions differ, so comparison is valid only within each panel.
85. Danish Agency for Digital Government, ITAV 2025 — https://digst.dk/media/5vkpv2uj/faktaark-virksomhedernes-anvendelse-af-ai-2023-2025-itav.pdf
86. NIA statistics (via Etnews) — https://www.etnews.com/20260120000071
87. IMDA, Singapore Digital Economy Report 2025 — https://www.imda.gov.sg/-/media/imda/files/about/resources/corporate-publications/annual-report/imda-sgde-report-fy2024-2025.pdf
88. Cetic.br, TIC Empresas 2025 — https://cetic.br/media/analises/TIC-Empresas-2025-lancamento.pdf
Chapter 3 — Sector comparison
Chapter 3 continued — Sector data table 1 of 4
Software and ICT, Financial services, Professional and legal services
Table 11 Sector data table 1 of 4
# Sector AI / generative AI adoption (year) Production / scaling Most common use cases Quantified outcomes Main deployment barriers Source and sample notes
Software and ICT Technology sector generative AI 88% (July 2024, n=199)89; official bases: Denmark information and communication 79% (2025)90; United Kingdom 58% (June 2026)91; US information firms with 250+ employees about 73% (early 2026)92; Korea ICT 56.0% (2024)93 Functions where agents are already scaled in the technology sector: software engineering 24%, IT 22%, service operations 21% (2025) — the highest anywhere, and still around one in five94 Software engineering and code generation; IT operations; knowledge management; customer interaction GitHub Copilot users completed 26% more pull requests (Cui et al. 2025); marketing teams increased multimodal ad output by 50% (Ju & Aral 2025); counter-evidence: METR found experienced open-source developers were 19% slower with AI, a result later work did not replicate94 A counter-intuitive learning penalty: engineers who leaned heavily on AI to learn new libraries showed no statistically significant speed gain (Shen & Tamkin
2025)94 Multiple sources; official statistics are enterprise-based, McKinsey is a self-selected executive sample
Financial services 75% of UK regulated financial firms already use AI and a further 10% plan to within three years (58% plus 14% in 2022; 118 responses)95; McKinsey basis, generative AI 63% (July 2024, n=193)89; Korea finance and insurance 63.1%93; Singapore finance and insurance 22.6%96 Sub-sector divergence: insurance 95% and international banks 94% already use AI; financial market infrastructure firms are lowest at 57%95 Risk and compliance (the only sector with high adoption in that function); fraud detection; customer service; marketing and sales (generative AI 29%) n.a. (the Bank of England and FCA survey publishes no sector-level financial returns) Regulation and model risk dominate; the 2024 survey added generative AI questions in response to its growth95 Regulator questionnaire, n=118; the UK ONS BICS sample excludes finance and insurance, so the two are not complementary
Professional and legal services Organisational use 22% in 2025, close to double the 12% of a year earlier (nearly 1,800 legal, tax, accounting, corporate risk and government professionals worldwide)97; McKinsey basis 68% (July 2024, n=179)89 Generative AI adoption in corporate tax departments rose to 75%, up 20 points in a year97 Document-intensive workflows; research and retrieval; advisory work; knowledge management (58%) Accountants increased throughput by 55% in weekly client support (Choi & Xie 2025)94 64% of professionals have had no training in the professional use of generative AI, and about one third of legal and risk professionals remain unsure whether their field should use it at all97 Vendor survey (Thomson Reuters sells legal AI), n≈1,800
89. McKinsey (PDF) — https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights /the%20state%20of%20ai/2025/the-state-of-ai-how-organizations-are-rewiring-to-capture-value_final.pdf
94. Stanford AI Index 2026, chapter 4 — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
90. Danish Agency for Digital Government, ITAV 2025 — https://digst.dk/media/5vkpv2uj/faktaark-virksomhedernes-anvendelse-af-ai-2023-2025-itav.pdf
95. Bank of England and FCA (2024) — https://www.bankofengland.co.uk/report/2024/artificial-intelligence-in-uk-financial-services-2024
91. Office for National Statistics (ONS) — https://www.ons.gov.uk/businessindustryandtrade/business/businessservices/artic les/artificialintelligenceinukbusinesses/2023to2026
96. IMDA report 2025 — https://www.imda.gov.sg/-/media/imda/files/about/resources/corporate-publications/annual-report/im da-sgde-report-fy2024-2025.pdf
92. PIIE analysis of BTOS by sector — https://www.piie.com/blogs/realtime-economics/2026/adoption-ai-industrial-sectors
97. Thomson Reuters, 2025 Generative AI in Professional Services Report — https://www.thomsonreuters.com/en/press-releases/ 2025/april/from-incubation-to-integration-generative-ai-adoption-nearly-doubles-as-professional-services-reach-crossroads
93. NIA compendium (via Etnews) — https://www.etnews.com/20260120000071
Chapter 3 — Sector comparison
Chapter 3 continued — Sector data table 2 of 4
Media and telecom, Manufacturing, Healthcare and life sciences
Table 12 Sector data table 2 of 4
# Sector AI / generative AI adoption (year) Production / scaling Most common use cases Quantified outcomes Main deployment barriers Source and sample notes
Media and telecom McKinsey basis, generative AI 65% (July 2024, n=77)98; NVIDIA's third annual telecom survey (450+ practitioners) reports that almost all respondents say their company is actively deploying or evaluating AI projects99 More than one third are investing or plan to invest in AI-RAN; 40% are deploying AI in network planning and operations99 Customer experience optimisation (44%); network planning and operations (40%); field operations optimisation (33%) 84% say AI helped raise annual revenue, of which 21% report revenue growth above 10% in specific areas; 77% say it reduced annual operating costs; 60% cite employee productivity as the largest benefit99 n.a. (the survey does not quantify barriers) Vendor survey; NVIDIA sells the related hardware, so these outcomes are not industry averages; n>450, self-selected Manufacturing AI/ML in use at facility or network scale 29% and generative AI deployed at the same scale 24%; piloting AI/ML 23% and piloting generative AI 38% (600 executives, revenue of $500m or more, more than 1,000 employees)100; official bases: Brazilian industry with 100+ employees 41.9% (2024)101; Danish industry 39%102; Singapore manufacturing 18.0%103 Production is the weakest link: the share of Brazilian industrial firms applying AI in production fell from 56.4% in 2022 to 52.0% in 2024 while management reached
87.9%101; on the McKinsey basis the manufacturing function shows only 7% generative AI use98 Predictive maintenance; quality inspection; digital twins and virtual commissioning; production scheduling; R&D and design Foxconn cut factory build and planning time by about 50% and accelerated CFD thermal simulation 150-fold104; on the Chinese smart-factory basis, R&D cycles −28.4% and production efficiency +22.3%105 An insufficient data and technology foundation: 78% of leaders direct more than 20% of their improvement budget to smart manufacturing to build it, and respondents rate their own maturity as barely at industry standard100 Deloitte n=600 (skewed to large firms); IBGE is an official survey of 10,167 enterprises
Healthcare and life sciences Individual use by physicians: 81% use AI in practice in 2026, more than double the 38% of 2023, with average use cases per physician rising from 1.1 to 2.3106; organisational level, McKinsey basis 79% (July 2024, n=113)98 Regulated products provide evidence of production deployment unique to this sector: the FDA maintains an AI-Enabled Medical Device List, stating these devices have met applicable premarket requirements while also noting the list "is not a comprehensive resource"107 Summarising medical research; clinical documentation; diagnostic support; software engineering (37%) More than three quarters of physicians say AI has improved their ability to care for patients (65% in
2023) and 70% see it as a way to automate burnout-inducing tasks106 Robust validation of safety and efficacy (88%) and data privacy (86%) are seen as decisive for wider adoption; 88% worry about skill atrophy106 The AMA survey is an annual industry-body survey (no sample size published); the FDA list is a regulatory register
98. McKinsey (PDF) — https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights /the%20state%20of%20ai/2025/the-state-of-ai-how-organizations-are-rewiring-to-capture-value_final.pdf
103. IMDA report 2025 — https://www.imda.gov.sg/-/media/imda/files/about/resources/corporate-publications/annual-report/i mda-sgde-report-fy2024-2025.pdf
99. NVIDIA, State of AI in Telecommunications 2025 — https://resources.nvidia.com/en-us-cross-industry-briefcase/ai-telcos-survey-2025
104. NVIDIA case study: Foxconn (Fii) — https://www.nvidia.com/en-us/case-studies/foxconn-develops-physical-ai-enabled-smart-factories-with-digital-twins/
100. Deloitte, 2025 Smart Manufacturing and Operations Survey — https://www.deloitte.com/us/en/about/press-room/deloitte-2025-smart-manufacturing-survey.html
105. CNNIC, 56th report — https://www.cnnic.net.cn/NMediaFile/2025/0730/MAIN1753846666507QEK67ZS9DH.pdf
101. IBGE, PINTEC Semestral — https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/44 551-de-2022-a-2024-percentual-de-empresas-industriais-utilizando-inteligencia-artificial-subiu-de-16-9-para-41-9
106. AMA, Physician Survey on Augmented Intelligence — https://www.ama-assn.org/press-center/ama-press-releases/ama-ai-us age-among-doctors-doubles-confidence-technology-grows
102. Danish Agency for Digital Government, ITAV 2025 — https://digst.dk/media/5vkpv2uj/faktaark-virksomhedernes-anvendelse-af-ai-2023-2025-itav.pdf
107. US FDA, AI-enabled medical devices — https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices
Chapter 3 — Sector comparison
Chapter 3 continued — Sector data table 3 of 4
Retail and consumer goods, Transport and logistics, Hospitality and travel
Table 13 Sector data table 3 of 4
# Sector AI / generative AI adoption (year) Production / scaling Most common use cases Quantified outcomes Main deployment barriers Source and sample notes
Retail and consumer goods McKinsey basis 79% (July 2024, n=111)108; official bases are markedly lower: Singapore retail
8.9%109 and Brazilian trade 17%110; US BTOS places retail, accommodation and food services among the laggards111 n.a. (the IBM–NRF study publishes no production or scaling share) Marketing and sales (51%, the sector's highest function); product search and recommendation; customer service; supply chain and inventory management Demand has already been reshaped: 45% of consumers use AI somewhere in the purchase journey, including researching products 41%, interpreting reviews 33% and finding deals 31%112 54% of brand executives report cross-channel, cross-system and data challenges112 The IBM–NRF executive sample is only 200; official statistics are more conservative
Transport and logistics 44% of shippers already use AI in transport planning and optimisation; 42% of carriers deploy AI in pricing and route optimisation and 39% for real-time tracking (230+ European and North American executives)113; official bases: Singapore transport and storage 10.3%109 and Brazilian transport 16%110 The report states explicitly that most companies remain at an early stage and provides no quantified split between pilots, production and scaling113 Transport planning and optimisation; rate and route optimisation; real-time visibility and ETA monitoring (52%); freight procurement n.a. (no quantified revenue or cost outcomes). On expectations: 86% of shippers expect AI to affect transport planning significantly within three to five years and 59% of carriers see pricing and route optimisation as the main source of value113 Limited trust in automation: two thirds of shippers and more than half of carriers still see AI as augmenting rather than replacing human decisions113 Vendor survey, n>230, Europe and North America
Hospitality and travel 41% of European hotels use AI technologies, 43% use none and 16% plan to adopt soon (1,485 hotels across Austria, France, Germany, Greece, Italy and Switzerland)114; official bases: Singapore accommodation and food 4.7%, the lowest of any sector109, and Brazil 10%110 Very shallow: 29% of adopters implemented within the past two years and only 4% have more than three years of experience114 The study lists application areas as operations, decision support and competitiveness (the full use-case ranking requires returning to the source) n.a. Structural barriers: 82% of the sample are independent hotels, 81% have fewer than 100 rooms and 49% employ fewer than 10 people114; in Korea the leading reason for non-adoption in accommodation and food is cost, at 49.0%115 Joint academic and industry-body survey, n=1,485, six countries only
108. McKinsey (PDF) — https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insight s/the%20state%20of%20ai/2025/the-state-of-ai-how-organizations-are-rewiring-to-capture-value_final.pdf
112. IBM Institute for Business Value and NRF, January 2026 — https://newsroom.ibm.com/2026-01-07-ibm-nrf-study-brands-and
-retailers-navigate-a-new-reality-as-ai-shapes-consumer-decisions-before-shopping-begins
109. IMDA report 2025 — https://www.imda.gov.sg/-/media/imda/files/about/resources/corporate-publications/annual-repo rt/imda-sgde-report-fy2024-2025.pdf
113. Trimble, Transportation Pulse Report 2026 — https://news.trimble.com/Transportation-Pulse-Report-2026-Transportation-I ndustry-at-AI-Inflection-Point-as-Adoption-Accelerates
110. Cetic.br, TIC Empresas 2025 — https://cetic.br/media/analises/TIC-Empresas-2025-lancamento.pdf
114. Schegg 2025, Artificial Intelligence in European Hotels — https://www.hotellerie.de/fileadmin/user_upload/Dokumente/Stu dien_und_Umfragen/Schegg_2025_AI_Adoption_in_European_Hotels_03072025.pdf
111. PIIE analysis of BTOS by sector — https://www.piie.com/blogs/realtime-economics/2026/adoption-ai-industrial-sectors
115. NIA compendium (via Etnews) — https://www.etnews.com/20260120000071
Chapter 3 — Sector comparison
Chapter 3 continued — Sector data table 4 of 4
Energy and utilities, Construction and real estate, Public sector and education
Table 14 Sector data table 4 of 4
# Sector AI / generative AI adoption (year) Production / scaling Most common use cases Quantified outcomes Main deployment barriers Source and sample notes
Energy and utilities North American utilities: 41% of executives report AI-related technology is now "fully integrated" and a further 40% have made major investments with mature projects under way (500 US and Canadian executives)116; McKinsey basis, energy and materials 59% (n=142, the lowest of any sector)117 As shown: 41% fully integrated versus only 27% of executives a year earlier who expected full integration within five years — reality ran ahead of expectations116 Grid optimisation (57%); safety and hazard identification (53%); demand forecasting (51%); predictive maintenance The IEA finds that widespread use of existing AI applications to optimise industrial processes could save energy equivalent to more than the total energy consumption of Mexico today, and improve forecasting and integration of variable renewables118 The IEA lists missing or inaccessible data and digital infrastructure, skills shortages and persistent digital and physical security concerns that "often outweigh potential efficiency gains"; only 2% of energy start-up equity funding goes to companies with an AI value proposition118 The IEA is an intergovernmental report with no adoption percentage; Itron is a vendor survey, n=500
Construction and real estate Construction: 45% of organisations report no AI use at all (more than 2,200 professionals worldwide)119; commercial real estate: 88% of investors and 92% of occupiers have launched AI pilots (1,500+ senior decision-makers, 16 markets)120; official bases: UK construction 13%121, Danish construction 24%122, Korean construction 15.9%123 The most extreme adoption-to-value gap in this white paper: only 1% of construction firms have scaled AI across projects119; in commercial real estate, piloting is near universal yet only 5% report achieving all their AI goals and 47% achieved two or three120 Design optioneering; scheduling; risk management; cost control; JLL identifies 56 AI use cases across the CRE value chain n.a. (RICS states it publishes no quantified revenue, cost, productivity, quality or risk outcomes). On expectations, nearly 70% of project managers and quantity surveyors believe AI will help deliver greater value119 Skills shortages and integration challenges: one quarter of firms plan to increase AI spending over the next 12 months, 28% have no plans and 22% are unsure119; more than 60% of property investors are still not ready120 RICS n>2,200; JLL n>1,500 across 16 markets; both are industry-body surveys
Public sector and education 67% of OECD countries use AI to improve the design and delivery of public services (2024)124; Saudi government entities about 39% use or are piloting AI (n=80)125; Korean education services
41.4% (2024)123 n.a. (the OECD states it provides no pilot, production or scaling shares and publishes neither the sample size nor the method behind the 67%)124 Process automation; resource allocation such as staff scheduling; decision support; citizen engagement; personalised service pathways Saudi Arabia: among entities using or piloting AI, 81% report significantly improved service delivery and 61% improved decision-making (n=80)125; the OECD publishes no quantified outcomes The OECD emphasises that how people perceive, trust and interact with AI-driven decisions will determine effectiveness and legitimacy, and notes that civil servants are "taking matters into their own hands" (shadow use)124 The OECD is an intergovernmental study (no sample size for the 67%); SDAIA n=80
116. Itron report, 27 October 2025 — https://www.globenewswire.com/news-release/2025/10/27/3174611/0/en/itron-reportreveals-81-of-north-american-utilities-already-use-ai.html
121. Office for National Statistics (ONS) — https://www.ons.gov.uk/businessindustryandtrade/business/businessservices/articles /artificialintelligenceinukbusinesses/2023to2026
117. McKinsey (PDF) — https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insight s/the%20state%20of%20ai/2025/the-state-of-ai-how-organizations-are-rewiring-to-capture-value_final.pdf
122. Danish Agency for Digital Government, ITAV 2025 — https://digst.dk/media/5vkpv2uj/faktaark-virksomhedernes-anvendelse-af-ai-2023-2025-itav.pdf
118. IEA, Energy and AI, executive summary — https://www.iea.org/reports/energy-and-ai/executive-summary
123. NIA compendium (via Etnews) — https://www.etnews.com/20260120000071
119. RICS, AI in Construction 2025 — https://www.rics.org/news-insights/optimism-high-for-ai-in-construction-but-skills-sho rtages-and-integration-challenges-adoption
124. OECD, Governing with Artificial Intelligence — https://www.oecd.org/en/publications/2025/06/governing-with-artificial-intel ligence_398fa287/full-report/ai-in-public-service-design-and-delivery_09704c1a.html
120. JLL, 28 October 2025 — https://www.jll.com/en-us/newsroom/real-estates-ai-reality-check-companies-piloting-only-achieved-all-ai-goals
125. SDAIA report — https://sdaia.gov.sa/en/MediaCenter/KnowledgeCenter/ResearchLibrary/StateofAIinSaudiArabia.pdf
Chapter 3 — Sector comparison
Chapter 3 continued — Four kinds of "use" that must be separated
Why 81% and 1% can both be true of the same sector
A single sector can report 81% and 1% without contradiction, because the two figures measure different things. Any sector conclusion must first state which layer it belongs to.
Table 15 Four kinds of use, compared across sectors
Layer Representative figure Meaning and caveats
Individual employee use 81% of physicians use AI in practice (2026)126 Individual behaviour; implies nothing about institutional process, validation or governance Shadow employee use 54% of employees say they use AI tools even without formal authorisation127 Shows tools are easy to obtain but not organised into products; also a governance risk Organisational adoption Organisational generative AI use in professional services is only 22%, although 89% of the same professionals see use cases128 Formally deployed by the institution, but not necessarily in core processes
Production and scaling Construction 1% scaled across projects; commercial real estate 5% achieved all AI goals; agentic systems scaled enterprise-wide 23%129130131 The only layer that can be connected to the profit and loss account, and the narrowest point in the chain
Sector conclusions
First, the ordering of sectors is consistent across official statistics and surveys and can be used to prioritise market entry. Second, absolute levels must never be quoted across measurement systems. Third, the sectors with the widest gap — construction and commercial real estate — are also the sectors with the most pilots, which shows that pilot counts are not a measure of capability.
126. AMA survey — https://www.ama-assn.org/press-center/ama-press-releases/ama-ai-usage-among-doctors-doubles-confidence-technology-grows
127. BCG, The AI Adoption Puzzle — https://www.bcg.com/publications/2025/ai-adoption-puzzle-why-usage-up-impact-not
128. Thomson Reuters (2025) — https://www.thomsonreuters.com/en/press-releases/2025/april/from-incubation-to-integration-generative-ai-adoption-nearly-doubles-as-profes sional-services-reach-crossroads
129. RICS (2025) — https://www.rics.org/news-insights/optimism-high-for-ai-in-construction-but-skills-shortages-and-integration-challenges-adoption
130. JLL (2025) — https://www.jll.com/en-us/newsroom/real-estates-ai-reality-check-companies-piloting-only-achieved-all-ai-goals
131. McKinsey, State of AI 2025 — https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
Chapter 3 — Sector comparison
Chapter 4 — The adoption-to-value gap
Five measures, five different bases
Figure 5 Five maturity measures on different bases; not one continuous conversion funnel
Finding: the evidence supports "diffusion outpaces value", but only at organisational level
This must not be restated as "AI does not work". The correct formulation is that personal exposure and tool-level use are close to mainstream while organisational workflow redesign and auditable financial returns remain a minority achievement. The gap exists inside a single survey: 88% use AI regularly, 62% are at least experimenting with agents, and only 23% have scaled agentic systems anywhere in the enterprise132. Fully scaled agent use is in single digits in almost every business function, and even in IT and knowledge management about two thirds or more of respondents report no use at all133.
A definitional discrepancy that must accompany any citation
The web version of the AI Index 2026 Economy chapter states that generative AI is now used in at least one business function by 70% of organisations, whereas figure 4.3.1 in the PDF of the same chapter gives 79% for generative AI in 2025 (and 88% for AI). This white paper cites the PDF values and flags the discrepancy rather than merging the two.
132. McKinsey, State of AI 2025 — https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
133. Stanford AI Index 2026, chapter 4 — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
Chapter 4 — Value gap
Chapter 4 continued — Value conversion table 1 of 3: adoption, scaling and self-reported gains
Value conversion table 1 of 3
Table 16 Value conversion table 1 of 3
Indicator Value Year Research body and sample Definition and basis
Organisations using AI regularly in at least one business function 88% (78% the previous year)134 McKinsey; 1,993 participants across 105 countries, fielded 25 June to 29 July 2025, weighted by each country's contribution to global GDP; 38% of respondents are at organisations with revenue above $1bn "Adoption" is explicitly left undefined and spans early experimentation by a few employees through to embedding across rewired business units135
Organisations using generative AI 79% (71% in 2024)136 As above (reproduced as AI Index figure 4.3.1) Regular use of generative AI in at least one business function Using AI in more than one function More than two thirds; three or more functions half As above
—
At least experimenting with agents 62%, of which 39% "have begun experimenting" As above
—
Agentic systems scaled somewhere in the enterprise 23% As above "Expanded deployment and adoption within at least one business function"; most report only one or two functions Agents "fully scaled" by function Single digits in almost every function; the highest in the technology sector are software engineering 24%, IT 22% and service operations 21% As above (AI Index figures 4.3.7 and 4.3.8) In most functions, most respondents report no agent use at all; even in IT and knowledge management about two thirds or more report none Executives describing their generative AI rollout as "mature" 1%135 McKinsey (developed-market supplementary survey) "Mature"
Reported improvement in organisational metrics (organisations using AI regularly) Innovation 64%, employee satisfaction 45%, customer satisfaction 45%, competitive differentiation 45%, cost 38%, profitability 36%, organic revenue growth 33%, talent attraction and retention 33%, market share 25% McKinsey (AI Index figure 4.3.5) Self-assessed as improved, no impact, worse or unknown; as many respondents report improvement as report no impact, and at most 7% say AI made cost metrics worse
EBIT impact Organisations investing $25m or more in responsible AI are "far more likely to realise material AI benefits, including EBIT impact above 5%"137 McKinsey, 2026 AI Trust Maturity Survey; about 500 organisations, December 2025 to January 2026 The page gives no share of respondents reporting EBIT impact, only this association
134. McKinsey, State of AI 2025 — https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
135. McKinsey (PDF) — https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/the%20state%20of%20ai/2025/the-state-of-ai-how-organizations-are-rewiring-to-capture-value_final.pdf
136. Stanford AI Index 2026, chapter 4 — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
137. McKinsey, 2026 AI Trust Maturity Survey — https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era
Chapter 4 — Value gap
Chapter 4 continued — Value conversion table 2 of 3: maturity distribution and pilot failure
Value conversion table 2 of 3
Table 17 Value conversion table 2 of 3
Indicator Value Year Research body and sample Definition and basis
Distribution of corporate AI maturity Future-built 5%, scaling 35%, and the remaining 60% obtain "almost no material value, reporting minimal revenue and cost benefits despite significant investment"138 BCG Build for the Future 2025, n=1,250 across 68 countries in Asia, Europe and North America The four levels are scored on 41 underlying capabilities (0–25 stalled, >25–50 emerging, >50–75 scaling, >75–100 future-built)139
Organisations with agents broadly embedded in workflows 13%140 n.a. (no year on the BCG page) BCG "Broadly integrated these agents into workflows"
Depth of employee adoption More than 85% remain at stage two (task assistance) and stage three (delegation); fewer than 10% reach stage four (semi-autonomous collaboration) or beyond n.a. BCG survey of software developers at three different organisations (no sample size published) Five stages: information assistance → task assistance → delegation → semi-autonomous collaboration → fully autonomous orchestration "Pilot failure" "For 95% of companies in the dataset, generative AI implementation fell short of expectations"; about 5% of AI pilots achieved rapid revenue acceleration141 MIT NANDA, The GenAI Divide, built on 150 leader interviews, 350 employee surveys and analysis of 300 public AI deployments "Failure" is operationalised as falling short of expectations, stalling, or having almost no measurable effect on the profit and loss account; the reporting page does not state which part of the research the 95% base comes from, nor its geographic scope Build versus buy Purchasing AI tools from specialist vendors and building partnerships succeeds "about 67% of the time"; internal builds succeed only one third as often As above The page gives no sample size for either route
Firm size and deployment stage "Larger companies are the most likely to report that their AI projects have reached the scaling stage" McKinsey (AI Index figure 4.3.6, five revenue bands) The figures cannot be aligned one-to-one with the bands; precise values require the original AI Index chart142
138. BCG, The Widening AI Value Gap — https://www.bcg.com/publications/2025/are-you-generating-value-from-ai-the-widening-gap
141. Fortune reporting on the MIT NANDA study — https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/
139. BCG (PDF, pages 19–20) — https://media-publications.bcg.com/The-Widening-AI-Value-Gap-Sept-2025.pdf
142. Stanford AI Index 2026, chapter 4 — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
140. BCG, The AI Adoption Puzzle — https://www.bcg.com/publications/2025/ai-adoption-puzzle-why-usage-up-impact-not
Chapter 4 — Value gap
Chapter 4 continued — Value conversion table 3 of 3: structural constraints and value pools
Value conversion table 3 of 3
Table 18 Value conversion table 3 of 3
Indicator Value Year Research body and sample Definition and basis
Governance capability and returns About two thirds of respondents cite security and risk concerns as the leading barrier to fully scaling agentic AI143 McKinsey AI Trust Maturity Survey, about 500 organisations
—
Data and digital foundation and value 78% of manufacturing leaders direct more than 20% of their improvement budget to smart manufacturing to build the data and technology foundation144 Fielded Aug ust–Septem ber 2024 Deloitte, n=600
—
Insufficient investment and value More than 95% of Indian organisations allocate less than 20% of the IT budget to AI and only 4% cross the 20% threshold145 EY–CII, n=200, C-suite and senior executives
—
Distribution of value pools 70% of AI value potential sits in core business functions (62% in 2024); the IT share rose 6 percentage points to 13%; R&D and innovation alone account for 15%146 BCG Build for the Future 2025, n=1,250 This is the distribution of value potential, not adoption by function
Tool availability and maturity In less mature industries fewer than 50% of employees have access to generative AI tools such as Copilot or ChatGPT; in more mature industries more than 70% do146 As above
—
Firm size and breadth of generative AI Companies with revenue above $500m use generative AI more broadly across the organisation McKinsey147
—
143. McKinsey, 2026 AI Trust Maturity Survey — https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forwa rd/state-of-ai-trust-in-2026-shifting-to-the-agentic-era
146. BCG (PDF, pages 6–7) — https://media-publications.bcg.com/The-Widening-AI-Value-Gap-Sept-2025.pdf
144. Deloitte (2025) — https://www.deloitte.com/us/en/about/press-room/deloitte-2025-smart-manufacturing-survey.html
147. McKinsey (PDF) — https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/th e%20state%20of%20ai/2025/the-state-of-ai-how-organizations-are-rewiring-to-capture-value_final.pdf
145. EY–CII, November 2025 — https://www.ey.com/en_in/newsroom/2025/11/india-s-ai-shift-from-pilots-to-performance-4 7-percent-of-enterprises-have-multiple-ai-use-cases-live-in-production-ey-cii-report
Chapter 4 — Value gap
Chapter 4 continued — Productivity evidence: seven task-level studies that cannot be averaged
Results range from −19% to +200% for comparable work
Table 19 Task-level productivity studies compiled in AI Index 2026, figure 4.4.27148 Study Occupation Application Change in productivity Who benefits most
Reimers & Waldfogel (2026) Software engineers LLM-generated content +200% in output volume; publications tripled New entrants drive volume; pre-AI authors maintain quality Brynjolfsson et al. (2025) Customer support agents Conversational assistant +14% to +15% in issues resolved per hour Less experienced or lower-skilled agents, +30% to +35% Cui et al. (2025) Software developers GitHub Copilot +26% in pull requests completed Junior and less experienced developers Ju & Aral (2025) Marketing teams Multimodal ad creation +50% in output per person Human–machine teams Choi & Xie (2025) Accountants AI bookkeeping +55% in weekly client-support throughput Experienced accountants using AI confidence scores to target review Shen & Tamkin (2025) Developers Learning new libraries 0%; the change in speed is not statistically significant High scorers who use AI for conceptual enquiry and avoid the learning penalty Becker et al. (2025) Developers Open-source tooling −19%; slower when using AI None; "a significant gap between perceived help and actual performance"
Macroeconomic evidence is equally dispersed
— Aldasoro et al. (2026), 12,000 European firms, 2019–2024: labour productivity +4%, with each 1% of spending on training adding 5.9 percentage points of gain.
— Yotzov et al. (2026), 6,000 executives in the United States, United Kingdom, Germany and Australia: expected productivity +1.4%, output +0.8% and employment −0.7% over three years; the AI Index summarises this as broad adoption with minimal realised productivity gains.
— Brynjolfsson (2026): US productivity grew 2.7% in 2025, close to double the 1.4% annual average of the preceding decade.
— Filippucci et al. (OECD, 2025), a ten-year horizon for the G7: annual labour productivity growth +0.4 to +1.3 percentage points for the United States and United Kingdom, and +0.2 to +0.8 for Italy and Japan.
— Penn Wharton Budget Model (2025): a contribution to total factor productivity of +0.01 percentage points, described as negligible.
— Brynjolfsson et al. (2025), US ADP payroll data to 2025: employment of early-career workers −15% to −16%. The AI Index's own qualitative judgement is that the evidence is "neither settled nor uniformly positive", with smaller gains on tasks requiring deeper reasoning148.
148. Stanford AI Index 2026, figure 4.4.27 — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
Chapter 4 — Value gap
Chapter 4 continued — Five datasets behind the core finding, and the caveats
What supports the conclusion, and what must be said alongside it
1. The gap inside a single survey. 88% of organisations use AI regularly, but only 23% have scaled agentic systems anywhere in the enterprise149, and fully scaled agent use is in single digits in almost every function150.
2. Self-reported financial outcomes run well below adoption. Only 36% see improved profitability and 33% improved organic revenue growth, and as many respondents report improvement as report no impact150.
3. An independent second source reaches the same conclusion. BCG (n=1,250, 68 countries) finds 60% of companies obtain almost no material value and only 5% are future-built151.
4. Extreme sector cases. Commercial real estate pilots at 88% to 92%, yet only 5% achieve all their AI goals152, and only 1% of construction firms scale across projects153.
5. The mismatch between individual and organisational use. French generative AI diffusion reached 44.0% of the population in H2 2025 against official enterprise adoption of 18%; US population-level diffusion of 28.3% compares with BTOS enterprise adoption of about 18% to 19.8%150154155.
The caveat that must always accompany the "95% failure rate"
The MIT NANDA figure of 95% is widely quoted as "95% of AI projects fail". The original wording is that "for 95% of companies in the dataset, generative AI implementation fell short of expectations", where failure is operationalised as stalling or having almost no measurable effect on the profit and loss account. The research rests on 150 interviews, 350 employee surveys and analysis of 300 public deployments; it is not a probability sample of the enterprise population and its geographic scope is not published156. This white paper requires all three qualifications to be stated whenever the number is used.
"Personal exposure and tool-level usage are close to mainstream. Organisational workflow redesign and auditable financial returns remain a minority achievement. The gap does not lie in model capability but in depth of deployment, the data and governance foundation, and process change that can be booked in the profit and loss account."
The finding of chapter 4; quotable in full.
149. McKinsey, State of AI 2025 — https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
150. Stanford AI Index 2026, chapter 4 — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
151. BCG, The Widening AI Value Gap — https://www.bcg.com/publications/2025/are-you-generating-value-from-ai-the-widening-gap
152. JLL (2025) — https://www.jll.com/en-us/newsroom/real-estates-ai-reality-check-companies-piloting-only-achieved-all-ai-goals
153. RICS (2025) — https://www.rics.org/news-insights/optimism-high-for-ai-in-construction-but-skills-shortages-and-integration-challenges-adoption
154. INSEE Premiere no. 2120 — https://www.insee.fr/fr/statistiques/9025878
155. US Census Bureau, BTOS — https://www.census.gov/library/stories/2026/05/ai-use-businesses.html
156. Fortune reporting on the MIT NANDA study — https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/
Chapter 4 — Value gap
Chapter 5 — Physical AI and hardware integration, part 1
Dated first-hand statements and the division of labour between platforms
This chapter uses only dated, verifiable first-hand statements and deployed cases. It contains no market forecasts.
Table 20 NVIDIA's strategic statements, verbatim and dated
Date Source Statement (extract)
16 Mar 2026 NVIDIA Newsroom "Physical AI has arrived — every industrial company will become a robotics company." NVIDIA's full-stack platform, spanning computing, open models and software frameworks, is described as the foundation for the robotics industry157 5 Jan 2026 NVIDIA Newsroom (CES) "The ChatGPT moment for robotics is here." "Breakthroughs in physical AI — models that understand the real world, reason and plan actions — are unlocking entirely new applications"158 March 2026 (GTC) Data Center Frontier, on-site report "We have digital agents. Now we have physically embodied agents. We call them robots."; "Tokens are the new commodity. AI factories are the infrastructure that produces them."; "If you have the wrong architecture, even if it's free, it's not cheap enough."159 18 Mar 2025 NVIDIA Newsroom (Rev Lebaredian, VP of Omniverse) "Omniverse is an operating system that connects the world's physical data to the realm of physical AI."160
Why this is more than a marketing narrative
Four robot manufacturers — FANUC, ABB Robotics, YASKAWA and KUKA, with more than two million robots installed worldwide — have integrated Omniverse libraries and the Isaac simulation framework into their virtual commissioning offerings and are embedding Jetson modules in controllers for real-time inference on the line157. That is a change to the production process chain, verifiable at the enterprise level, rather than a roadmap commitment.
157. NVIDIA Newsroom, 16 March 2026 — https://nvidianews.nvidia.com/news/nvidia-and-global-robotics-leaders-take-physical-ai-to-the-real-world
158. NVIDIA Newsroom, 5 January 2026 — https://nvidianews.nvidia.com/news/nvidia-releases-new-physical-ai-models-as-global-partners-unveil-next-generation-robots
159. Data Center Frontier, NVIDIA GTC 2026 — https://www.datacenterfrontier.com/machine-learning/news/55364406/jensen-huang-maps-the-ai-factory-era-at-nvidia-gtc-2026
160. NVIDIA Newsroom, 18 March 2025 — https://nvidianews.nvidia.com/news/nvidia-omniverse-physical-ai-operating-system-expands-to-more-industries-and-partners
Chapter 5 — Physical AI
Chapter 5 continued — Division of labour: from simulation to inference at the edge
What each platform does, and the quantified claims attached to it
Table 21 Division of labour across the physical AI stack
Platform Role (verbatim or near-verbatim) Key quantified facts
Omniverse "An operating system built on the OpenUSD framework that lets developers unify physical-world data and applications"; at GTC 2026 NVIDIA introduced the Omniverse DSX blueprint, allowing operators to create a physically accurate digital twin of an AI factory — layout, power distribution, cooling, network fabric and load behaviour — before construction begins
—161162
Isaac / Isaac Sim Used "to create and train robots in simulation before deploying them in the real world"; Foxconn uses Isaac Sim for "simulation-driven design and evaluation ahead of physical deployment", simulating tasks such as screw driving and cable insertion
—163
Isaac GR00T A general-purpose robot foundation model. GR00T N2 previewed in March 2026, with availability planned for late 2026 "Helps robots complete new tasks in new environments with more than twice the success rate of leading vision-language-action models" and currently ranks first on the MolmoSpaces and RoboArena generalist robot policy leaderboards; the CES 2026 release was GR00T N1.6164165 Cosmos A world foundation model. Cosmos 3 was announced in March 2026 as "the first world foundation model to unify synthetic world generation and vision" The CES 2026 release comprised Cosmos Transfer 2.5, Cosmos Predict 2.5 and Cosmos Reason 2165
Jetson / Jetson Thor Robotics compute at the edge; "built for generative reasoning models", letting physical AI agents "run in real time at the edge while minimising reliance on the cloud"
7.5x the AI compute, 3.1x the CPU performance and 2x the memory of the previous Jetson Orin; the Jetson T5000 module starts at $2,999 per 1,000 units and the developer kit at $3,499166
Jetson T4000 / IGX Thor Blackwell-architecture edge modules launched in January 2026; IGX Thor "extends robotics to the industrial edge with high-performance AI compute, enterprise software support and functional safety" $1,999 per 1,000 units, 4x the performance of the previous generation, 1,200 FP4 TFLOPS, 64GB of memory, a configurable 70W power envelope, and a 4x improvement in energy efficiency and compute165
Mega / Metropolis Mega is "an Omniverse blueprint for testing multi-robot fleets at scale in industrial digital twins"; Metropolis underpins the video search and summarisation blueprint used to build agents that monitor activity across a facility
—161
Nemotron / PhysicsNeMo Nemotron 3 is "a family of open models, data and libraries to support transparent, efficient and specialised agentic AI development across industries"; PhysicsNeMo is a physics-informed machine learning framework Foxconn used PhysicsNeMo to accelerate CFD simulation 150-fold against conventional methods167163
161. NVIDIA, 18 March 2025 — https://nvidianews.nvidia.com/news/nvidia-omniverse-physical-ai-operating-system-expandsto-more-industries-and-partners
165. NVIDIA, 5 January 2026 — https://nvidianews.nvidia.com/news/nvidia-releases-new-physical-ai-models-as-global-partners-unveil-next-generation-robots
162. Data Center Frontier, GTC 2026 — https://www.datacenterfrontier.com/machine-learning/news/55364406/jensen-huan g-maps-the-ai-factory-era-at-nvidia-gtc-2026
166. NVIDIA blog: Jetson Thor and physical AI at the edge — https://blogs.nvidia.com/blog/jetson-thor-physical-ai-edge/
163. NVIDIA case study: Foxconn (Fii) — https://www.nvidia.com/en-us/case-studies/foxconn-develops-physical-ai-enabled-smart-factories-with-digital-twins/
167. NVIDIA, 15 December 2025: Nemotron 3 — https://nvidianews.nvidia.com/news/nvidia-debuts-nemotron-3-family-of-open-models
164. NVIDIA, 16 March 2026 — https://nvidianews.nvidia.com/news/nvidia-and-global-robotics-leaders-take-physical-ai-to-the-real-world
Chapter 5 — Physical AI
Chapter 5 continued — Deployed enterprise and factory cases, not forecasts
Verifiable deployments and their quantified outcomes
Table 22 Deployed physical AI cases
Company What was deployed Quantified outcome Source
FANUC, ABB Robotics, YASKAWA, KUKA More than two million robots installed worldwide; all four are integrating Omniverse libraries and the Isaac simulation framework into their virtual commissioning offerings "to develop and validate complex robotic applications and entire production lines through physically accurate digital twins", and are embedding Jetson modules in controllers for real-time inference on the line 2 million robots installed NVIDIA, 16 March 2026168
Foxconn (Fii) The Fii Omniverse Digital Twin platform builds virtual replicas of factories using Omniverse libraries and OpenUSD; Isaac Sim supports simulation-driven design and evaluation; PhysicsNeMo handles thermal analysis; standardised USD assets allow entire lines to be assembled virtually and replicated across sites, for example from Taiwan to Mexico; the new Houston plant validated every mechanical, electrical and piping system virtually before construction Leo Guo, general manager of Fii's robotics group: "We believe we can reduce [time to market, factory build and factory planning] by about 50%"; CFD thermal simulation 150x faster (minutes rather than hours); the FoxBrain large model was trained in four weeks NVIDIA case study169; NVIDIA, 18 March 2025170
General Motors "The largest US automaker, announced adoption of Omniverse to enhance its factories and train platforms for material handling, transport and precision welding"
— NVIDIA, 18 March 2025170
SAP and Unilever SAP customers and partners can use Omniverse to develop their own virtual environments for warehouse management; Unilever adopted Omniverse and physically accurate digital twins to streamline and optimise marketing content creation for its products
— NVIDIA, 18 March 2025170
Agility Robotics and Boston Dynamics Agility has integrated Jetson into the fifth generation of Digit and plans to use Jetson Thor as the onboard compute platform for the sixth; Boston Dynamics is integrating Jetson Thor into the Atlas humanoid
— NVIDIA blog171
Salesforce Agents applied to incident resolution Incident resolution time halved NVIDIA, 5 January 2026172 Automotive and autonomous driving NVIDIA announced a further group of automotive partnerships including BYD, Hyundai, Nissan and Geely, alongside existing work with Mercedes-Benz and Toyota The company states its platform will support "autonomous systems in tens of millions of vehicles a year" — a company claim, not independently verified Data Center Frontier173
168. NVIDIA Newsroom, 16 March 2026 — https://nvidianews.nvidia.com/news/nvidia-and-global-robotics-leaders-take-physical-ai-to-the-real-world
171. NVIDIA blog: Jetson Thor — https://blogs.nvidia.com/blog/jetson-thor-physical-ai-edge/
169. NVIDIA case study: Foxconn (Fii) — https://www.nvidia.com/en-us/case-studies/foxconn-develops-physical-ai-enabled-smart-factories-with-digital-twins/
172. NVIDIA, 5 January 2026 — https://nvidianews.nvidia.com/news/nvidia-releases-new-physical-ai-models-as-global-partners-unveil-next-generation-robots
170. NVIDIA, 18 March 2025 — https://nvidianews.nvidia.com/news/nvidia-omniverse-physical-ai-operating-system-expandsto-more-industries-and-partners
173. Data Center Frontier, GTC 2026 — https://www.datacenterfrontier.com/machine-learning/news/55364406/jensen-huang-ma ps-the-ai-factory-era-at-nvidia-gtc-2026
Chapter 5 — Physical AI
Chapter 5 continued — Global industrial robot data (IFR World Robotics 2025, reference year
Installations, operational stock, shares and density
Table 23 Installations and operational stock174175 Indicator Value
Global installations, 2024 542,076 units ("542,000" in the press release), the second-highest year on record and only 2% below the peak two years earlier; the fourth consecutive year above 500,000 Historical series 500,000 first passed in 2021; 552,946 units in 2022 (the record); 541,302 units in 2023 Global operational stock, 4,664,000 units, up 9% year on year
Regional shares, 2024 Asia 74% of new installations (401,665 units, +5%); Europe 16% (85,006 units, −8%); the Americas 9% (50,077 units, −10%) EU-27, 2024 67,819 units (−8%), or 80% of European installations 2025 forecast Global +6% to 575,000 units; Asia about 435,000; Europe below 79,000; North America about 43,500 2028 forecast Above 700,000 units
Table 24 Major countries, 2024 (density in units per 10,000 employees)176 Country 2024 installs Change Share Density
China 295,045 +7% 54% Japan 44,453 −4% 8% United States 34,164 −9% 6% Korea 30,596 −3% 6% 1,220 Germany 26,982 −5% 5% Singapore n.a.
—
—
India 9,100 +7%
— n.a.
Italy 8,783 −16%
— top 20
Mexico 5,594 −4%
—
Spain 5,086 +1%
— top 20
France 4,900 −24%
— top 20
Canada 3,787 −12%
—
United Kingdom 2,500 −35%
—
China's operational stock exceeds two million units, the largest in the world and roughly 4.5 times that of Japan, whose stock stands at 450,500 units (+3%). The UK density figure of 112 is on a Make UK basis and trails the EU average of 208. China, Japan, the United States, Korea and Germany together account for 80% (431,240 units) of global installations in 2024, and the domestic market share held by Chinese manufacturers "climbed to 57% last year, against roughly 28% over the past decade"174175.
174. IFR press release, 25 September 2025 — https://ifr.org/ifr-press-releases/news/global-robot-demand-in-factories-doubles-over-10-years
175. IFR executive summary (PDF) — https://ifr.org/img/worldrobotics/Executive_Summary_WR_2025_Industrial_Robots.pdf
176. IFR density data (via The Robot Report) — https://www.therobotreport.com/ifr-reports-robot-density-increase-across-europe-asia-americas/
Chapter 5 — Physical AI
Chapter 5 continued — Installations and density must be read separately
The installation leader and the density leader are different countries
Figure 6 Industrial robot installations and global share by country, 2024
Figure 7 Robot density in units per 10,000 employees, 2024
Sources for Figures 6 and 7: IFR World Robotics 2025 press release and executive summary for installations, stock and shares177178, and IFR robot density data for the 2024 reference year as published by The Robot Report179. The two charts must not be merged: installations measure the annual addition, density measures the ratio of the installed stock to the workforce.
177. IFR press release, 25 September 2025 — https://ifr.org/ifr-press-releases/news/global-robot-demand-in-factories-doubles-over-10-years
178. IFR executive summary (PDF) — https://ifr.org/img/worldrobotics/Executive_Summary_WR_2025_Industrial_Robots.pdf
179. IFR density data (via The Robot Report) — https://www.therobotreport.com/ifr-reports-robot-density-increase-across-europe-asia-americas/
Chapter 5 — Physical AI
Chapter 5 continued — Three structural facts that must be presented together
Concentration, localisation and the difference between volume and density
1. Concentration in five markets. China, Japan, the United States, Korea and Germany together accounted for 80% (431,240 units) of global installations in 2024180.
2. Localisation in China. The domestic market share of Chinese manufacturers "climbed to 57% last year, against roughly 28% over the past decade"181.
3. Density and installations must be read separately. China leads installations (54%) but has a density of only 166, ranking 22nd; Korea ranks fourth on installations but first on density at 1,220. Global average density reached 132 units per 10,000 employees in 2024, having doubled between 2014 and 2024: EU-27 231, western Europe 267 (+3%), North America 204 (+4%) and Asia 131 (+11%)182. "The robot density metric provides a common basis for comparison by relating the total number of robots in use in a country to the size of its economy as measured by its workforce."
Takayuki Ito, President of the IFR182
Set against enterprise-level penetration
The other side of the picture must be shown at the same time: among enterprises that use AI, technologies for autonomous machine movement remain in single digits — 6% in Germany183, 2% in Denmark for two consecutive years and the only category with no growth184, and 7% in Brazil185. Robot density measures the automation stock; it is not a measure of AI
penetration.
The asymmetry between capital signals and installed penetration
Between January and April 2025 China recorded 140 robotics-related financing deals, 38.7% of all AI financing events, while enterprise use of autonomous machine movement remains at 2% to 7%. Capital is entering faster than the technology is penetrating production lines, and this is the structural feature investors in physical AI most need to understand.
180. IFR executive summary (PDF) — https://ifr.org/img/worldrobotics/Executive_Summary_WR_2025_Industrial_Robots.pdf
181. IFR press release, 25 September 2025 — https://ifr.org/ifr-press-releases/news/global-robot-demand-in-factories-doubles-over-10-years
182. IFR density data (via The Robot Report) — https://www.therobotreport.com/ifr-reports-robot-density-increase-across-europe-asia-americas/
183. Destatis — https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Unternehmen/IKT-in-Unternehmen-IKT-Branche/Tabellen/ikti-unternehmen-kuenstliche-intelligenz.html
184. Statistics Denmark (Danmarks Statistik) — https://www.dst.dk/nytpdf/55352
185. Cetic.br (2025) — https://cetic.br/media/analises/TIC-Empresas-2025-lancamento.pdf
Chapter 5 — Physical AI
Chapter 6 — Product thresholds for industrial-grade AI, part 1
Why there is no universal "90% accuracy" bar
No authoritative body was found to set a uniform cross-industry accuracy threshold. Any claim that "90% is enough to go live" has no source behind it. The evidence shows thresholds are set by the consequence of the task, across six separate dimensions.
Figure 8 A six-threshold framework for industrial-grade AI
First-hand basis for each threshold in Figure 8: the FDA list of AI-enabled devices, where the route is regulatory review rather than a percentage186; CAICT data on service success rate, TPS and time to first token187; NVIDIA Jetson T4000 and T5000 pricing, power envelope and functional safety188189; the GR00T N2 task success claim190; the human-in-the-loop preference in logistics191; the link between responsible-AI investment and EBIT impact192; and physicians' views on validation and skill atrophy193.
186. US FDA, AI-enabled medical device list — https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices
187. CAICT, AI Industry Development Research Report 2025 — https://www.caict.ac.cn/kxyj/qwfb/bps/202602/P020260202487301304903.pdf
188. NVIDIA, 5 January 2026 — https://nvidianews.nvidia.com/news/nvidia-releases-new-physical-ai-models-as-global-partners-unveil-next-generation-robots
189. NVIDIA blog: Jetson Thor — https://blogs.nvidia.com/blog/jetson-thor-physical-ai-edge/
190. NVIDIA, 16 March 2026 — https://nvidianews.nvidia.com/news/nvidia-and-global-robotics-leaders-take-physical-ai-to-the-real-world
191. Trimble (2026) — https://news.trimble.com/Transportation-Pulse-Report-2026-Transportation-Industry-at-AI-Inflection-Point-as-Adoption-Accelerates
192. McKinsey, State of AI trust in 2026 — https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era
193. AMA survey — https://www.ama-assn.org/press-center/ama-press-releases/ama-ai-usage-among-doctors-doubles-confidence-technology-grows
Chapter 6 — Thresholds
Chapter 6 continued — First-hand evidence for each threshold dimension
Statements and metrics, not generalities
Table 25 Threshold dimensions for industrial and edge AI, with first-hand evidence
Threshold dimension First-hand evidence
Determinism and real-time performance Jetson Thor is positioned to bring "high-speed sensor data processing and visual reasoning to the edge — workflows that were previously too slow to run in dynamic real-world environments", with "the low latency and high performance real-world applications require"194 Operation without connectivity Jetson Thor lets next-generation physical AI agents "run in real time at the edge while minimising reliance on the cloud"194 Functional safety certification NVIDIA IGX Thor "extends robotics to the industrial edge with high-performance AI compute, enterprise software support and functional safety"195 Unit cost and energy (a bill-of-materials constraint) Jetson T4000: $1,999 per 1,000 units, 1,200 FP4 TFLOPS, 64GB of memory and a configurable 70W envelope, positioned as a cost-effective upgrade path195; against Jetson T5000 at $2,999 per 1,000 units and a developer kit at $3,499194. The economics of edge deployment are set by component cost and power envelope, not by benchmark scores. Architectural efficiency over "free" Jensen Huang at GTC 2026: "If you have the wrong architecture, even if it's free, it's not cheap enough."196 Pre-deployment validation instead of trial and error The four major robot manufacturers use Omniverse and Isaac "to develop and validate complex robotic applications and entire production lines through physically accurate digital twins"; Foxconn assembles and validates every mechanical, electrical and piping system virtually before construction begins197198 Reliability as an engineered metric CAICT reports that across 11 Chinese MaaS platforms the average call success rate for DeepSeek-R1 rose from 87.01% in February 2025 to 99.36% in September, TPS from 17.86 to 26.76, and time to first token fell from 3.07 seconds to 1.02 seconds199 The industrial significance of energy The IEA states that "widespread adoption of existing AI applications to optimise industrial processes could deliver energy savings equivalent to more than the total energy consumption of Mexico today", while noting that missing or inaccessible data and digital infrastructure, skills shortages and persistent digital and physical security concerns "often outweigh potential efficiency gains"200 Human-in-the-loop rather than full autonomy In transport and logistics, two thirds of shippers and more than half of carriers still see AI as augmenting rather than replacing human decisions, and most prefer human-in-the-loop designs201 Validation thresholds in healthcare 88% of physicians regard robust validation of safety and efficacy, and 86% data privacy, as decisive for wider adoption202; the FDA's AI device list uses satisfaction of applicable premarket requirements, including focused review of overall safety and effectiveness, as the entry standard203
194. NVIDIA blog: Jetson Thor — https://blogs.nvidia.com/blog/jetson-thor-physical-ai-edge/
195. NVIDIA, 5 January 2026 — https://nvidianews.nvidia.com/news/nvidia-releases-new-physical-ai-models-as-global-partners-unveil-next-generation-robots
196. Data Center Frontier, GTC 2026 — https://www.datacenterfrontier.com/machine-learning/news/55364406/jensen-huang-maps-the-ai-factory-era-at-nvidia-gtc-2026
197. NVIDIA, 16 March 2026 — https://nvidianews.nvidia.com/news/nvidia-and-global-robotics-leaders-take-physical-ai-to-the-real-world
198. NVIDIA case study: Foxconn — https://www.nvidia.com/en-us/case-studies/foxconn-develops-physical-ai-enabled-smart-factories-with-digital-twins/
199. CAICT, AI Industry Development Research Report 2025 — https://www.caict.ac.cn/kxyj/qwfb/bps/202602/P020260202487301304903.pdf
200. IEA, Energy and AI, executive summary — https://www.iea.org/reports/energy-and-ai/executive-summary
201. Trimble (2026) — https://news.trimble.com/Transportation-Pulse-Report-2026-Transportation-Industry-at-AI-Inflection-Point-as-Adoption-Accelerates
202. AMA survey — https://www.ama-assn.org/press-center/ama-press-releases/ama-ai-usage-among-doctors-doubles-confidence-technology-grows
203. US FDA, AI-enabled medical devices — https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices
Chapter 6 — Thresholds
Chapter 6 continued — A threshold checklist for procurement and contracts
How to set each threshold, with citable evidence
Table 26 Product threshold checklist for industrial-grade AI
Threshold How to set it, with citable first-hand evidence
Task accuracy Tier by consequence; set no single value. The medical-device route is regulatory review, not a percentage204; robotics is measured by success rate: GR00T N2 achieves more than twice the success rate of leading VLA models on new tasks in new environments — a relative multiple, not an absolute accuracy205 Repeatability and determinism Use the service call success rate as the operable metric: an average of 99.36% across 11 MaaS platforms in September 2025206 Exception handling and human oversight Logistics evidence favours human-in-the-loop: two thirds of shippers and more than half of carriers see AI as augmenting rather than replacing decisions207 SLA and latency Write TTFT and TPS into the contract: time to first token 1.02 seconds and TPS 26.76 (11-platform average, September
2025)206; the edge requirement is "low latency and high performance" for dynamic real-world environments208
Audit and governance Directly related to returns: organisations investing $25m or more in responsible AI are more likely to report EBIT impact above 5%, and nearly two thirds cite security and risk as the leading barrier to scaling agents209; financial services are monitored continuously by the Bank of England and FCA, whose 2024 survey drew 118 responses210 Unit cost Edge bill-of-materials cost is a hard constraint: Jetson T4000 at $1,999 per 1,000 units against T5000 at $2,999211. On the inference side, 12 billion of 120 billion parameters are activated, and NVFP4 runs up to 4x faster than FP8 on Hopper with no loss of accuracy212 Energy A configurable 70W envelope and a 4x improvement in energy efficiency (Jetson T4000)211; for sector-level savings potential see the IEA (equivalent to more than Mexico's national energy consumption)213 Operation without connectivity An explicit product requirement: "run in real time at the edge while minimising reliance on the cloud"208 Safety certification Functional safety is an entry requirement at the industrial edge (NVIDIA IGX Thor)211; the energy sector cites digital and physical security concerns as barriers that often outweigh efficiency gains213 Lifecycle maintenance "With NVIDIA CUDA ecosystem support across the full lifecycle, Jetson Thor is expected to deliver better throughput and faster response through future software releases"208; the long-run risk in healthcare is skill atrophy, a concern for 88% of physicians214
204. US FDA, AI-enabled medical devices — https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices
205. NVIDIA, 16 March 2026 — https://nvidianews.nvidia.com/news/nvidia-and-global-robotics-leaders-take-physical-ai-to-the-real-world
206. CAICT, 2025 report — https://www.caict.ac.cn/kxyj/qwfb/bps/202602/P020260202487301304903.pdf
207. Trimble (2026) — https://news.trimble.com/Transportation-Pulse-Report-2026-Transportation-Industry-at-AI-Inflection-Point-as-Adoption-Accelerates
208. NVIDIA blog: Jetson Thor — https://blogs.nvidia.com/blog/jetson-thor-physical-ai-edge/
209. McKinsey, 2026 AI Trust Maturity Survey — https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era
210. Bank of England and FCA (2024) — https://www.bankofengland.co.uk/report/2024/artificial-intelligence-in-uk-financial-services-2024
211. NVIDIA, 5 January 2026 — https://nvidianews.nvidia.com/news/nvidia-releases-new-physical-ai-models-as-global-partners-unveil-next-generation-robots
212. NVIDIA blog: Nemotron 3 Super — https://blogs.nvidia.com/blog/nemotron-3-super-agentic-ai/
213. IEA executive summary — https://www.iea.org/reports/energy-and-ai/executive-summary
214. AMA survey — https://www.ama-assn.org/press-center/ama-press-releases/ama-ai-usage-among-doctors-doubles-confidence-technology-grows
Chapter 6 — Thresholds
Chapter 6 continued — The engineering turn: not the strongest model, but the more reliable one
Three candidates, ranked by strength of evidence
Industry frequently asks which recent product represents the shift towards precision, low error rates, cost and speed rather than frontier capability. The available evidence does not point uniquely to one product, but three candidates can be ranked by strength of evidence.
Table 27 Candidate identification and assessment
Candidate Official positioning (verbatim or near-verbatim) Assessment
Candidate 1: NVIDIA Nemotron 3 (Nano / Super / Ultra), released 15 December 2025 Announced as "the most efficient family of open models, with leading accuracy" for building agentic AI applications, introducing a "hybrid latent mixture-of-experts architecture" that helps developers build and deploy reliable multi-agent systems; Jensen Huang emphasised "transparency and efficiency"; the ServiceNow CEO described "unmatched efficiency, speed and accuracy"215. Quantified claims for Nemotron 3 Super: 120 billion parameters with only 12 billion activated at inference; a 1 million token context window; up to 5x higher throughput and up to 2x higher accuracy than the previous generation; Mamba layers giving 4x memory and compute efficiency; multi-token prediction giving 3x faster inference; and NVFP4 running up to 4x faster than FP8 on Hopper with no loss of accuracy216 The strongest evidence. The description matches leading accuracy for its size class combined with top-ranked efficiency and cost, throughput and latency as the core selling points. Note that NVIDIA also describes Nemotron 3 Ultra as "America's smartest open-weights AI model"; that wording appears in third-party reproductions and could not be verified on an official page, so it is not treated as evidence
Candidate 2: Isaac GR00T N2 (previewed March 2026) The official claim concerns task success rate rather than general capability: it "helps robots complete new tasks in new environments with more than twice the success rate of leading vision-language-action models" and ranks first on the MolmoSpaces and RoboArena leaderboards217 A partial match: success rates and real physical tasks are emphasised. Against: it is claimed to rank first on its own leaderboards, which does not fit the description of not being the most advanced Candidate 3: Jetson T4000 and IGX Thor (January 2026) The T4000's official selling points are entirely cost, energy efficiency and an upgrade path: $1,999 per 1,000 units, 4x performance, 4x energy efficiency and a configurable 70W envelope; IGX Thor is sold on functional safety218 A partial match: cost, speed and reliability. Against: these are hardware modules, not models
Overall assessment: NVIDIA's product line from late 2025 into early 2026 shows a clear engineering turn towards products that are not the most capable but are more reliable, cheaper and faster. The clearest example is the Nemotron 3 family of open models, sold on efficiency and accuracy within a size class, alongside Jetson T4000 and IGX Thor for the industrial edge, sold on cost, energy efficiency and functional safety.
215. NVIDIA, 15 December 2025: Nemotron 3 — https://nvidianews.nvidia.com/news/nvidia-debuts-nemotron-3-family-of-open-models
216. NVIDIA blog: Nemotron 3 Super — https://blogs.nvidia.com/blog/nemotron-3-super-agentic-ai/
217. NVIDIA, 16 March 2026 — https://nvidianews.nvidia.com/news/nvidia-and-global-robotics-leaders-take-physical-ai-to-the-real-world
218. NVIDIA, 5 January 2026 — https://nvidianews.nvidia.com/news/nvidia-releases-new-physical-ai-models-as-global-partners-unveil-next-generation-robots
Chapter 6 — Thresholds
Chapter 7 — OpenAI hardware fact-check, part 1 of 3
Claim-by-claim assessment
Claim under review: "OpenAI poached four to five hundred hardware staff from Apple, effectively an entire department, triggering litigation, and may build smart glasses to replace the phone." All status statements are as at 15 August 2026; the disputes are undecided.
Table 28 Claim-by-claim assessment, part 1 of 3
# Claim under review Assessment Evidence (verbatim or key points)
OpenAI has a transaction with Jony Ive and io Confirmed OpenAI's letter of 21 May 2025, updated 9 July 2025: "the io Products, Inc. team has officially merged with OpenAI"; "Jony Ive and LoveFrom remain independent and have assumed deep design and creative responsibilities across OpenAI"; io was co-founded by Jony Ive with Scott Cannon, Evans Hankey and Tang Tan219 The transaction was worth $6.5bn Partly confirmed (press reporting, not official disclosure) The official OpenAI page gives no figure219. Reuters reported that "OpenAI announced the acquisition of Jony Ive's hardware start-up io Products in a deal valued at $6.5bn", an all-stock transaction, and that according to sources OpenAI already held a 23% stake in io, reflecting an OpenAI valuation of $300bn220; MacRumors describes it as "an estimated $6.5bn"221 The size of the io team Not confirmed (no public figure) OpenAI's letter describes assembling "the best hardware and software engineers, technologists, physicists, scientists, researchers and experts in product development and manufacturing" but gives no headcount219; none of the Reuters, MacRumors, 9to5Mac, Bloomberg Law or IPWatchdog pages reviewed provides a figure for the io team "Four to five hundred hardware staff poached from Apple" Partly confirmed, but the figure must be recharacterised: it is "400+ former Apple employees" company-wide, not 400–500 hardware staff Apple's complaint of 10 July 2026, as reproduced by 9to5Mac: "With over four hundred former Apple employees now working at OpenAI…"222; Reuters likewise reports "more than 400 former Apple employees now work for OpenAI"223; Business Insider: "in the complaint, Apple says the total number of Apple alumni now at OpenAI is more than 400"224. Key qualification: this is Apple's own figure in its pleading, covers all former Apple employees and is not broken down by hardware function.
219. OpenAI, A letter from Sam and Jony — https://openai.com/sam-and-jony/
222. 9to5Mac, 13 July 2026 — https://9to5mac.com/2026/07/13/apple-lawsuit-reveals-how-many-former-employees-now-work-at-openai/
220. Reuters, 21 May 2025 — https://www.reuters.com/business/openai-acquire-jony-ives-hardware-startup-io-products-2025-05-21/
223. Reuters, 10 July 2026 — https://www.reuters.com/legal/litigation/apple-sues-openai-alleging-misappropriation-trade-secret s-court-records-show-2026-07-10/
221. MacRumors, 9 July 2025 — https://www.macrumors.com/2025/07/09/openai-jony-ive-io-acquisition-complete/
224. Business Insider — https://www.businessinsider.com/openai-apple-lawsuit-trade-secrets-ai-talent-war-2026-8
Chapter 7 — Fact-check
Chapter 7 continued — OpenAI hardware fact-check, part 2 of 3
Claim-by-claim assessment
Table 29 Claim-by-claim assessment, part 2 of 3
# Claim under review Assessment Evidence (verbatim or key points)
"Effectively an entire department" Incorrect attribution (overstated) No source supports "an entire department". What is verifiable is breadth rather than a wholesale move: Mark Gurman reported that OpenAI "brought in more than 40 people in the past month alone", spanning camera engineering, iPhone and Mac hardware, silicon, device test and reliability, industrial design, manufacturing, audio, smartwatches, Vision Pro development, software and human factors225; the Wall Street Journal, reviewing LinkedIn data, reported that "dozens" of Apple engineers and designers had left for OpenAI in recent months226; Bloomberg reports that "most" of OpenAI's Apple hires come from the engineering organisation led by incoming CEO John Ternus227 "It triggered litigation" — did Apple sue OpenAI directly Confirmed (but the cause of action is trade secrets, not hiring as such) On 10 July 2026 Apple sued OpenAI and two former employees in the US District Court for the Northern District of California, alleging misappropriation of its trade secrets to benefit the ChatGPT owner's move into consumer hardware; the defendants also include OpenAI Foundation, OpenAI Group PBC and io Products; the two former Apple employees are Chang Liu, a former senior systems electrical engineer, and Tang Yew Tan, a former vice-president of product design for iPhone and Apple Watch228. TechCrunch adds that the claims are trade-secret theft and breach of contract, and notes that io is named in the complaint while Ive is not a defendant229 Was the litigation caused by hiring Partly confirmed; the parties' positions conflict Apple's central allegation is that "OpenAI orchestrated a broad campaign to systematically obtain and exploit Apple's confidential information through former employees, recruiting practices and supplier relationships in order to accelerate its entry into the consumer hardware business"228. In its August 2026 motion OpenAI responded that "Apple should not be allowed to use a baseless and pretextual lawsuit to make up for its shortcomings in the talent market and in employee retention"230 Whether other parties and disputes are involved Confirmed — a second, unrelated line of litigation exists (iyO v OpenAI) iyO, Inc. sued IO, OpenAI, Inc., OpenAI, LLC, Sam Altman and Jonathan Paul Ive, alleging that the IO mark is "confusingly similar" to its IYO mark; the Ninth Circuit affirmed the preliminary injunction in December 2025231; Bloomberg Law notes the injunction "does not bar all use of the 'io' trademark but restricts marketing and sales of products 'sufficiently similar' to iyO's AI audio computer"232; iyO subsequently added trade-secret claims233; on 27 April 2026 iyO issued a release stating that a federal court had granted a preliminary injunction and adding Tang Yew Tan to its amended claims234; the trademark case was stayed on 28 July 2026 pending settlement talks
225. 9to5Mac, 24 November 2025 — https://9to5mac.com/2025/11/24/openai-poaching-apple-hardware-engineers/
230. Business Insider — https://www.businessinsider.com/openai-apple-lawsuit-trade-secrets-ai-talent-war-2026-8
226. MacRumors citing the WSJ, 5 December 2025 — https://www.macrumors.com/2025/12/05/apple-bleeding-talent-to-openai/
231. IPWatchdog, 4 December 2025 — https://ipwatchdog.com/2025/12/04/ninth-circuit-affirms-trademark-injunction-against-o penai-company-dispute-similar-marks/
227. 9to5Mac, 13 July 2026 — https://9to5mac.com/2026/07/13/apple-lawsuit-reveals-how-many-former-employees-now-work-at-openai/
232. Bloomberg Law — https://news.bloomberglaw.com/ip-law/ban-against-openai-using-io-trademark-backed-by-ninth-circuit
228. Reuters, 10 July 2026 — https://www.reuters.com/legal/litigation/apple-sues-openai-alleging-misappropriation-trade-s ecrets-court-records-show-2026-07-10/
233. 9to5Mac, 28 July 2026 — https://9to5mac.com/2026/07/28/iyos-trademark-lawsuit-against-openai-and-jony-ive-paused-over-settlement-talks/
229. TechCrunch, 10 July 2026 — https://techcrunch.com/2026/07/10/apple-sues-openai-over-alleged-trade-secret-theft/
234. iyO press release, 27 April 2026 — https://www.prnewswire.com/news-releases/federal-court-issues-preliminary-injunctionagainst-openai-sam-altman-and-sir-jony-ive-iyo-alleges-trade-secret-theft-by-altmans-hardware-chief-302754047.html
Chapter 7 — Fact-check
Chapter 7 continued — OpenAI hardware fact-check, part 3 of 3
Claim-by-claim assessment
Table 30 Claim-by-claim assessment, part 3 of 3
# Claim under review Assessment Evidence (verbatim or key points)
"May build smart glasses" Partly confirmed: press reporting, not confirmed by OpenAI On 20 February 2026 Reuters, citing The Information, reported that "OpenAI has more than 200 people working on a family of AI devices", including a smart speaker235; a related report lists "a smart speaker, smart glasses and a smart lamp", says the speaker will come first at an expected $200 to $300 and "will not ship before February 2027 at the earliest", attributing this to "two people familiar with the project"236. No official OpenAI page confirms any specific product category237 "To replace the phone" Not confirmed (speculation) No source reviewed shows OpenAI officially claiming it will replace the smartphone. The strongest official statement is that the team is "focused on developing products that inspire, empower and enable"237; Reuters describes the purpose of the transaction as capturing growing demand for physical AI and augmented reality235. The closest media formulation is Bloomberg's analysis headline "How Apple's Lawsuit Threatens to Disrupt OpenAI's Bid to Rival the iPhone", which is interpretation rather than an official statement Current status of the litigation (as at 15 August Confirmed: the disputes are undecided On 3 August 2026 OpenAI published an official response, "Apple is getting this wrong", updated on 6 August to note that "you can read our motion to dismiss here"; OpenAI argues that Apple's request for a preliminary injunction "is based on false information and is entirely unnecessary because we do not have, and do not want, any of their trade secrets"238; as at the Business Insider report, "Apple had not yet responded in court to OpenAI's motion and the judge had not ruled on the request to dismiss"; an Apple spokesperson said "significant evidence has emerged that individuals employed by OpenAI improperly obtained Apple's confidential information about unreleased technology"239
235. Reuters, 20 February 2026 — https://www.reuters.com/business/openai-developing-ai-devices-including-smart-speaker-information-reports-2026-02-20/
238. OpenAI, 3 August 2026 — https://openai.com/index/apple-is-getting-this-wrong/
236. Investing.com citing The Information — https://www.investing.com/news/economy-news/openai-developing-smart-spe aker-and-glasses-with-over-200-employees-93CH-4516599
239. Business Insider — https://www.businessinsider.com/openai-apple-lawsuit-trade-secrets-ai-talent-war-2026-8
237. OpenAI letter — https://openai.com/sam-and-jony/
Chapter 7 — Fact-check
Chapter 7 continued — The corrected formulation we recommend
A single paragraph that can be quoted in full
"On 10 July 2026 Apple sued OpenAI and two former Apple employees (Chang Liu and Tang Yew Tan) in the US District Court for the Northern District of California. The causes of action are trade-secret misappropriation and breach of contract, not hiring as such. In its complaint Apple states that more than 400 former Apple employees now work at OpenAI, a company-wide figure that is not broken down by hardware function. OpenAI has filed a motion to dismiss and publicly denies holding any Apple trade secrets. A separate line of litigation, unrelated to Apple, involves the start-up iyO, which sued OpenAI, Sam Altman and Jony Ive over the 'io' trademark; the Ninth Circuit affirmed a restrictive injunction in December 2025 and the case was stayed in July 2026 pending settlement talks. Product categories such as smart glasses and smart speakers appear only in press reporting, notably by The Information as relayed by Reuters; OpenAI has never confirmed them and has never officially claimed it will replace the smartphone."
Recommended for verbatim use. Status: as at 15 August 2026 neither line of litigation has been finally decided by a court. This white
paper makes no prediction about the outcome.
Three practical implications for corporates and investors
— Talent mobility is not itself a cause of action. The verifiable causes are trade secrets and breach of contract, which means due diligence should focus on onboarding procedures, revocation of system access and document segregation rather than on hiring volumes240241.
— Trademark and brand risk exists independently. The injunction affirmed by the Ninth Circuit restricts marketing and sales of products sufficiently similar to iyO's, rather than banning the mark outright242.
— Product categories should not enter an investment thesis. Hardware categories and timelines rest on anonymous sources and are unconfirmed by the company243244.
240. Reuters, 10 July 2026 — https://www.reuters.com/legal/litigation/apple-sues-openai-alleging-misappropriation-trade-secrets-court-records-show-2026-07-10/
241. TechCrunch, 10 July 2026 — https://techcrunch.com/2026/07/10/apple-sues-openai-over-alleged-trade-secret-theft/
242. Bloomberg Law — https://news.bloomberglaw.com/ip-law/ban-against-openai-using-io-trademark-backed-by-ninth-circuit
243. Reuters, 20 February 2026 — https://www.reuters.com/business/openai-developing-ai-devices-including-smart-speaker-information-reports-2026-02-20/
244. OpenAI letter — https://openai.com/sam-and-jony/
Chapter 7 — Fact-check
Chapter 8 — Where the value pools are moving over two to five years
Six directions of travel, each with supporting evidence
Table 31 Six directions of travel for AI value pools
Direction of travel Supporting evidence
From the model layer to core business workflows BCG finds 70% of AI value potential in core business functions such as sales and marketing, manufacturing, supply chain and pricing, up from 62% in its 2024 report; research and innovation alone accounts for 15%, and the IT share rose 6 percentage points to 13% in 2025245 From generic functions to sector-specific functions The AI Index notes higher adoption in functions related to information processing, software, customer interaction and internal knowledge work, while strategy and corporate finance, risk and compliance "remain low in most industries", with financial services the exception246. BCG reports that in aviation and telecoms the contribution of core functions to AI value approaches 80%, up 15 and 8 percentage points respectively year on year245 From model capability to deployment integration and the data foundation In manufacturing, 78% of leaders direct more than 20% of their improvement budget to smart manufacturing foundations247; in retail, 54% of brand executives report cross-channel and data challenges248; in energy, the IEA ranks missing or inaccessible data and digital infrastructure as the leading barrier249 From building to buying and partnering, with speed of deployment as the decision variable In India, 91% of leaders name speed of deployment as the single largest factor in build-versus-buy decisions, nearly 60% co-create with start-ups and 78% use hybrid models250. MIT NANDA data, which must be quoted with the caveats in chapter 4, report that purchasing from specialist vendors and building partnerships succeeds "about 67% of the time", roughly three times the success frequency of internal builds251 From the cloud to edge hardware and robots Jetson T4000 enters the industrial edge at $1,999 per 1,000 units in a configurable 70W envelope, IGX Thor adds functional safety, and the four largest robot makers are embedding Jetson modules in controllers for real-time edge inference252253. Penetration of the physical layer nevertheless remains very low: Germany 6%254, Denmark 2%255, Brazil 7%256 From going live to governance and validation McKinsey's 2026 AI trust maturity survey of about 500 organisations finds that those investing $25 million or more in responsible AI "score significantly higher on maturity and are far more likely to realise material AI benefits, including EBIT impact above 5%", while nearly two-thirds of respondents name security and risk concerns as the leading barrier to fully scaling agentic AI257
245. BCG (PDF) — https://media-publications.bcg.com/The-Widening-AI-Value-Gap-Sept-2025.pdf
246. Stanford AI Index 2026, chapter 4 — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
247. Deloitte, 2025 Smart Manufacturing Survey — https://www.deloitte.com/us/en/about/press-room/deloitte-2025-smart-manufacturing-survey.html
248. IBM Institute for Business Value and NRF, January 2026 — https://newsroom.ibm.com/2026-01-07-ibm-nrf-study-brands-and-retailers-navigate-a-new-reality-as-ai-shapes-consumer-decisions-before-shopping-begins
249. IEA, Energy and AI executive summary — https://www.iea.org/reports/energy-and-ai/executive-summary
250. EY–CII, November 2025 — https://www.ey.com/en_in/newsroom/2025/11/india-s-ai-shift-from-pilots-to-performance-47-percent-of-enterprises-have-multiple-ai-use-cases-live-in-production-ey-cii-report
251. Fortune reporting on the MIT NANDA study — https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/
252. NVIDIA, 5 January 2026 — https://nvidianews.nvidia.com/news/nvidia-releases-new-physical-ai-models-as-global-partners-unveil-next-generation-robots
253. NVIDIA, 16 March 2026 — https://nvidianews.nvidia.com/news/nvidia-and-global-robotics-leaders-take-physical-ai-to-the-real-world
254. Destatis — https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Unternehmen/IKT-in-Unternehmen-IKT-Branche/Tabellen/ikti-unternehmen-kuenstliche-intelligenz.html
255. Statistics Denmark — https://www.dst.dk/nytpdf/55352
256. Cetic.br, TIC Empresas 2025 — https://cetic.br/media/analises/TIC-Empresas-2025-lancamento.pdf
257. McKinsey, State of AI trust in 2026 — https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era
Chapter 8 — Strategy
Chapter 8 continued — Implications for corporates and for government
What to do differently once the six layers are separated
For corporates
1. Locate yourself before you invest. Use the six layers to establish whether you are at L2 or L4, and benchmark against your own country's official statistics — EU-27 20.0%, Germany 26%, France 18%258259260 — rather than against the 88% of executive surveys.
2. Direct budget at core business functions rather than support functions: 70% of the value potential sits in core functions261.
3. Fund governance and validation first. It is the observable input variable associated with EBIT impact above 5%262.
4. Do not mistake employee usage for organisational capability. 54% of employees use AI tools without formal authorisation, while only 13% of organisations have integrated agents broadly into workflows263.
5. For SMEs the binding constraint is cost and staffing, not willingness. In Korea the leading reason for non-use is the burden of economic cost at 39.6%, followed by missing infrastructure and staff at 34.9%264; in Canada 40.0% of businesses consider AI not relevant to them265.
For governments and policymakers
1. Statistical definitions are a precondition for policy. Adopting the Eurostat ICT enterprise survey framework yields grade A comparability266; Korea has stated that it drew on the Eurostat questionnaire to improve cross-country consistency267.
2. Do not rank countries for policy purposes using population-level diffusion. France shows 44.0% population diffusion against 18% enterprise adoption268260.
3. SMEs are the policy lever. Danish firms with 10–49 employees moved from 12% to 37% in three years while those with 250 or more moved from 51% to 75%269; the French size gap widened from 16 to 43 percentage points260.
4. Physical-AI policy should track density, not volume. China accounts for 54% of global installations but records a density of only 166, ranking 22nd270271.
5. The public sector is itself an adopter. 67% of OECD countries use AI to improve the design and delivery of public services (2024)272.
258. Eurostat, 11 December 2025 — https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251211-2
259. Destatis — https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Unternehmen/IKT-in-Unternehmen-IKT-Branche/Tabellen/ikti-unternehmen-kuenstliche-intelligenz.html
260. INSEE Premiere no. 2120 — https://www.insee.fr/fr/statistiques/9025878
261. BCG (PDF) — https://media-publications.bcg.com/The-Widening-AI-Value-Gap-Sept-2025.pdf
262. McKinsey, State of AI trust in 2026 — https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era
263. BCG, The AI Adoption Puzzle — https://www.bcg.com/publications/2025/ai-adoption-puzzle-why-usage-up-impact-not
264. NIA statistics (via Etnews) — https://www.etnews.com/20260120000071
265. Statistics Canada, CSBC — https://www150.statcan.gc.ca/n1/pub/11-621-m/11-621-m2026010-eng.htm
266. Eurostat Statistics Explained — https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises
267. Dong-A Science: Korean statistics follow the Eurostat questionnaire — https://m.dongascience.com/en/news/63090
268. Stanford AI Index 2026, chapter 4 — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
269. Danish Agency for Digital Government, ITAV 2025 — https://digst.dk/media/5vkpv2uj/faktaark-virksomhedernes-anvendelse-af-ai-2023-2025-itav.pdf
270. IFR press release, 25 September 2025 — https://ifr.org/ifr-press-releases/news/global-robot-demand-in-factories-doubles-over-10-years
271. IFR density data (via The Robot Report) — https://www.therobotreport.com/ifr-reports-robot-density-increase-across-europe-asia-americas/
272. OECD, Governing with Artificial Intelligence — https://www.oecd.org/en/publications/2025/06/governing-with-artificial-intelligence_398fa287/full-report/ai-in-public-service-design-and-delivery_09704c1a.html
Chapter 8 — Strategy
Chapter 8 continued — Implications for AI start-ups and investors
Why the strongest model is not the most usable product
1. Sell speed of deployment, not benchmark scores. 91% of Indian enterprise leaders name speed of deployment as the primary build-versus-buy factor273.
2. Target large enterprises and the information, financial and professional services sectors, and note that sector ceilings differ enormously: Singapore accommodation and food at 4.7% versus information and communication at
35.9%274.
3. Budget ceilings are a hard reality. More than 95% of Indian organisations allocate less than 20% of their IT budget to AI273; on BCG's basis AI represents roughly 4.4% to 5.2% of IT budgets by region275.
4. In physical AI the capital and ecosystem signals are clear but the installed penetration is not. China recorded 140 robotics-related financing transactions between January and April 2025, 38.7% of all AI financing events276, while enterprise use of autonomous machine movement runs at only 2% to 7%277278.
5. The energy vertical is severely under-invested. "Only 2% of energy start-up equity funding goes to companies with an AI-related value proposition"279.
Why the strongest model is not the most usable product
1. On the same task, AI can move outcomes in opposite directions. Contact-centre agents gained 14–15%, developer pull requests 26% and marketing output 50%, yet METR found experienced open-source developers were 19% slower, a result later studies failed to replicate280.
2. Benefits are distributed very unevenly. In the contact-centre study the largest gains accrued to less experienced agents, at 30–35% (same source).
3. The constraint on scaling is organisational and trust-related, not capability. Fully scaled agent use is in single digits across almost every function280.
4. Architecture and unit cost can veto the "best" model outright. "If you have the wrong architecture, even if it's free, it's not cheap enough."281.
5. Engineering reliability can transform usability with no change of model. The same DeepSeek-R1 service moved from
87.01% to 99.36% call success and from 3.07 to 1.02 seconds time to first token in seven months282.
273. EY–CII, November 2025 — https://www.ey.com/en_in/newsroom/2025/11/india-s-ai-shift-from-pilots-to-performance-47-percent-of-enterprises-have-multiple-ai-use-cases-live-in-production-ey-cii-report
274. IMDA, Singapore Digital Economy Report 2025 — https://www.imda.gov.sg/-/media/imda/files/about/resources/corporate-publications/annual-report/imda-sgde-report-fy2024-2025.pdf
275. BCG (PDF) — https://media-publications.bcg.com/The-Widening-AI-Value-Gap-Sept-2025.pdf
276. CNNIC 56th report — https://www.cnnic.net.cn/NMediaFile/2025/0730/MAIN1753846666507QEK67ZS9DH.pdf
277. Statistics Denmark — https://www.dst.dk/nytpdf/55352
278. Cetic.br, TIC Empresas 2025 — https://cetic.br/media/analises/TIC-Empresas-2025-lancamento.pdf
279. IEA executive summary — https://www.iea.org/reports/energy-and-ai/executive-summary
280. Stanford AI Index 2026, figure 4.4.27 — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
281. Data Center Frontier, GTC 2026 — https://www.datacenterfrontier.com/machine-learning/news/55364406/jensen-huang-maps-the-ai-factory-era-at-nvidia-gtc-2026
282. CAICT, AI Industry Development Research Report 2025 — https://www.caict.ac.cn/kxyj/qwfb/bps/202602/P020260202487301304903.pdf
Chapter 8 — Strategy
Chapter 9 — Data limitations and rules of use
To be published together with any extract from this white paper
1. Bases must never be mixed. The enterprise adoption rates and population diffusion rates in chapter 2 and the industry surveys in chapter 3 come from three non-interchangeable measurement systems. Placing them on a single ranking chart produces false conclusions.
2. What the A/B/C grades mean. Grade A is restricted to countries using the Eurostat framework (Germany, France, Denmark, EU-27); grade B covers official statistics on a different basis (United States, United Kingdom, Canada, Korea, Singapore, Brazil); grade C covers institutional or industry surveys and telemetry estimates (China's enterprise adoption, Japan, India, the United Arab Emirates, Saudi Arabia).
3. Five countries have no verifiable enterprise AI adoption rate. This white paper records n.a. and does not substitute consumer data: China, Japan (on an enterprise basis), India (official basis), the United Arab Emirates and Saudi Arabia (business-sector basis).
4. Known breaks in series. The US BTOS question changed in November 2025 and the two periods cannot be joined into a trend line283284; Eurostat and INSEE added image, video and audio generation in 2025, taking France from seven to eight technologies285; Japan's Ministry of Internal Affairs states that the estimation method changed between the 2023 and 2024 surveys, so they "cannot be simply compared"286.
5. Vendor and consultancy sources are labelled. The NVIDIA telecoms survey, the Thomson Reuters professional-services survey, the Itron utilities survey, the Trimble transportation survey and the IBM–NRF retail study are vendor or vendor-commissioned research and must not be read as industry averages.
6. This white paper contains no market-size forecasts. The only forward-looking figures are the IFR's official projections for 2025 (575,000 units) and 2028 (more than 700,000 units)287 and CAICT's estimate that China's core AI industry will exceed RMB 1.2 trillion in 2025288, both labelled as projections.
7. Fields that require a return to source. The total number of FDA-authorised AI-enabled devices (the FDA page gives no total)289; the full use-case ranking in the European hotel study290; and the quantified benefit section of the IMDA report291.
8. A discrepancy between the web and PDF editions of the AI Index 2026. The web edition states that generative AI is used in 70% of organisations292, while figure 4.3.1 of the PDF gives 79%293. This white paper quotes the PDF value and notes the difference rather than merging them.
9. All self-reported data are not audited financials. Every proportion at layer L5 (cost 38%, profitability 36%, revenue 33% and so on) reflects respondent judgement293.
10. Productivity evidence cannot be averaged. Within a single compilation, comparable work ranges from −19% to +200%. Reports should list study, occupation, application and beneficiary group rather than quote a single "AI raises productivity by X%" figure.
283. US Census Bureau, BTOS — https://www.census.gov/library/stories/2026/05/ai-use-businesses.html
284. Federal Reserve, FEDS Notes — https://www.federalreserve.gov/econres/notes/feds-notes/monitoring-ai-adoption-in-the-u-s-economy-20260403.html
285. INSEE Premiere no. 2120 — https://www.insee.fr/fr/statistiques/9025878
286. Japan Ministry of Internal Affairs and Communications white paper — https://www.soumu.go.jp/johotsusintokei/whitepaper/ja/r07/html/nd112220.html
287. IFR press release, 25 September 2025 — https://ifr.org/ifr-press-releases/news/global-robot-demand-in-factories-doubles-over-10-years
288. CAICT, 2025 report — https://www.caict.ac.cn/kxyj/qwfb/bps/202602/P020260202487301304903.pdf
289. US FDA, AI-enabled medical device list — https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices
290. Schegg 2025 — https://www.hotellerie.de/fileadmin/user_upload/Dokumente/Studien_und_Umfragen/Schegg_2025_AI_Adoption_in_European_Hotels_03072025.pdf
291. IMDA, Singapore Digital Economy Report 2025 — https://www.imda.gov.sg/-/media/imda/files/about/resources/corporate-publications/annual-report/imda-sgde-report-fy2024-2025.pdf
292. Stanford AI Index 2026, Economy (web) — https://hai.stanford.edu/ai-index/2026-ai-index-report/economy
293. Stanford AI Index 2026 (PDF, chapter 4) — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
Chapter 9 — Limitations
Chapter 10 — Conclusions: eight findings that can be quoted directly
Each rests on the evidence set out in the preceding chapters
1. On an official statistical basis, global enterprise AI adoption remains at the order of one in five. EU-27 20.0%, United States 19.8% and Canada 19.2% agree closely294295296.
2. The consultancy basis exceeds the official basis by more than four times because of base and definition, not data quality. McKinsey explicitly leaves "adoption" undefined297.
3. Scaling is the narrowest bottleneck in the chain. 62% at least experiment with agents, only 23% have scaled anywhere in the enterprise, and fully scaled use is in single digits across functions298299.
4. Firm size is the most stable structural fact worldwide and is widening in some countries. The French size gap grew from 16 to 43 percentage points in three years300.
5. Sector divergence exceeds country divergence. Finance and information and communication are the only sectors in the high-adoption band, while construction has scaled AI across projects in only 1% of organisations301302.
6. Production, not the back office, is the weakest link in manufacturing AI. The share of Brazilian industrial firms using AI in production fell from 56.4% to 52.0% while administration reached 87.9%303.
7. Physical AI has started in strategy and ecosystem terms while enterprise penetration remains in single digits; both must be shown together. The installation leader and the density leader are different countries: China takes 54% of installations at a density of 166, while Korea leads on density at 1,220304305.
8. Investment in governance and validation is an observable variable associated with EBIT impact, not a cost centre. Organisations investing $25 million or more in responsible AI are more likely to report EBIT impact above 5%306.
Next step: turn the six layers into an internal dashboard
Bring layers L2 to L6 into auditable definitions: the number of registered AI use cases (L2), the number of use cases in production and the functions they cover (L4), attributable cost and revenue movements (L5), and the number of edge and robotic deployment points (L6). Only when L4 and L5 are written into management-accounting definitions can AI spending enter a serious profit-and-loss discussion.
294. Eurostat, 11 December 2025 — https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251211-2
295. US Census Bureau, BTOS — https://www.census.gov/library/stories/2026/05/ai-use-businesses.html
296. Statistics Canada, CSBC — https://www150.statcan.gc.ca/n1/pub/11-621-m/11-621-m2026010-eng.htm
297. McKinsey (PDF) — https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/the%20state%20of%20ai/2025/the-state-of-ai-howorganizations-are-rewiring-to-capture-value_final.pdf
298. McKinsey, State of AI 2025 — https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
299. Stanford AI Index 2026, chapter 4 — https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf
300. INSEE Premiere no. 2120 — https://www.insee.fr/fr/statistiques/9025878
301. Bank of England and FCA, 2024 — https://www.bankofengland.co.uk/report/2024/artificial-intelligence-in-uk-financial-services-2024
302. RICS, 2025 — https://www.rics.org/news-insights/optimism-high-for-ai-in-construction-but-skills-shortages-and-integration-challenges-adoption
303. IBGE, PINTEC Semestral — https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/44551-de-2022-a-2024-percentual-de-empresas-industri ais-utilizando-inteligencia-artificial-subiu-de-16-9-para-41-9
304. IFR executive summary (PDF) — https://ifr.org/img/worldrobotics/Executive_Summary_WR_2025_Industrial_Robots.pdf
305. IFR density data (via The Robot Report) — https://www.therobotreport.com/ifr-reports-robot-density-increase-across-europe-asia-americas/
306. McKinsey, State of AI trust in 2026 — https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era
Chapter 10 — Conclusions
Global AI in the Real Economy · English edition · August 2026
Disclaimer and limitations of use
1. Data cut-off. All data in this white paper are based on material publicly available as at 15 August 2026. Official statistics, industry surveys and litigation developments published after that date are not included.
2. Different bases cannot be ranked directly. The enterprise adoption rates, population-level AI diffusion rates and industry surveys used here come from three non-interchangeable measurement systems whose thresholds, bases, recall periods and technology lists all differ. Without calibration they must not be used to rank or rate countries, regions or industries. The meaning of the A/B/C comparability grades is set out in chapter 9.
3. Vendor surveys are not industry averages. Some data quoted here originate from vendors or vendor-commissioned research, including NVIDIA, Thomson Reuters, Itron, Trimble and IBM–NRF. Such results reflect the experience of self-selected samples and must not be equated with industry-average outcomes. All self-reported financial and productivity results are unaudited.
4. Unresolved disputes. Statements concerning the litigation between Apple and OpenAI, and between iyO and OpenAI, describe the public record as at 15 August 2026. Those disputes have not been finally decided by any court. This white paper makes no prediction about the outcome and does not find any party's claims to be established.
5. Not professional advice. This white paper is not investment, legal or regulatory advice and should not be the sole basis for any investment decision, compliance judgement or procurement choice. Readers should consider their own circumstances and consult suitably qualified professionals.
6. Copyright. © 2026 InsightBridge Global LLC. All rights reserved. Tables and charts may be quoted provided the source, the definitional caveats and this disclaimer are retained in full.
About InsightBridge Global LLC
InsightBridge Global LLC produces industry and technology research grounded in primary evidence, turning public statistics, regulatory filings and first-hand corporate statements into verifiable decision material. Attribution: InsightBridge Global LLC / Dr. Tong Yin
Suggested citation
InsightBridge Global LLC / Dr. Tong Yin, Global AI in the Real Economy: From Adoption Hype to Industrial Value and Physical AI, August 2026. When quoting, state the data year, the statistical basis and the comparability grade (A/B/C).
InsightBridge Global LLC / Dr. Tong Yin Prepared August 2026 · Data cut-off 15 August 2026 · Public release Every figure can be traced through the numbered source links at the foot of each page.