{ "schemaVersion": 1, "datasetId": "ki-nutzungsatlas-de-eu", "title": { "de": "KI-Nutzungsatlas Deutschland & EU", "en": "AI Adoption Atlas Germany & EU" }, "description": { "de": "Amtliche Eurostat-Surveywerte zur Nutzung von KI-Technologien in Unternehmen und generativer KI durch Menschen – mit Deutschland-Fokus, EU-Vergleich und archivierten Revisionen.", "en": "Official Eurostat survey values for business use of AI technologies and individual use of generative AI, with a Germany focus, EU comparison and archived revisions." }, "generatedAt": "2026-07-22T11:13:28.323Z", "dateModified": "2026-06-15", "trackingSince": "2026-07-22", "latestEnterpriseYear": "2025", "latestIndividualYear": "2025", "materialFingerprint": "d5863742a97ee1f0f8a00bbf22074ca681c8b980226d1e0ad06464fd822b15ae", "license": "https://creativecommons.org/licenses/by/4.0/", "sourceLicense": "https://ec.europa.eu/eurostat/about-us/policies/copyright", "surveyBoundary": { "enterprise": "Enterprises with at least 10 employees and self-employed persons in covered NACE Rev. 2 activities, excluding the financial sector.", "individuals": "Individuals aged 16 to 74; use of generative AI tools during the three months before the survey.", "separateUniverses": true, "noCompositeScore": true, "noGenerativeClassification": true }, "counts": { "countries": 27, "geographies": 28, "observations": 2352, "available": 2333, "missing": 19, "flagged": 57, "enterpriseYears": 4, "revisions": 5 }, "collectionStatus": { "mode": "automated-source-observation", "revision": 2, "observedAt": "2026-07-22T11:13:28.323Z", "sourceCount": 5, "observedSourceCount": 5, "unavailableSourceCount": 0, "retainedSourceCount": 0 }, "sourceCollection": { "revision": 2, "observedAt": "2026-07-22T11:13:28.323Z", "historyTotalCount": 5, "historyRetainedCount": 5, "historyTruncated": false }, "labels": { "freq": { "label": "Time frequency", "categories": { "A": "Annual" } }, "size_emp": { "label": "Size classes in number of persons employed", "categories": { "GE10": "10 persons employed or more", "10-49": "From 10 to 49 persons employed", "50-249": "From 50 to 249 persons employed", "GE250": "250 persons employed or more" } }, "nace_r2": { "label": "Statistical classification of economic activities in the European Community (NACE Rev. 2)", "categories": { "C10-S951_X_K": "All activities (except agriculture, forestry and fishing, and mining and quarrying), without financial sector", "C": "Manufacturing", "F": "Construction", "G": "Wholesale and retail trade; repair of motor vehicles and motorcycles", "H": "Transportation and storage", "I": "Accommodation and food service activities", "J": "Information and communication", "L_M": "Real estate activities; professional, scientific and technical activities", "N": "Administrative and support service activities" } }, "indic_is": { "label": "Information society indicator", "categories": { "E_AI_TANY": "Enterprises using at least one of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR, E_AI_TPVSG", "E_AI_TTM": "Enterprises using AI technologies performing analysis of written language (text mining)", "E_AI_TSR": "Enterprises using AI technologies converting spoken language into machine-readable format (speech recognition)", "E_AI_TNLG": "Enterprises using AI technologies generating written or spoken language or programming codes (natural language generation, speech synthesis)", "E_AI_TIR": "Enterprises using AI technologies identifying objects or persons based on images (image recognition, image processing)", "E_AI_TML": "Enterprises using machine learning (e.g. deep learning) for data analysis", "E_AI_TPA": "Enterprises using AI technologies automating different workflows or assisting in decision making (AI based software robotic process automation)", "E_AI_TAR": "Enterprises using AI technologies enabling physical movement of machines via autonomous decisions based on observation of surroundings (autonomous robots, self-driving vehicles, autonomous drones)", "E_AI_TPVSG": "Enterprises using AI technologies generating pictures, videos, sound/audio", "I_IUAI": "Use of generative AI tools: in the last 3 months", "I_IUAIPR": "Use of generative AI tools: for private purposes", "I_IUAIWP": "Use of generative AI tools: for professional (work) purposes", "I_IUAIFE": "Use of generative AI tools: for formal education" } }, "unit": { "label": "Unit of measure", "categories": { "PC_ENT": "Percentage of enterprises", "PC_IND": "Percentage of individuals" } }, "geo": { "label": "Geopolitical entity (reporting)", "categories": { "EU27_2020": "European Union - 27 countries (from 2020)", "EA": "Euro area (EA11-1999, EA12-2001, EA13-2007, EA15-2008, EA16-2009, EA17-2011, EA18-2014, EA19-2015, EA20-2023, EA21-2026)", "BE": "Belgium", "BG": "Bulgaria", "CZ": "Czechia", "DK": "Denmark", "DE": "Germany", "EE": "Estonia", "IE": "Ireland", "EL": "Greece", "ES": "Spain", "FR": "France", "HR": "Croatia", "IT": "Italy", "CY": "Cyprus", "LV": "Latvia", "LT": "Lithuania", "LU": "Luxembourg", "HU": "Hungary", "MT": "Malta", "NL": "Netherlands", "AT": "Austria", "PL": "Poland", "PT": "Portugal", "RO": "Romania", "SI": "Slovenia", "SK": "Slovakia", "FI": "Finland", "SE": "Sweden", "NO": "Norway", "BA": "Bosnia and Herzegovina", "ME": "Montenegro", "MK": "North Macedonia", "AL": "Albania", "RS": "Serbia", "TR": "Türkiye", "CH": "Switzerland", "XK": "Kosovo*" } }, "time": { "label": "Time", "categories": { "2021": "2021", "2023": "2023", "2024": "2024", "2025": "2025" } }, "ind_type": { "label": "Individual type", "categories": { "IND_TOTAL": "All individuals", "Y16_24": "Individuals, 16 to 24 years old", "Y25_54": "Individuals, 25 to 54 years old", "Y55_74": "Individuals, 55 to 74 years old", "M_Y16_74": "Males, 16 to 74 years old", "F_Y16_74": "Females, 16 to 74 years old", "I0_2": "Individuals with no or low formal education", "I3_4": "Individuals with medium formal education", "I5_8": "Individuals with high formal education", "IND_DEG1": "Individuals living in cities", "IND_DEG2": "Individuals living in towns and suburbs", "IND_DEG3": "Individuals living in rural areas", "SAL_SELF_FAM": "Employees, self-employed, family workers", "STUD": "Students", "UNE": "Unemployed" } } }, "officialSources": [ { "id": "api", "title": "Eurostat Statistics API", "url": "https://ec.europa.eu/eurostat/api/dissemination/statistics/1.0/" }, { "id": "enterprise", "title": "Eurostat · isoc_eb_ai · DOI 10.2908/ISOC_EB_AI", "url": "https://doi.org/10.2908/ISOC_EB_AI" }, { "id": "sector", "title": "Eurostat · isoc_eb_ain2 · DOI 10.2908/ISOC_EB_AIN2", "url": "https://doi.org/10.2908/ISOC_EB_AIN2" }, { "id": "individual", "title": "Eurostat · isoc_ai_iaiu · DOI 10.2908/ISOC_AI_IAIU", "url": "https://doi.org/10.2908/ISOC_AI_IAIU" }, { "id": "enterprise-method", "title": "Eurostat methodology · ICT usage in enterprises", "url": "https://ec.europa.eu/eurostat/cache/metadata/en/isoc_e_esms.htm" }, { "id": "individual-method", "title": "Eurostat methodology · ICT usage by individuals", "url": "https://ec.europa.eu/eurostat/cache/metadata/en/isoc_i_esms.htm" }, { "id": "destatis-validation", "title": "Destatis · KI-Technologien nach Beschäftigtengrößenklasse", "url": "https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Unternehmen/IKT-in-Unternehmen-IKT-Branche/Tabellen/ikti-unternehmen-kuenstliche-intelligenz.html" } ], "sourceQueries": [ { "id": "eurostat-enterprise-timeline", "dataset": "isoc_eb_ai", "label": { "de": "KI-Nutzung in Unternehmen nach Land und Jahr", "en": "Enterprise AI use by country and year" }, "url": "https://ec.europa.eu/eurostat/api/dissemination/statistics/1.0/data/isoc_eb_ai?lang=en&freq=A&size_emp=GE10&nace_r2=C10-S951_X_K&indic_is=E_AI_TANY&unit=PC_ENT&time=2021&time=2023&time=2024&time=2025", "updatedAt": "2026-06-15T09:00:00.000Z", "observationCount": 112, "availableCount": 112, "missingCount": 0, "flaggedCount": 3 }, { "id": "eurostat-enterprise-size", "dataset": "isoc_eb_ai", "label": { "de": "KI-Nutzung nach Unternehmensgröße", "en": "Enterprise AI use by size" }, "url": "https://ec.europa.eu/eurostat/api/dissemination/statistics/1.0/data/isoc_eb_ai?lang=en&freq=A&size_emp=10-49&size_emp=50-249&size_emp=GE250&nace_r2=C10-S951_X_K&indic_is=E_AI_TANY&unit=PC_ENT&time=2021&time=2023&time=2024&time=2025", "updatedAt": "2026-06-15T09:00:00.000Z", "observationCount": 112, "availableCount": 112, "missingCount": 0, "flaggedCount": 2 }, { "id": "eurostat-enterprise-technology", "dataset": "isoc_eb_ai", "label": { "de": "Eingesetzte KI-Technologien", "en": "AI technologies used" }, "url": "https://ec.europa.eu/eurostat/api/dissemination/statistics/1.0/data/isoc_eb_ai?lang=en&freq=A&size_emp=GE10&nace_r2=C10-S951_X_K&indic_is=E_AI_TTM&indic_is=E_AI_TSR&indic_is=E_AI_TNLG&indic_is=E_AI_TIR&indic_is=E_AI_TML&indic_is=E_AI_TPA&indic_is=E_AI_TAR&indic_is=E_AI_TPVSG&unit=PC_ENT&time=2021&time=2023&time=2024&time=2025", "updatedAt": "2026-06-15T09:00:00.000Z", "observationCount": 224, "availableCount": 224, "missingCount": 0, "flaggedCount": 2 }, { "id": "eurostat-enterprise-sector", "dataset": "isoc_eb_ain2", "label": { "de": "KI-Nutzung nach Wirtschaftszweig", "en": "Enterprise AI use by economic activity" }, "url": "https://ec.europa.eu/eurostat/api/dissemination/statistics/1.0/data/isoc_eb_ain2?lang=en&freq=A&size_emp=GE10&nace_r2=C&nace_r2=C10-S951_X_K&nace_r2=F&nace_r2=G&nace_r2=H&nace_r2=I&nace_r2=J&nace_r2=L_M&nace_r2=N&indic_is=E_AI_TANY&unit=PC_ENT&time=2021&time=2023&time=2024&time=2025", "updatedAt": "2026-06-15T09:00:00.000Z", "observationCount": 224, "availableCount": 220, "missingCount": 4, "flaggedCount": 5 }, { "id": "eurostat-individual-use", "dataset": "isoc_ai_iaiu", "label": { "de": "Nutzung generativer KI durch Menschen", "en": "Individual use of generative AI" }, "url": "https://ec.europa.eu/eurostat/api/dissemination/statistics/1.0/data/isoc_ai_iaiu?lang=en&freq=A&ind_type=IND_TOTAL&ind_type=Y16_24&ind_type=Y25_54&ind_type=Y55_74&ind_type=M_Y16_74&ind_type=F_Y16_74&ind_type=I0_2&ind_type=I3_4&ind_type=I5_8&ind_type=IND_DEG1&ind_type=IND_DEG2&ind_type=IND_DEG3&ind_type=SAL_SELF_FAM&ind_type=STUD&ind_type=UNE&indic_is=I_IUAI&indic_is=I_IUAIPR&indic_is=I_IUAIWP&indic_is=I_IUAIFE&unit=PC_IND&time=2025", "updatedAt": "2026-06-05T09:00:00.000Z", "observationCount": 1680, "availableCount": 1665, "missingCount": 15, "flaggedCount": 45 } ], "methodology": { "enterpriseUniverse": "Enterprises with at least 10 employees and self-employed persons in covered NACE Rev. 2 activities, excluding the financial sector.", "individualUniverse": "Individuals aged 16 to 74; use of generative AI tools during the three months before the survey.", "boundaries": [ "Enterprise and individual survey universes remain separate and are never combined into one adoption score.", "PC_ENT and PC_IND use different denominators and are never summed.", "Missing, confidential, low-reliability, break-in-series and definition-difference flags remain explicit; missing values are never converted to zero.", "Reference year, Eurostat dataset update time and i6eal observation time remain separate.", "Country ranks are descriptive ordering of the selected official value, not a quality assessment." ], "generativeAiUsed": false }, "countries": [ { "code": "EU27_2020", "slug": "eu-27", "name": { "de": "EU-27", "en": "EU-27" }, "kind": "aggregate", "dateModified": "2026-06-15", "enterprise": { "latest": { "code": "E_AI_TANY", "year": "2025", "value": 19.95, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, "timeline": [ { "code": "E_AI_TANY", "year": "2021", "value": 7.65, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "E_AI_TANY", "year": "2023", "value": 8.06, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "E_AI_TANY", "year": "2024", "value": 13.48, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "E_AI_TANY", "year": "2025", "value": 19.95, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" } ], "sizes": [ { "code": "GE10", "year": "2025", "value": 19.95, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "10-49", "year": "2025", "value": 17, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "50-249", "year": "2025", "value": 30.36, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "GE250", "year": "2025", "value": 55.03, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" } ], "technologies": [ { "code": "E_AI_TTM", "year": "2025", "value": 11.75, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "E_AI_TSR", "year": "2025", "value": 7.22, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "E_AI_TNLG", "year": "2025", "value": 8.76, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "E_AI_TPVSG", "year": "2025", "value": 9.55, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "E_AI_TIR", "year": "2025", "value": 3.78, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "E_AI_TML", "year": "2025", "value": 5.1, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "E_AI_TPA", "year": "2025", "value": 5.35, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "E_AI_TAR", "year": "2025", "value": 1.39, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" } ], "sectors": [ { "code": "C", "year": "2025", "value": 17.27, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "F", "year": "2025", "value": 10.79, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "G", "year": "2025", "value": 18.62, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "H", "year": "2025", "value": 11.15, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "I", "year": "2025", "value": 11.98, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "J", "year": "2025", "value": 62.52, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "L_M", "year": "2025", "value": 38.16, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "N", "year": "2025", "value": 19.86, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" } ], "rank": null, "deltaToEu": 0 }, "individuals": { "latest": { "code": "I_IUAI", "year": "2025", "value": 32.66, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, "purposes": [ { "code": "I_IUAI", "year": "2025", "value": 32.66, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "I_IUAIPR", "year": "2025", "value": 25.49, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "I_IUAIWP", "year": "2025", "value": 15.36, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "I_IUAIFE", "year": "2025", "value": 9.32, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" } ], "demographics": [ { "code": "Y16_24", "year": "2025", "value": 63.81, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "Y25_54", "year": "2025", "value": 38.55, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "Y55_74", "year": "2025", "value": 11.98, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "M_Y16_74", "year": "2025", "value": 34.91, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "F_Y16_74", "year": "2025", "value": 30.45, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "I0_2", "year": "2025", "value": 22.27, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "I3_4", "year": "2025", "value": 25.71, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "I5_8", "year": "2025", "value": 48.98, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "IND_DEG1", "year": "2025", "value": 39.92, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "IND_DEG2", "year": "2025", "value": 30.47, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "IND_DEG3", "year": "2025", "value": 24.47, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "STUD", "year": "2025", "value": 71.96, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "SAL_SELF_FAM", "year": "2025", "value": 36.62, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" }, { "code": "UNE", "year": "2025", "value": 29.44, "status": null, "unit": "PC_IND", "updatedAt": "2026-06-05T09:00:00.000Z" } ], "rank": null, "deltaToEu": 0 } }, { "code": "AT", "slug": "oesterreich", "name": { "de": "Österreich", "en": "Austria" }, "kind": "country", "dateModified": "2026-06-15", "enterprise": { "latest": { "code": "E_AI_TANY", "year": "2025", "value": 29.95, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, "timeline": [ { "code": "E_AI_TANY", "year": "2021", "value": 8.83, "status": null, "unit": "PC_ENT", "updatedAt": "2026-06-15T09:00:00.000Z" }, { "code": "E_AI_TANY", "year": "2023", "value": 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