AI Monitor 2026
Data explorer
2 min readPublished Published by Teklens
The explorer shows the published aggregates of the AI Monitor 2026: the six dimensions and 19 items of the DACH analysis with 64 organisations, optionally per readiness profile, and the six dimensions of the European survey with 165 responses, optionally per hierarchical level. Every view names sample, scale and source and can be shared as a link. Combinations the raw data cannot support are not offered.
Contents · Short answer
Analysis
Analysis
View
Readiness profile
Sample of this view: DACH analysis (n = 64)
- People & capabilitiesInternal Assets3.38
- LeadershipLeadership3.26
- Technology foundationsTech Foundation2.86
- DataData Engine2.80
- Governance & ecosystemExternal Alignment2.65
- AdaptabilityDynamic Capabilities2.61
- Unit
- 19 items on a five-level maturity scale (1 = least developed, 5 = most advanced); most levels carry a behavioural anchor.
- Source
- Probst 2026, Table 4 · Table 4 and Figure 4 – Means and standard deviations of the six dimensions (Section 4.3) · Self-assessment, exploratory, not representative.
Profiles exist only in the DACH analysis, hierarchical levels only in the European survey. The explorer offers no combinations because the raw data cannot support them.
Data as a table
| Dimension | Research label | M | SD | α | ω | n |
|---|---|---|---|---|---|---|
| People & capabilities | Internal Assets | 3.38 | 0.79 | 0.677 | 0.702 | 64 |
| Leadership | Leadership | 3.26 | 0.98 | 0.863 | 0.869 | 64 |
| Technology foundations | Tech Foundation | 2.86 | 0.98 | 0.863 | 0.869 | 64 |
| Data | Data Engine | 2.80 | 0.87 | 0.757 | 0.767 | 64 |
| Governance & ecosystem | External Alignment | 2.65 | 0.89 | 0.663 | 0.719 | 64 |
| Adaptability | Dynamic Capabilities | 2.61 | 0.96 | 0.861 | 0.862 | 64 |
What the explorer shows and what it does not
- DACH analysis: six dimension scores and 19 item scores on the 1–5 scale, overall and per readiness profile (dimensions). Item scores per profile were not published by the thesis.
- European survey: six dimension scores on 0–100, overall and per hierarchical level. Item scores exist for a handful of items only and are quoted in the chapters.
- No industry or size segments: for the European survey they are not reconciled with the source, and for the DACH analysis they are documented as cross-tabulations with small cells in the readiness profiles chapter.
- No combinations: profiles exist only in the DACH analysis, hierarchical levels only in the European survey. Segments below five cases are not shown, segments below ten are flagged.
Scope & limits
- All values are self-assessments from two differently filtered analyses of the same survey. They are neither representative nor disjoint.
- Comparisons between hierarchical levels compare different people from different organisations.
- Distributions, confidence intervals and cross-tabulations cannot be derived from means; the explorer only shows what is published.
Questions & answers
Can I share a view?
Yes. "Share view" copies this page's address with the chosen analysis, view and selection. The link carries no personal data and no tracking parameters.
Why is there no country filter?
The European survey export carries no country field. The DACH analysis treats Germany and Switzerland together because 18 German responses are too few to split.
Where do the raw data come from?
From the AI Monitor survey that Teklens ran in 2026 with the research partners ETH Zurich and the University of St.Gallen. Raw data, respondents and identifiers are not part of this website.
Sources
- Probst 2026, Table 4 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Table 4 and Figure 4 – Means and standard deviations of the six dimensions (Section 4.3). Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: Maturity scale 1–5 · Limits: Self-assessment, exploratory, not representative.Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? An Empirical Analysis Using an Integrated TOE–DCT Framework. Master's thesis, University of St.Gallen, Institute of Information Systems and Digital Business. Supervisor: Prof. Dr. Ingrid Bauer-Hänsel; co-supervisor: Prof. Dr. Benjamin van Giffen. St. Gallen, 21 August 2026. Conducted in cooperation with Teklens.
- Probst 2026, Table 5 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Table 5 and Figure 7 – Dimension scores by profile (Section 4.4.2). Sample: n = 64; profiles of n = 12, 27 and 25 · Scale: Maturity scale 1–5 · Limits: Profiles from K-means on the same six dimensions; differences between profiles are large by construction.Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? An Empirical Analysis Using an Integrated TOE–DCT Framework. Master's thesis, University of St.Gallen, Institute of Information Systems and Digital Business. Supervisor: Prof. Dr. Ingrid Bauer-Hänsel; co-supervisor: Prof. Dr. Benjamin van Giffen. St. Gallen, 21 August 2026. Conducted in cooperation with Teklens.
- Probst 2026, Appendix B · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Appendix B – Mean, standard deviation, median, minimum, maximum and item-rest correlation of all 19 items. Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: Maturity scale 1–5 · Limits: Self-assessment, exploratory, not representative.Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? An Empirical Analysis Using an Integrated TOE–DCT Framework. Master's thesis, University of St.Gallen, Institute of Information Systems and Digital Business. Supervisor: Prof. Dr. Ingrid Bauer-Hänsel; co-supervisor: Prof. Dr. Benjamin van Giffen. St. Gallen, 21 August 2026. Conducted in cooperation with Teklens.
- AI Monitor 2026, Europe · AI Monitor 2026 – European survey, raw export (2026). Recomputed from the raw export of 2026-08-19; documented in the internal editorial brief of 2026-08-18 (addenda). Sample: n = 165 valid responses, 106 with demographic detail; single items n = 163 · Scale: 1–5 per item, each response normalised to 0–100 · Limits: Self-assessment; no country field in the export; industry and size not reconciled. Role comparisons compare different people, not the same organisation.Teklens (2026). AI Monitor 2026 – European survey on organisational AI readiness, raw export of 19 August 2026. Research partners: ETH Zürich and University of St.Gallen. Internal aggregate, published on teklens.ai/ai-monitor.