How was AI readiness measured?

Evidence

Analysis

Population and collection

The thesis purposively surveyed people with strategic exposure to AI initiatives: C-level executives, heads of AI and product leads. About 2,000 people across DACH were contacted via LinkedIn and email, drawing on the network of Teklens, which hosted the survey platform and ran the outreach; participants could invite further people. 118 people answered at least the first three items.[Probst 2026, Table 2]

Four acceptance criteria reduced the 118 to 64: complete answers, complete and plausible contextual data (cross-checked against LinkedIn profiles among other things), an organisation based in DACH, and a completion time of at least 4.13 minutes, half the median of 8.25 minutes.[Probst 2026, Figure 3]

Instrument and scale

The questionnaire operationalises 19 factors: 15 from the TOE framework after Ali and Khan (2025), four dynamic capabilities after Dey and Ghose (2025). Each item has five maturity levels. After a pilot with three practitioners, anchor texts were shortened; seven items carry level labels only, and every dimension keeps at least two fully anchored items. The questionnaire was available in German and English.[Probst 2026, Appendix A]

A dimension score is the mean of its items. The European survey converts each answer to 0 to 100 with (x − 1) / 4 × 100 and averages per dimension. Where this edition shows DACH values on 0–100, they are converted from the published three-decimal dimension means, not from individual responses; the figures concerned say so.

Reliability

Cluster analysis

K-means on the six z-standardised dimension scores, ten random starts, fixed seed, computed in jamovi 2.7 with the snowCluster module. The number of clusters was determined with the elbow criterion, the silhouette coefficient and the Krzanowski–Lai index. Criterion validity was checked via depth of AI adoption (Q23), which was not part of the clustering.[Probst 2026, Section 4.4]

The one-way ANOVAs across the six dimensions describe how sharply the clusters separate on the clustering variables themselves; they are not a hypothesis test. Levene's test was non-significant on all dimensions, so Fisher's ANOVA with Tukey HSD was applied. No significance tests were run for cluster composition by industry, size and level, on purpose.[Probst 2026, Appendix C]

Two datasets, one survey

The DACH analysis (n = 64) and the European survey (n = 165, 106 of them with demographic detail) come from the same survey platform and the same outreach. The thesis applied its own four filters. Whether each of the 64 responses sits among the 165 cannot be checked from the export, which carries no country field. We therefore treat the two as differently filtered analyses of one survey: not disjoint, not subtractable, and never computed as "Europe excluding DACH".[AI Monitor 2026, Europe]

The European dimension scores and hierarchical levels were recomputed from the raw export on 19 August 2026. Industry and size segments come from an enrichment the export does not carry; they are not reconciled and are not shown in this edition.[AI Monitor 2026, Europe]

Presentation rules of this edition

• Small segments: interactive comparisons show no segments below five cases and flag segments below ten. Published tables of the thesis are reproduced as reported; cells below five are flagged and not interpreted.
• Rounding: published values are shown as in the source (two decimals). Conversions to 0–100 use the three-decimal values from the thesis's figure scripts. One example of a rounding difference: 25 of 64 recomputes to 39.1%; the thesis publishes 39.0%, and we show the published value.
• Language: study findings, Teklens interpretations and Teklens recommendations are labelled separately in every chapter. Associations are not causes; the study measures neither impact, time savings, returns nor legal compliance.
• Profile labels: the thesis's Leaders, Followers and Laggards are called advanced profile, intermediate profile and early stage here.

Authorship and partners

The DACH analysis is Felix Probst's master's thesis at the Institute of Information Systems and Digital Business of the University of St.Gallen (2026), supervised by Prof. Dr. Ingrid Bauer-Hänsel and Prof. Dr. Benjamin van Giffen and written in cooperation with Teklens. Research authorship rests with the author of the thesis. The editorial presentation of this edition, the foreword, the interpretations and the recommendations are by Teklens. The thesis is not publicly available; it is cited bibliographically.[Probst 2026]

ETH Zurich and the University of St.Gallen are research partners on the AI Monitor. They are not Teklens reference customers, and nothing in this edition implies an endorsement of Teklens by either university.

Behind the study

Who runs the AI Monitor

Published by Teklens, in collaboration with the research partners University of St. Gallen (HSG) and ETH Zurich.

Kathrin Wolff

Research partner

Prof. Dr. David Finken

Professor of Marketing × Technology (TUM) and Senior Research Affiliate (ETH Zurich)

Marc Gasser

Teklens

Simon Scheurer

Teklens

Take the AI Readiness Check

Scope & limits

• The sample of 64 is the most important limitation: sufficient for an exploratory assessment, too small for robust subgroup comparisons.
• The DACH label comes from the title of the thesis; geographically the sample covers Germany and Switzerland only.
• Self-assessments are subject to social desirability and self-enhancement; the concentration of top executives in the advanced profile (10 of 12) underlines that.
• The questionnaire is deliberately general; sector-specific nuances and slight differences in meaning between the German and English versions cannot be ruled out.

Questions & answers

Sources

1. Probst 2026 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Whole thesis. 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.
2. Probst 2026, Table 2 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Table 2 – Characteristics of the DACH sample (Section 4.1). Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: Frequencies · 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.
3. Probst 2026, Table 3 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Table 3 – Reliability of the six dimensions (Section 4.2). Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: Cronbach's α and McDonald's ω · Limits: Three to four items per dimension; α ≥ 0.6 is treated as acceptable in exploratory research.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.
4. Probst 2026, Figure 3 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Figure 3 – Four filtering steps from 118 to 64 responses (Section 4.1). Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: Number of responses · 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.
5. Probst 2026, Figure 6 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Figure 6 – Elbow, silhouette and KL index for the number of clusters (Section 4.4.1). Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: Cluster indices · Limits: Two and three clusters were both statistically viable; three were chosen for interpretability.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.
6. Probst 2026, Section 4.4 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Section 4.4 – Cluster analysis, criterion validity and composition. 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.
7. Probst 2026, Appendix A · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Appendix A – Full survey instrument (19 items, five levels). Sample: Instrument, no sample · Scale: Maturity scale 1–5 · Limits: Seven of the 19 items carry level labels only, without a written behavioural anchor.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.
8. Probst 2026, Appendix C · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Appendix C – One-way ANOVA and Tukey HSD per dimension. Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: F values, df (2, 61), p · Limits: Describes how sharply the clusters separate on the clustering variables themselves; not a hypothesis test.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.
9. Probst 2026, Appendix D · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Appendix D – External validation of the profiles via depth of AI adoption (Q23). Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: Composite 4–20 across four business functions · Limits: Q23 was not part of the clustering; association, not causation.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.
10. 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.
11. Ali & Khan 2025 · Factors influencing readiness for artificial intelligence: A systematic literature review (2025). Data Science and Management, 8(2). Sample: 52 reviewed studies · Scale: – · Limits: Literature review; provides the 15 TOE factors of the framework.Ali, W., & Khan, A. Z. (2025). Factors influencing readiness for artificial intelligence: A systematic literature review. Data Science and Management, 8(2), 224–236. https://doi.org/10.1016/j.dsm.2024.09.005
12. Dey & Ghose 2025 · Integrating AI adoption frameworks with digital maturity models (2025). Journal of Contemporary Business Research, 1(2). Sample: Conceptual · Scale: – · Limits: Provides the four factors of the Dynamic Capabilities dimension.Dey, D., & Ghose, D. (2025). Integrating AI adoption frameworks with digital maturity models: Strategic pathways in a rapidly evolving technological landscape. Journal of Contemporary Business Research, 1(2), 156–170. https://doi.org/10.1177/3049513X251387463