AI Monitor 2026
What the AI Monitor 2026 shows
4 min readPublished Published by Teklens
Organisations in the DACH analysis are more open to AI than they are organisationally prepared for it. Employees' openness reaches 3.94 of 5, adapting structures and processes 2.47. Between those two values sit 17 more items, and eight of the nine lowest belong to external alignment, adaptability and data. Only about one organisation in five belongs to the advanced profile.
Contents · Short answer
Three findings
- 01
Openness at the top, adaptation at the bottom
Q6 openness to change reaches 3.94 with the smallest spread of all items (SD 0.73); nobody chose level 1. Q18 adapting structures and processes sits at 2.47 with a median of 2.[Probst 2026, Appendix B]
- 02
About one organisation in five is advanced
12 of 64 organisations (18.8%) form the advanced profile, 27 (42.2%) the intermediate one, 25 (39.0%) the early stage. Adaptability separates the three most sharply (F = 83.1).[Probst 2026, Table 5]
- 03
External alignment is weak everywhere
External Alignment, at 2.65, is the second-lowest dimension and the only one where the intermediate (2.44) and early-stage (2.43) profiles cannot be told apart. The EU AI Act (Q15) shows the widest spread of all items (SD 1.28).[Probst 2026, Appendix C]
Evidence
- Q18 Reconfiguring (Structures)Adaptability2.47
- Q11 Data QualityData2.61
- Q15 EU AI ActGovernance & ecosystem2.63
- Q16 SensingAdaptability2.63
- Q19 Reconfiguring (Learning)Adaptability2.64
- Q8 CompatibilityTechnology foundations2.66
- Q14 Ecosystem IntegrationGovernance & ecosystem2.66
- Q13 AI PolicyGovernance & ecosystem2.67
- Q7 IT InfrastructureTechnology foundations2.70
- Q17 SeizingAdaptability2.72
- Q12 Data GovernanceData2.73
- Q10 Data AccessibilityData3.05
- Q5 Collaborative CulturePeople & capabilities3.08
- Q3 Vision & StrategyLeadership3.09
- Q2 Resource AvailabilityLeadership3.11
- Q4 AI TalentPeople & capabilities3.11
- Q9 IT ArchitectureTechnology foundations3.22
- Q1 Top Management SupportLeadership3.56
- Q6 InnovativenessPeople & capabilities3.94
Select an item to see its wording, levels and statistics.
Takeaway: Openness to change (Q6, 3.94) is the highest score, adapting structures and processes (Q18, 2.47) the lowest. Eight of the nine lowest scores come from External Alignment, Dynamic Capabilities and Data Engine.
- Unit
- Mean on the maturity scale 1 (least developed) to 5 (most advanced)
- Population
- DACH analysis: 64 cleaned self-assessments from Germany and Switzerland, mostly senior decision-makers
- Denominator
- n = 64 organisations
- Source
- Probst 2026, Figure 5 · Figure 5 and Appendix B – Means of the 19 items (Section 4.3)
Data as a table
| Item | Dimension | M | SD | Median | Min | Max | r (item-rest) |
|---|---|---|---|---|---|---|---|
| Q18 Reconfiguring (Structures) | Adaptability | 2.47 | 1.13 | 2.00 | 1 | 5 | 0.69 |
| Q11 Data Quality | Data | 2.61 | 1.11 | 2.00 | 1 | 5 | 0.66 |
| Q15 EU AI Act | Governance & ecosystem | 2.63 | 1.28 | 2.00 | 1 | 5 | 0.48 |
| Q16 Sensing | Adaptability | 2.63 | 1.19 | 2.00 | 1 | 5 | 0.70 |
| Q19 Reconfiguring (Learning) | Adaptability | 2.64 | 1.12 | 3.00 | 1 | 5 | 0.74 |
| Q8 Compatibility | Technology foundations | 2.66 | 1.14 | 3.00 | 1 | 5 | 0.75 |
| Q14 Ecosystem Integration | Governance & ecosystem | 2.66 | 1.03 | 2.50 | 1 | 5 | 0.31 |
| Q13 AI Policy | Governance & ecosystem | 2.67 | 1.14 | 2.50 | 1 | 5 | 0.68 |
| Q7 IT Infrastructure | Technology foundations | 2.70 | 1.14 | 3.00 | 1 | 5 | 0.82 |
| Q17 Seizing | Adaptability | 2.72 | 1.13 | 2.00 | 1 | 5 | 0.70 |
| Q12 Data Governance | Data | 2.73 | 1.03 | 3.00 | 1 | 5 | 0.62 |
| Q10 Data Accessibility | Data | 3.05 | 1.03 | 3.00 | 1 | 5 | 0.50 |
| Q5 Collaborative Culture | People & capabilities | 3.08 | 1.15 | 3.00 | 1 | 5 | 0.62 |
| Q3 Vision & Strategy | Leadership | 3.09 | 1.03 | 3.00 | 1 | 5 | 0.78 |
| Q2 Resource Availability | Leadership | 3.11 | 1.27 | 3.00 | 1 | 5 | 0.77 |
| Q4 AI Talent | People & capabilities | 3.11 | 1.10 | 3.00 | 1 | 5 | 0.59 |
| Q9 IT Architecture | Technology foundations | 3.22 | 1.03 | 3.00 | 1 | 5 | 0.66 |
| Q1 Top Management Support | Leadership | 3.56 | 1.01 | 4.00 | 1 | 5 | 0.71 |
| Q6 Innovativeness | People & capabilities | 3.94 | 0.73 | 4.00 | 2 | 5 | 0.32 |
Analysis
How the 19 items rank
The ranking of the items follows the ranking of the dimensions. All three External Alignment items and three of the four Dynamic Capabilities items are among the eight lowest scores, together with compatibility (Q8, 2.66) and data quality (Q11, 2.61). The highest scores come from the organisational dimensions: openness (Q6, 3.94) and top-management support (Q1, 3.56).[Probst 2026, Figure 5]
The distributions tell more than the means. Three of the four adaptability items have a median of 2 although their means sit between 2.47 and 2.72: most answers sit at the lower end, a few high ones pull the mean up. Answers on the EU AI Act spread the widest, a sign of uncertainty rather than a shared assessment.[Probst 2026, Appendix B]
Two analyses, one pattern
The AI Monitor's European survey holds 165 valid responses and converts the scale to 0 to 100. There too, openness (4.01) is the highest and adapting processes (2.59) the lowest score; 59% of answers to that item sit at level 1 or 2. The DACH analysis sits slightly below the European value on every dimension.[AI Monitor 2026, Europe]
- LeadershipLeadership58.0 · 56.4
- People & capabilitiesInternal Assets59.6 · 59.4
- Technology foundationsTech Foundation49.3 · 46.5
- DataData Engine49.9 · 44.9
- Governance & ecosystemExternal Alignment44.1 · 41.3
- AdaptabilityDynamic Capabilities45.6 · 40.3
Takeaway: Normalised to 0–100, the DACH analysis sits below the European value on every dimension, most clearly on Data Engine (45.0 versus 49.9) and Dynamic Capabilities (40.3 versus 45.6). Both analyses come from the same survey and are filtered differently.
- Unit
- 0–100, converted from the 1–5 scale with (x − 1) / 4 × 100. A value is a maturity score, not a share of organisations.
- Population
- Europe: 165 valid responses to the AI Monitor survey. DACH: 64 cleaned responses of the thesis.
- Denominator
- n = 165 and n = 64
- Source
- AI Monitor 2026, Europe · Recomputed from the raw export of 2026-08-19; documented in the internal editorial brief of 2026-08-18 (addenda)
The DACH values are converted from the three-decimal dimension means, not from individual responses. A subset relationship cannot be verified; the two lines are not disjoint.
Data as a table
| Dimension | Europe (n = 165) | DACH analysis (n = 64) | Gap |
|---|---|---|---|
| Leadership | 58.0 | 56.4 | -1.6 |
| People & capabilities | 59.6 | 59.4 | -0.2 |
| Technology foundations | 49.3 | 46.5 | -2.8 |
| Data | 49.9 | 44.9 | -5.0 |
| Governance & ecosystem | 44.1 | 41.3 | -2.8 |
| Adaptability | 45.6 | 40.3 | -5.3 |
What the thesis concludes
The thesis describes the state of AI readiness as two-sided: many organisations hold solid organisational foundations, such as an open workforce, project-related resources and tangible leadership engagement. The remaining dimensions are less developed, most clearly the ability to sense, decide and reconfigure around AI, and the handling of regulation and ecosystem.[Probst 2026, Section 5.1]
Teklens interpretation
For your product team
The study describes organisations across sectors. Applying it to software product teams is our interpretation, not an empirical finding about product teams.
Three questions a product team can derive from these findings.
What changes after rollout?
Once an AI tool is in place: which role, step or approval was adapted afterwards? If the answer is "nothing", you sit at level 1 or 2 on Q18.
Where does data quality come from?
Every Activity inherits the quality of its sources. A ticket built on outdated specs does not improve through automation.
Who owns the external conditions?
Policy, partners and regulation rarely sit with the product team. Name who clarifies them so uncertainty is not renegotiated in every decision.
Scope & limits
- 64 responses support an exploratory assessment, not a representative benchmark of the DACH region.
- Self-assessments are subject to social desirability; 10 of the 12 advanced organisations were rated by the top level.
- The European values come from an internal recomputation of the raw export. Industry and firm size are not reconciled there and are not shown.
Questions & answers
Which item scores highest?
Q6, employees' openness to change through AI, at 3.94 of 5 in the DACH analysis and 4.01 in the European survey.
Which item scores lowest?
Q18, adapting workflows, responsibilities and AI solutions to changed requirements, at 2.47 of 5 in the DACH analysis and 2.59 in the European survey.
Are values available separately for Germany and Switzerland?
No. The thesis analyses the 64 responses together. Splitting by country would leave the sample too small at 18 German responses.
Sources
- Probst 2026, Figure 5 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Figure 5 and Appendix B – Means of the 19 items (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, 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.
- 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 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.
- Probst 2026, Section 5.1 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Section 5.1 – Interpretation of key findings. Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: Maturity scale 1–5 · Limits: Interpretation by the thesis; no causal claim.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.