Chapter 05
Research dimension: External Alignment
How well is your organisation aligned externally?
3 min readPublished Published by Teklens
An organisation is aligned externally when it has rules for responsible AI use, actively uses its partner network and can place the requirements of the EU AI Act. In the DACH analysis all three factors sit below 2.7 of 5. External alignment is the dimension on which the readiness profiles differ least: even advanced organisations reach only 3.58. The gap is systemic.
Contents · Short answer
Three findings
- 01
All three factors below 2.7
AI policy (Q13) 2.67, ecosystem integration (Q14) 2.66, EU AI Act (Q15) 2.63. Q13 and Q14 have a median of 2.5, the EU AI Act a median of 2.[Probst 2026, Appendix B]
- 02
The profiles barely separate here
The intermediate (2.44) and early-stage (2.43) profiles cannot be told apart statistically (Tukey p = .996). External Alignment has the weakest separation of all dimensions at F = 10.5; the advanced profile reaches 3.58, its only score below 4.[Probst 2026, Appendix C]
- 03
The EU AI Act shows the widest spread
Q15 has the widest spread of all 19 items at SD 1.28: from "major clarification needed" to "fully under control". Inside each profile the dimension spreads more widely than any other (SD 0.73 to 0.82).[Probst 2026, Table 5]
Evidence
- Q13 AI Policy2.67
- Q14 Ecosystem Integration2.66
- Q15 EU AI Act2.63
Select an item to see its wording, levels and statistics.
Takeaway: All three items sit below 2.7. The EU AI Act (Q15, 2.63) shows the widest spread of all 19 items (SD 1.28).
- 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, Appendix B · Appendix B – Mean, standard deviation, median, minimum, maximum and item-rest correlation of all 19 items
Data as a table
| Item | Dimension | M | SD | Median | Min | Max | r (item-rest) |
|---|---|---|---|---|---|---|---|
| Q13 AI Policy | Governance & ecosystem | 2.67 | 1.14 | 2.50 | 1 | 5 | 0.68 |
| Q14 Ecosystem Integration | Governance & ecosystem | 2.66 | 1.03 | 2.50 | 1 | 5 | 0.31 |
| Q15 EU AI Act | Governance & ecosystem | 2.63 | 1.28 | 2.00 | 1 | 5 | 0.48 |
Analysis
A systemic weakness
The External Alignment dimension combines AI policy, ecosystem integration and government regulatory issues and represents the environmental context of the TOE framework. At 2.65 it is the second-lowest dimension. The thesis reads the small differences between the profiles as a sign of a systemic rather than a company-specific deficit: organisations across all profiles struggle to reach high values here.[Probst 2026, Section 5.1]
Ecosystem integration is deliberately phrased actively. Instead of "competitive pressure" as a passive influence, Q14 asks how actively an organisation uses and shapes its partner network. Level 2, the median, reads "informal observation: trends and events are tracked ad hoc". Level 4 requires active work in committees and associations.[Probst 2026, Appendix A]
The European survey shows the same value for the ecosystem (2.64) and a slightly higher one for the EU AI Act (2.95, n = 165).[AI Monitor 2026, Europe]
What the spread means
The thesis interprets the low and widely spread EU AI Act values as widespread uncertainty and a lack of orientation. It derives a recommendation for policymakers: clearer, more accessible guidance so that regulation is perceived as orientation rather than as an obstacle. For organisations it recommends an active stance towards their environment, for instance through partnerships and participation in standard-setting.[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.
Our recommendation for product teams that do not set governance themselves but work with it every day.
Make accountability visible
People steer and approve. In every AI-assisted workflow, record who owns the result and what gets checked before approval. That is the operational core of level 4 on Q13: "binding requirements for safety, fairness and conformity; clear roles".
Name the operating constraints
Which data may go to which model? Where does it run? Who may automate what? Clear answers to these three questions save the team from renegotiating uncertainty in every decision.
The ecosystem as a source of knowledge
Level 3 on Q14 reads "fixed partnerships to adopt established standards". For a product team that can be a regular exchange with two teams solving the same problem.
Scope & limits
- EU AI Act values are self-assessments of implementation, not a conformity audit. They prove no legal status.
- The dimension has the lowest reliability at α = 0.663; Q14 relates only weakly to the other two items (r = 0.31).
- EU AI Act requirements vary strongly by use case and risk class; one item cannot capture that.
Questions & answers
Why do advanced organisations differ so little here?
The thesis suggests that the conditions lie outside the organisation: unclear regulation and thin networks. Internal strength compensates only partly.
Are organisations with high scores compliant with the EU AI Act?
The study cannot say. It measures how respondents rate the state of implementation, not whether an audit would confirm that rating.
What counts as the ecosystem?
External partners such as technology providers, universities, associations and committees with which an organisation reduces risks and co-creates standards.
Sources
- 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.
- 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, 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.
- 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.
- 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, 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.
- 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.