Chapter 02
Research dimension: Internal Assets
What capabilities does your team need for AI?
Page 5 of 123 min readPublished Published by Teklens
A team needs three things: people who can build and run AI products, collaboration between product, IT, data science and business units, and the willingness to change the status quo. The DACH analysis shows the willingness is there: openness reaches 3.94 of 5, the highest value of all 19 items. AI talent (3.11) and collaboration (3.08) sit well below.
Contents · Short answer
Three findings
- 01
Openness is the strongest score in the study
Q6 reaches 3.94 with a median of 4 and the smallest spread of all items (SD 0.73). It is the only item where nobody chose level 1.[Probst 2026, Appendix B]
- 02
Talent and collaboration lag behind
Q4 AI talent sits at 3.11, Q5 collaboration at 3.08, both with a median of 3. Level 3 reads "initial specialist roles" and "interface model with formal information flow".[Probst 2026, Appendix A]
- 03
The strongest dimension, even in the early stage
Internal Assets, at 3.38, is the highest dimension. The early stage reaches 2.73 here, its best value; the advanced profile 4.53.[Probst 2026, Table 5]
Evidence
- Q4 AI Talent3.11
- Q5 Collaborative Culture3.08
- Q6 Innovativeness3.94
Select an item to see its wording, levels and statistics.
Takeaway: Openness (Q6, 3.94) is the highest score of all 19 items. AI talent (Q4, 3.11) and collaboration (Q5, 3.08) sit well below it.
- 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) |
|---|---|---|---|---|---|---|---|
| Q4 AI Talent | People & capabilities | 3.11 | 1.10 | 3.00 | 1 | 5 | 0.59 |
| Q5 Collaborative Culture | People & capabilities | 3.08 | 1.15 | 3.00 | 1 | 5 | 0.62 |
| Q6 Innovativeness | People & capabilities | 3.94 | 0.73 | 4.00 | 2 | 5 | 0.32 |
Analysis
Willingness is not capability
The Internal Assets dimension combines AI talent, collaborative culture and innovativeness. The high mean of 3.38 comes mostly from openness. Without Q6, the other two factors would sit at the level of the leadership and data items.[Probst 2026, Table 4]
The thesis points to a peculiarity: Q6 has the second-lowest item-rest correlation of all items at 0.32. Openness relates only weakly to talent and collaboration. Organisations can be open without holding the capabilities, and vice versa. That also explains the comparatively low reliability of the dimension (α = 0.677, ω = 0.702).[Probst 2026, Appendix B]
The European survey confirms the openness at 4.01 of 5 (n = 165).[AI Monitor 2026, Europe]
What the levels look like
On Q4, level 4 describes "interdisciplinary product teams (PM, UX, AI) that build complex AI products largely internally". On Q5, level 4 reads "integrated product teams: product management leads cross-functional teams, structured exchange from idea to go-live". At the median, half of the organisations fall short of both.[Probst 2026, Appendix A]
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 when openness is there and the capabilities are missing.
Build capability on a real task
Instead of a general training programme: one concrete Activity, from task to approval, with the people who will own it later.
Shared memory instead of handovers
An interface model (level 3 on Q5) hands requirements over sequentially. A shared context that keeps decisions, tickets and code connected makes many handovers unnecessary. That is what we build the Product Brain for, a shared memory for people and agents.
Do not overrate openness
An open team is a good starting point, but not evidence of readiness. Check talent and collaboration against outcomes, not against mood.
Scope & limits
- Openness is rated by leaders about their employees, not by the employees themselves.
- Q6 measures attitude, not behaviour. A high value says nothing about whether AI is actually used day to day.
- The dimension has the second-lowest reliability; its three factors do not measure the same thing.
Questions & answers
Is openness enough to get started with AI?
To start, yes; to sustain, no. The data show open organisations with little talent and formal collaboration. Without capabilities it stays at pilots.
Do we have to hire data scientists?
The Q4 scale runs from external dependency through single specialist roles to interdisciplinary product teams. What matters is that knowledge stays in the team, not which job titles carry it.
What exactly does "collaborative culture" measure?
How collaboration between product management, IT, data science and business units is organised for AI use cases, from isolated domain work to a lived culture of exchange.
Sources
The DACH analysis comes from Felix Probst's master's thesis (University of St.Gallen, 2026), which focuses on Germany and Switzerland and was written in collaboration with Studyond and Teklens. Teklens and readyai.one ran the European survey as the pre-study for the Swiss AI Monitor; Teklens, as publisher, provides the interpretation. Full citations and the locator index: Methodology & sources.
- Probst 2026 · Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? Master's thesis, University of St.Gallen, in cooperation with Teklens. Germany and Switzerland, n = 64, maturity scale 1–5, self-assessment, exploratory.Locators used: Table 3, Table 4 and Figure 4, Table 5 and Figure 7, Appendix A, Appendix B
- AI Monitor 2026, Europe · Teklens & readyai.one (2026). AI Monitor 2026 – European survey on organisational AI readiness (pre-study for the Swiss AI Monitor), raw export of 19 August 2026. Research partners: ETH Zürich and University of St.Gallen. Internal aggregate, published on teklens.ai/ai-monitor.
PDF edition
The whole AI Monitor as a PDF
Every chapter, figure and source in one file. Download in exchange for your email address.
We email you the link and occasionally get in touch about the AI Monitor. No sharing, unsubscribe any time. Privacy