AI Monitor 2026
What does AI readiness mean?
Page 2 of 123 min readPublished Published by Teklens
Organisational AI readiness is the degree to which an organisation holds the structural preconditions for AI and can keep adapting them. The AI Monitor 2026 measures it across six dimensions with 19 factors: five foundations from technology, organisation and environment, plus adaptability from dynamic capabilities theory. Every item is answered on a maturity scale from 1 to 5.
Contents · Short answer
Three findings
- 01
Two of six dimensions above the scale midpoint
In the DACH analysis only Internal Assets (3.38) and Leadership (3.26) exceed 3. Tech Foundation (2.86), Data Engine (2.80), External Alignment (2.65) and Dynamic Capabilities (2.61) stay below.[Probst 2026, Table 4]
- 02
The foundations are more developed than the movement
The two organisational dimensions lead, the dynamic capabilities dimension comes last. A framework built from TOE factors alone would have left this gap invisible.[Probst 2026, Section 5.1]
- 03
All six dimensions measure consistently
McDonald's ω ranges from 0.70 to 0.87. The weakest values belong to Internal Assets and External Alignment, whose items cover the widest ground.[Probst 2026, Table 3]
Evidence
- People & capabilitiesInternal Assets3.38
- LeadershipLeadership3.26
- Technology foundationsTech Foundation2.86
- DataData Engine2.80
- Governance & ecosystemExternal Alignment2.65
- AdaptabilityDynamic Capabilities2.61
Takeaway: Only Internal Assets and Leadership sit above the scale midpoint of 3. Dynamic Capabilities and External Alignment bring up the rear.
- 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, Table 4 · Table 4 and Figure 4 – Means and standard deviations of the six dimensions (Section 4.3)
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 |
Analysis
Where the framework comes from
The AI Monitor 2026 combines two perspectives. The TOE framework by Tornatzky and Fleischer has described since 1990 which technological, organisational and environmental conditions shape the adoption of a technology. Dynamic capabilities theory after Teece explains how organisations sense opportunities, seize them and reconfigure their structures.[Tornatzky & Fleischer 1990]
The 15 structural factors come from the systematic literature review by Ali and Khan (2025), the four dynamic factors from Dey and Ghose (2025). The author of the thesis mapped the factors onto six dimensions. Firm size is deliberately a contextual variable, not a factor.[Ali & Khan 2025]
The six dimensions and their 19 factors
The table lists the research labels and the definition of each factor. The chapters of this edition carry their own titles for readability; each chapter's subtitle names the research dimension.[Probst 2026, Table 1]
| Dimension | Factor | Definition |
|---|---|---|
| LeadershipLeadership · Organisation (TOE) | Q1 Top Management Support | Senior leadership's willingness to initiate AI projects, make critical decisions, and foster an innovation-enabling environment. |
| Q2 Resource Availability | The presence and accessibility of internal and external tools, materials, and technical and financial assets required for AI adoption. | |
| Q3 Vision & Strategy | The existence of achievable AI goals that outline an organisation's expectations, combined with a strategic plan to guide AI adoption. | |
| People & capabilitiesInternal Assets · Organisation (TOE) | Q4 AI Talent | The AI-specific expertise an organisation possesses to effectively deploy AI solutions. |
| Q5 Collaborative Culture | The extent of trust, information sharing, and intentional knowledge exchange at the departmental level to achieve shared goals. | |
| Q6 Innovativeness | Employees' current commitment and positive attitudes towards change to successfully drive AI adoption. | |
| Technology foundationsTech Foundation · Technology (TOE) | Q7 IT Infrastructure | The hardware, software, networking, and computing resources that provide the technical foundation required to support AI-related workloads, data storage, and processing. |
| Q8 Compatibility | How well AI systems and technologies fit into an organisation's existing setup, processes, and objectives. | |
| Q9 IT Architecture | The structural design and segmentation of an organisation's IT infrastructure, enabling the modular and efficient integration of new AI applications. | |
| DataData Engine · Technology (TOE) | Q10 Data Accessibility | The ability to promptly and effortlessly access numerous data sources, enabling AI specialists to create prototypes and develop AI solutions. |
| Q11 Data Quality | The degree of data completeness and accuracy reflected in organisational proficiency in data preparation, processing, and quality assurance. | |
| Q12 Data Governance | The overall framework and data management controls an organisation must have in place for AI adoption. | |
| Governance & ecosystemExternal Alignment · Environment (TOE) | Q13 AI Policy | Organisational rules and guidelines ensuring ethical and responsible AI usage while addressing legal limitations. |
| Q14 Ecosystem Integration | The degree to which an organisation is embedded in external networks and corporate partnerships that drive and support AI adoption. | |
| Q15 EU AI Act | Government policies, limitations, and laws organisations must monitor and comply with during AI adoption (e.g., EU AI Act). | |
| AdaptabilityDynamic Capabilities · Dynamic Capabilities (DCT) | Q16 Sensing | Systematically scanning the environment for relevant AI trends, algorithmic advances, regulatory changes, and emerging customer demands. |
| Q17 Seizing | Mobilising resources and initiating funded pilot AI projects to capitalise on identified opportunities. | |
| Q18 Reconfiguring (Structures) | Reassessing existing assets, structures, and processes to integrate AI into operational and strategic decision-making, including legacy retirement. | |
| Q19 Reconfiguring (Learning) | Institutionalising systems and practices that enable employees to continuously develop skills required for sustained AI adoption. |
How it is measured
Each factor is an item with five maturity levels. Most levels describe concrete behaviour, such as "AI pilots are officially approved; however, AI is not yet a central part of the strategy" for level 3 of top-management support. That narrows the room abstract agreement scales leave. After the pilot, seven of the 19 items carry level labels only.[Probst 2026, Appendix A]
A dimension score is the mean of its items. The European survey converts the same answers to 0 to 100 with (x − 1) / 4 × 100. A value of 50 means "level 3 on average", not "half of the organisations".
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.
We read the framework as a checklist for a single initiative, not as a maturity certificate.
Pick one workflow
Take one concrete workflow, say from customer feedback to an approved spec, and check it along the six dimensions.
Separate foundation from movement
Is budget, access or knowledge missing? Or the routine of adapting the workflow after every run? Each needs different measures.
Self-assessment as a conversation starter
Have product, engineering and leadership answer the same questions. The difference between the answers is often more telling than the mean.
Scope & limits
- The framework is theory-led, but the factor mapping is a decision by the thesis author, not an empirically tested structure.
- The items are deliberately general so they work across sectors. They do not capture sector-specific nuances.
- Maturity levels are self-assessments. They measure perceived, not audited, readiness.
Questions & answers
Is AI readiness the same as AI maturity?
Not quite. Maturity models describe stages an organisation passes through. Readiness asks whether the preconditions for the next step are in place. The AI Monitor uses maturity levels as the measuring instrument for readiness.
Why six dimensions rather than three?
The TOE framework has three contexts. The thesis splits technology and organisation into two dimensions each, keeps the environmental context as one and adds dynamic capabilities as the sixth. That keeps every dimension measurable with at least three factors.
Can I assess my own team with this framework?
Yes, for orientation. The AI readiness assessment on teklens.ai asks twelve of the 19 items and compares each answer with the study value. It is not a statistically validated profile assignment.
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 1, Table 3, Table 4 and Figure 4, Appendix A
- Ali & Khan 2025 · Ali, W., & Khan, A. Z. (2025). Factors influencing readiness for artificial intelligence: A systematic literature review. Data Science and Management, 8(2), 224–236. doi.org/10.1016/j.dsm.2024.09.005
- Dey & Ghose 2025 · 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. doi.org/10.1177/3049513X251387463
- Jöhnk et al. 2021 · Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or not, AI comes – An interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63(1), 5–20. doi.org/10.1007/s12599-020-00676-7
- Teece 2007 · Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. doi.org/10.1002/smj.640
- Tornatzky & Fleischer 1990 · Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
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