In short
A model for public bodies that looks at the readiness of the organization and of its people together. Use it as a structured list of what to check. Its weights come from expert opinion, not from data.
For whoever runs the assessment: an HR or operations lead, an internal project team, or an outside consultant.
What it measures
Most readiness models look at the organization only. This one joins two levels:
- The organization: its technology, how it is run, and the pressures and rules around it.
- The people: whether individual staff expect AI to help them, find it easy, and feel supported in using it.
The model gives each criterion a weight and combines them into a readiness assessment for a public organization.
How to use it
- Use the criteria as an agenda for a readiness discussion, with managers and staff in the room.
- Note where the organization looks ready and its people do not, or the other way round. That gap is common: in one survey of leaders, most used AI daily while their organizations were still in pilots.
- Do not publish the combined score as a rating of your organization.
What it does not measure
- Private companies. It was designed for the public sector.
- Staff behaviour. It asks about expectations, not about what people do.
How it was tested
It was not tested on data. Experts compared the criteria in pairs, and a standard method turned their comparisons into weights. The technical name for the method is the analytic hierarchy process.
That makes the model a reasoned opinion. It is useful as a checklist of what matters.
Licence and access
The article is open access under a CC BY licence, so you may adapt the model with credit. The number of questions a respondent would answer is not established.
Alternatives
Other assessments with a similar job, and when to prefer each.
- Does the company have what it needs to use AI? AI Capability scale Prefer it when you want a questionnaire that has been tested on real answers. Managers, about their own company · 46 questions Free to use and adapt
- Are staff committed to this change, and do they believe it can be done? Organizational Readiness for Implementing Change (ORIC) Prefer it when the question is whether staff are ready for this particular change. Staff, about their organization or team · 12 questions Not established
Practices that use it
- Do not treat a maturity score as a forecast An AI maturity or readiness score describes where you say you stand. Evidence that a higher score leads to better results is thin and disputed. Use it to start a discussion, not to predict returns. Do next · Effort: low (days, one person)
- Assess readiness from several sources, not one executive’s rating A readiness check filled in by one senior person reflects that person’s view. Ask staff and managers as well, and compare the answers with what you can observe. Do next · Effort: medium (weeks, a few people)
The details
- Testing
- The weights in the model come from experts’ judgements. No published study has tested whether its scores measure readiness.
- Looks at
- Organization, Person
- How the answers are collected
- Expert judgement. Experts compare the criteria in pairs to set their weights.
- Length
- 4 categories of criteria. The number of questions is not established.
- Cost
- Free
- Licence
- CC BYYou may translate and adapt the model if you credit the authors.
- Languages
- English. The model was developed in Slovenia.
- By
- Hržica, Debelak and Pevcin, 2025
Sources
- Hržica, Debelak and Pevcin (2025). A Dual-Level Model of AI Readiness in the Public Sector: Merging Organizational and Individual Factors Using TOE and UTAUT. Systems. doi:10.3390/systems13080705
Related studies on this site
- Leaders use AI daily while their organizations stay in pilots Wojewodzic (2026). AI Transformation in Polish Organisations: The Gap Between Individual Adoption and Organisational Maturity. e-mentor, 2(114), 75–83.
- Whose view does a digital maturity score reflect? Silva, São Mamede and Santos (2024). A new proposed model to assess the digital organizational readiness to maximize the results of the digital transformation in SMEs. Journal of Innovation & Knowledge, 10(1), 100644.