In short
A 46-question survey of what a company has in place to use AI: data, technology, skills and the ability to change. Use it to see where leaders, IT and operations agree and where they do not.
For whoever runs the assessment: an HR or operations lead, an internal project team, or an outside consultant.
What it measures
The scale asks how far a company has what it needs to use AI. It covers eight areas in three groups:
- Things you can buy or build: data, technology, and basic resources such as budget and time.
- People: technical skills and business skills.
- How the organization works: coordination between departments, the capacity to change, and willingness to take risks.
It is the best-tested open questionnaire in this section that looks at the whole organization and not only at its technology.
How to use it
- Give it to several people in the company, not one: a senior leader, someone from IT or data, and someone from operations or HR.
- Collect the answers separately.
- Put them side by side. Where the groups disagree is the useful part.
- Report the spread of answers for each area, not only the average.
The original study asked one executive per company. That is the weakness a later study of maturity models warns about: one senior view is an opinion, not a measurement.
What it does not measure
- Staff attitudes or behaviour. The questions about change and risk record what managers say about the company. They do not show what staff do.
- Results. A high score does not forecast returns. See do not treat a maturity score as a forecast.
- What AI could do in each job. For that, use a task map.
It is also not short. Forty-six questions take time, and managers skim long forms.
How it was tested
- The authors built the questions from earlier research and tested them on 143 senior executives in the United States. The answers grouped into the eight areas as intended. The study is described on its own page.
- The lead author then studied AI capability in 91 municipalities in Germany, Norway and Finland, and in a second study of 168 municipalities.
The technical detail: the first study checked the measure with standard statistical tests of survey structure. All three studies share a lead author.
Licence and access
The questions are printed in the article, which is open access under CC BY 4.0. You may translate and adapt them with credit to the authors.
Alternatives
Other assessments with a similar job, and when to prefer each.
- Are a public body and its people both ready for AI? Dual-level model of AI readiness in the public sector Prefer it in a public body, or when you want the readiness of the organization and of its people in one model. It rests on expert judgement only. Managers and staff, in a discussion · 4 groups of criteria. The number of questions is not established Free to use and adapt
- What makes this technology project hard? NASSS-CAT: complexity assessment tools for technology projects Prefer it when you want a discussion and an action list, not a score. The project team, and staff in interviews · Two checklists, a 35-question form and an interview guide Free to use, ask before changing
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
- Built and tested on 143 senior executives in the United States, and the results held up. The same lead author later measured AI capability in two studies of European municipalities. No independent team has yet repeated the test.
- Looks at
- Organization
- How the answers are collected
- A survey answered by managers about their own company. The original study asked one executive per company.
- Length
- 46 questions in 8 groups
- Cost
- Free
- Licence
- CC BY 4.0You may translate, adapt and reprint the questions if you credit the authors. No permission is needed.
- Languages
- English
- By
- Mikalef and Gupta, 2021
Sources
- Mikalef and Gupta (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management. doi:10.1016/j.im.2021.103434
- Mikalef, Lemmer, Schaefer, Ylinen and colleagues (2021). Enabling AI capabilities in government agencies: A study of determinants for European municipalities. Government Information Quarterly. doi:10.1016/j.giq.2021.101596
- Mikalef, Lemmer, Schaefer, Ylinen and colleagues (2023). Examining how AI capabilities can foster organizational performance in public organizations. Government Information Quarterly. doi:10.1016/j.giq.2022.101797
Related studies on this site
- AI capability and company performance: a survey of 143 executives Mikalef and Gupta (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434.
- 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.
- Ten years of digital maturity models: do they predict results? Thordsen and Bick (2023). A decade of digital maturity models: much ado about nothing?. Information Systems and e-Business Management, 21(4), 947–976.