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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.

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When Do next. Once your first uses of AI are running.
Effort Medium (weeks, a few people)
Evidence Emerging

Four studies using different methods point the same way: one person’s rating is a poor guide. None is a rigorous test, and two are about digital skills in general. No study has compared decisions made from one source with decisions made from several.

Fits
Industries All industries
In the guide Mid-market companies

What it is

Most AI readiness checks are a questionnaire that one executive fills in. Collect three kinds of answer instead: what leaders say, what staff say, and what you can observe or test.

What the evidence shows

  • Most models record the view from the top. Researchers note that many maturity models mainly record the views of senior management, not measurable outcomes.
  • Views differ by level. At one financial company, employees and supervisors described the same people’s digital skills differently.
  • Self-ratings miss. In a study of 629 students, self-rated digital skills differed from test scores beyond chance. Some people rated themselves too high, some too low.
  • Personal use is not company readiness. In a survey of 179 leaders in Poland, 83% used AI daily, while about 70% of their organizations had not moved beyond pilots. Leaders who knew more about AI rated their organizations more harshly.
  • What the company can do counts more than its size. KfW, Germany’s state development bank, asked about 6,800 small and medium-sized firms whether they used AI. Once the analysis took account of graduates employed, innovation, research and a digital strategy, size and sector mattered little. A firm with no graduates and no innovation had an 8% probability of using AI. A firm with continuous research and development had 38%. In the European Central Bank’s survey of 5,067 euro-area firms, 41% of micro firms used no AI, against 13% of large firms. But about 7–8% of firms made significant use of AI at every size. Assess what the company can do, not how big it is.

The last finding matters most for the person filling in the form. An executive who uses AI every day may assume the company is further along than it is. One who knows little may not see the gaps.

How to do it

  1. Choose a readiness questionnaire. Any reasonable one will do. The AI Capability scale is open and has been tested.
  2. Have at least three groups answer it separately: senior leaders, middle managers and front-line staff.
  3. Add facts that do not depend on opinion: how many AI uses are in daily operation, how many staff completed training, test results, and whether the company runs innovation projects or has a digital strategy.
  4. Put the answers side by side. Look at where the groups disagree.
  5. Discuss the gaps with all three groups in the room. The gaps are the finding.
  6. Repeat after six to twelve months.

What to measure

  • The gap between groups on each question.
  • The gap between what people say and the facts you collected.

Where it fails

  • Averaging the answers. An average hides the disagreement, which is the useful part.
  • Too much surveying. Keep it short. A long form answered carelessly is worse than a short one answered honestly.
  • No safe way to disagree. Staff will copy their manager’s view if answers are not anonymous.

By company size

  • Medium: a one-hour workshop with a mixed group can replace the survey.
  • Mid-market and enterprise: run it by business unit. Units differ more than company averages suggest.

Assessments for this practice

These are existing questionnaires, tests and guides you can use. Each card gives the question it answers, who answers it and whether you can use it freely. All assessments.

Sources

  1. 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. What this study did and found
  2. Rogacka (2022). Digital skills in the workplace – from the employees’ and supervisors’ perspective. e-mentor, 5(97), 53–64. What this study did and found
  3. Luna, Ocaña, Ortiz-Colón and Rodríguez Moreno (2026). From Self-Perception to Reality in Digital Competence: Findings from the DigComp 2.2 Framework. The Electronic Journal of e-Learning, 24(3), 137–153. What this study did and found
  4. Wojewodzic (2026). AI Transformation in Polish Organisations: The Gap Between Individual Adoption and Organisational Maturity. e-mentor, 2(114), 75–83. What this study did and found
  5. Zimmermann (KfW Research) (2026). Einsatz von Künstlicher Intelligenz vor allem in Unternehmen mit hohen Innovations- und Digitalisierungsaktivitäten. KfW Research, Fokus Volkswirtschaft No. 533 (11 February 2026), with No. 554 (29 July 2026). What this study did and found
  6. Ferrando, Lamboglia, Rariga and Schmidt (2026). Adoption and investment in AI across the euro area: Insights from harmonised firm-level data. European Central Bank, Occasional Paper Series No 395. What this study did and found

Claims on this topic, checked against these studies: