What it is
Many staff surveys ask how ready people are for AI and report one number. Ask two sets of questions instead: what makes people willing, and what makes them hold back. Report the two results side by side and never merge them into one.
What the evidence shows
- Readiness and resistance are not opposites. A review of the research on readiness and resistance argues that the two are separate. A person can be high on both. Resistance also shows up in two ways: in what people think, and in what they do.
- Both reactions exist in the same workforce. In a survey that asked the same 301 employees three times, people who saw the extra demands of AI as a challenge became more willing to use AI. People who saw the demands as obstacles grew anxious and less willing.
- One reading is not enough. A review of 47 studies found that readiness is defined in many ways and usually measured only once. Its link to successful rollout remains unclear.
A team with high readiness and high resistance needs something different from a team that is low on both. The first needs its worries dealt with. The second needs a reason to care.
How to do it
- In your staff survey, keep questions about willingness apart from questions about concerns. Short questionnaires exist for both. ATAI gives separate scores for acceptance and fear in five questions.
- On the concerns side, ask about specific things: job security, loss of skill, extra workload, being monitored, quality of the tool.
- Ask about behaviour as well as opinion: do people use the tool, avoid it, or work around it?
- Report two scores per team, plus the most common concerns.
- Sort teams into four groups: high or low on each. Plan a different response for each group.
- Measure before the rollout and again during it.
What to measure
- Willingness and concerns, as two separate scores per team.
- Actual use against stated willingness.
Where it fails
- Treating concerns as a fault. Some concerns are correct. Staff may have seen that the tool is wrong for the task.
- Surveys only. People do not always report resistance. Watch what they do.
- No action. Measuring concerns and then ignoring them makes them worse.
By company size
- Medium: a short anonymous survey and a team discussion will do.
- Mid-market and enterprise: results by team matter more than the company average. Give the results to line managers with guidance on what to do.
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.
- Are staff committed to this change, and do they believe it can be done? Organizational Readiness for Implementing Change (ORIC) Staff, about their organization or team · 12 questions Not established
- How positive are staff about AI in general? AI Attitude Scale (AIAS-4) Staff, each about their own views · 4 questions Free to use and adapt
- What positive and negative views of AI do staff hold? General Attitudes towards Artificial Intelligence Scale (GAAIS) Staff, each about their own views · 20 questions, or 10 in the short form Free to use, ask before changing
- How far do staff accept AI, and how far do they fear it? Attitude Towards Artificial Intelligence scale (ATAI) Staff, each about their own views · 5 questions Free to use and adapt
Sources
- Repovš, Drnovšek and Kaše (2019). Change Ready, Resistant, or Both? Exploring the Concepts of Individual Change Readiness and Resistance to Organizational Change. Economic and Business Review, 21(2). What this study did and found
- Chang, Zhang, Cai and Guo (2024). Does AI-Driven Technostress Promote or Hinder Employees’ Artificial Intelligence Adoption Intention? A Moderated Mediation Model of Affective Reactions and Technical Self-Efficacy. Psychology Research and Behavior Management, 17, 413–427. What this study did and found
- Caci, Nyantakyi, Blum, Sonpar, Schultes, Albers and Clack (2025). Organizational readiness for change: A systematic review of the healthcare literature. Implementation Research and Practice, 6. What this study did and found
Related practices
- 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)
- Protect psychological safety during an AI rollout AI projects can make staff afraid to speak up and unwilling to share what they know. Say early what AI means for jobs, and consult the people affected. Do first · Effort: medium (weeks, a few people)
- 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)