leadership, team development, General, Organisational Leadership Development, AI in the workplace

Why AI Resistance Is Personal

Rachael Stott

29 July 2026

What the Big Five Can Teach Leaders

About Adoption

When people resist AI, it is tempting to assume they do not understand the tools, lack confidence, or simply need more training. Current research and narratives are pointing to something deeper. AI can threaten the way people see themselves at work: their expertise, status, judgement, influence and sense of purpose. Resistance is not always rational opposition to technology; sometimes it is a protective response to identity loss.

The ‘Big Five’ model of personality¹ gives leaders a useful lens for understanding why the same AI rollout can produce very different reactions across a team.

Openness, conscientiousness, extraversion, agreeableness, and emotional stability do not determine whether someone will embrace or reject AI, but they can shape how people experience uncertainty, loss of control, and change.

AI resistance is not one thing. Michael Blanding’s June HBR article describes three ways AI can shrink a role: “role compression, control shift, and span erosion” and offers that each of these challenges a different part of a professional identity.

Role compression asks, “If AI can do this, what makes me valuable?”
Control shift asks, “If the algorithm decides, what is my judgement worth?”
Span erosion asks, “If I am only involved when something goes wrong, do I still matter?”

These are not technical questions; they are human ones. They are leadership matters.

AI adoption is a leadership matter, not just a technology one.

Jared Griffiths, Practice Lead – AI Transformation. Martin Jenkins

Because people differ in how they respond to novelty, risk, social pressure, responsibility, and threat, a personality lens can help leaders move beyond one-size-fits-all communication, engagement and leadership.

Big-Five Personality Insights – Potential Experiences of AI Change.

Openness: curiosity versus caution. People higher in openness are often more comfortable with ambiguity, experimentation and new ideas. In an AI rollout, they may be the first to explore tools, test prompts, challenge old processes, and imagine new possibilities. They can become early adopters and translators for the rest of the organisation.

People lower in openness may not be anti-AI. They may simply need a clearer bridge between the familiar and the new. They are likely to ask practical questions: Does this actually work? Is it reliable? Why should I change a process that already delivers results? Leaders can help this group by providing evidence, practical demonstrations, low-risk trials, and examples that connect AI to existing strengths rather than implying everything old is obsolete.

Conscientiousness: accuracy versus agility. Highly conscientious people care about quality, accuracy, process and responsibility. They may resist AI not because they dislike change, but because they are concerned with delivering high-standard outcomes. If AI introduces errors, bias, shallow thinking or unclear accountability, conscientious employees may see adoption as a threat to their professionalism.

This is particularly relevant to the article’s point about control shift. If AI begins making recommendations that people are expected to accept, conscientious employees will want to know who checks the output, who owns the decision, and what happens when the system is wrong. Leaders can support them by defining governance, review points, ethical boundaries and quality expectations. For this group, trust grows when the operating model is clear.

Extraversion: visible enthusiasm, quiet withdrawal. More extraverted employees may process AI change out loud. They may ask questions in meetings, influence peers, and either champion the change or amplify concern. If they feel included, they can help create momentum. If they feel threatened, they can make resistance socially contagious.

More introverted employees may be harder to read. They may not challenge the rollout publicly, but that does not mean they are adopting meaningfully. This is where symbolic adoption becomes dangerous. Leaders who only listen to the loudest voices may miss quiet avoidance, private concern or silent disengagement. Adoption plans need multiple channels for feedback, including one-on-one conversations, anonymous input and practical communities of practice.

Agreeableness: harmony can hide resistance. Highly agreeable people often value cooperation, trust and team harmony. In an AI rollout, they may be reluctant to appear negative or obstructive. They may nod along, express support, and avoid raising concerns that could make them seem difficult. This makes them especially vulnerable to the “say yes and mean no” pattern described in Blanding's article.

Leaders should not mistake compliance for commitment, nor debate for dissent. For agreeable employees, psychological safety is essential because it gives permission to disagree without damaging relationships. Leaders can help by explicitly inviting dissent, thanking people for surfacing risks, and framing concern as contribution rather than resistance.

Those on the more direct side of Agreeableness, may appear more skeptical of claims and are less likely to hide their doubts. They may openly challenge flawed workflows, poke holes in algorithm accuracy or call out exaggerated claims. While this can feel confrontational, leaders should not label these team members as ‘cynics’. Instead, leverage their willingness to challenge groupthink, shift passive compliance and model the robust task-related debate that can underpin more sustainable change.

Emotional Stability: threat sensitivity and identity protection. People who are more emotionally tuned-in and expressive may experience AI change as more threatening.

Job security, competence, status and future relevance can all feel uncertain. When leaders combine AI announcements with vague comments about efficiency or headcount, anxiety is amplified. People may then protect themselves by withholding or waiting, ruminating on what is unknown, and distracted by how perceived threats may play out.

This group needs more than reassurance. They need credible signals: what will change, what will not, what support will be provided, how decisions will be made, and how their value will evolve. Ambiguity fuels threat. Specificity reduces it.

Resilient, unbothered by workplace ambiguity, and slow to panic; in an AI rollout, the calm presence of emotionally stable team members can be a valuable stabilising force. However, because they don't easily feel threat or urgency, they may view AI initiatives with detached indifference. Unthreatened, they may see adopting new AI tools as unnecessary effort. Their resistance isn't driven by fear, it's driven by inertia.

Don't rely on high-drama urgency to motivate this group, it won't work. Instead, leaders should frame adoption not as an existential transformation, but as a practical upgrade to their day-to-day workflows; eliminating tedious tasks or saving them time.

In summary - AI adoption is not just about getting people to use tools. It is about helping people preserve and renew their sense of professional value as work changes around them.

The Big Five reminds us that resistance will not look the same for everyone.

Some people will ask for evidence. Some will ask for rules. Some will talk through their concerns. Some will hide them. Some will feel deeply threatened before they can see any upside.

Leaders who understand this will be less likely to confuse silence with support or compliance with commitment. They will know that successful AI adoption depends not only on smart systems, but on more human leadership.

The most important and unchanging leadership matter:

Know your people.

^1.WBL uses the Assessio five-factor MAP (Measuring & Assessing Individual Potential)

References:

Why Employees Resist AI - and How Companies Can Win Them Over. Featuring Das Narayandas and Shunyuan Zhang. By Michael Blanding on June 24, 2026. https://www.library.hbs.edu/working-knowledge/why-employees-resist-ai-and-how-companies-can-win-them-over