A survey of 2,000 professionals has found that data privacy and security — not fear of being replaced — is the top reason employees resist AI tools at work. Fifty-eight per cent of respondents cited privacy concerns as their primary barrier to AI adoption, 11 points ahead of the 47% who cited fear of job displacement.
The research, conducted by software development firm Sombra, frames the pattern under a new label: AI Scopophobia — the fear of being observed, analysed, or evaluated by AI systems. As organisations deploy AI to monitor workflows, productivity, and workplace behaviour, that fear is becoming a tangible obstacle to adoption.
The figures suggest the problem runs deep. Seventy per cent of companies surveyed report moderate or strong internal pushback on AI, and almost one in three say it delayed their most recent AI project.
"The instinct is to treat resistance as people being afraid of change. But the research reveals the main fear is privacy and trust — and following companies' AI monitoring and AI-related hacks, this is completely justified. When employees don't know where their data goes or whether they can trust the outcome, slowing down isn't actually sabotage. In that case, it's a matter of caution — and AI Scopophobia is a natural reaction to it. The best companies turn these concerns into feedback. They're transparent with their employees about how data is handled, where AI belongs, and what it's accountable for."
— Viacheslav Brui, Data & AI Practice Lead, Sombra
The timing sharpens the finding. Meta's reported decision to halt an internal AI programme after a data leak gave employees a visible reference point for what AI privacy failure looks like in practice. Sombra's data suggests that kind of event doesn't just damage trust — it confirms fears that were already there.
For enterprise technology teams rolling out AI tools, the implication is that change management programmes built around productivity gains and job-security reassurances are addressing the wrong concern. Transparency about data handling and the boundaries of AI oversight — not better messaging about automation — appears to be the more effective path to adoption.
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