Employees are not just worried about AI taking jobs; they are worried about how it will feel to work in an AI-heavy environment.

According to the AI and Workplace Humanity Report from Resume Now:

• 63% of workers believe AI will make the workplace feel less human.
• 57% think AI will reduce human skills.
• 43% believe AI will devalue human work.
• 20% expect a cold, machine-driven environment.
• Only 16% say AI will make the workplace more human.

Behind these numbers are a few specific worries:

• Loss of human connection: People fear a more sterile culture with fewer real conversations and more interactions mediated by tools and bots.
• Erosion of critical thinking: As AI handles more tasks, employees are concerned their own problem-solving and judgment skills will atrophy.
• Devaluation of human contribution: When AI is framed as the main driver of productivity, workers can feel like their work matters less.
• Accountability gaps: When output is delegated to AI, coworkers may end up checking or redoing low-quality AI-generated work, which can create frustration and distrust.

In short, employees are asking not just “Will AI change my job?” but “What kind of culture will we have if AI is everywhere?”

Leaders can reduce anxiety and build trust around AI by being clear, transparent, and inclusive in how they communicate.

Key practices include:

• Be explicit about your AI principles and goals: Share why you are using AI (e.g., to improve efficiency, reduce repetitive work) and where you are not planning to use it (e.g., replacing entire teams in the near term).
• Clarify job impact in the near- to mid-term: Gartner research shows that clarity about employees’ current value and role evolution is a stronger driver of AI adoption than other forms of support. Even if you cannot predict the long-term future, explain what will and will not change in the next few years.
• Address job security directly: Acknowledge the conflicting news about AI and jobs. Explain how AI will support people rather than simply cut headcount, where that is the case.
• Create channels for employee voice: As Kaelyn Lowmaster of Gartner notes, employees should be able to surface concerns, ask questions, and suggest AI use cases. This can include town halls, feedback forms, pilot user groups, and internal communities of practice.
• Highlight the need for human oversight: As Frank Antezana of iTech AG points out, AI may complete 80–90% of a workflow, but people are still needed to validate outcomes, make decisions, and own accountability.

When leaders consistently communicate that AI is a support tool, not a replacement for human judgment and connection, employees are more likely to engage with it constructively.

To keep culture and collaboration strong while adopting AI, companies need to design both the technology rollout and the human experience around it.

Practical steps include:

• Position AI as a support tool: As Megan Slabinski of Robert Half notes, organizations that frame AI as an assistant rather than a replacement tend to see stronger employee interest and less resistance.
• Invest in connection, not just tools: Use mentorship time, team-based projects, and in-person or hybrid touchpoints to bring people together. This helps ensure that efficiency gains do not come at the cost of relationships.
• Watch for “AI workslop”: Undertrained employees can produce low-quality AI outputs that others must fix. Provide guidance on when and how to use AI, set quality standards, and make it clear that humans remain accountable for final results.
• Protect collaboration: If employees rely on AI for brainstorming, reviewing, or decision support, they may collaborate less with colleagues. Encourage peer reviews, co-creation sessions, and team-based problem solving alongside AI use.
• Manage performance expectations: When AI boosts individual efficiency, there is a risk of setting unsustainable, AI-driven targets. Leaders should be careful not to turn productivity gains into constant pressure.
• Address trust in AI output: Gartner’s research highlights a lack of trust in AI accuracy as a major barrier. Build trust by piloting tools, sharing performance data, and clearly defining which decisions require human review.

By intentionally designing for human connection, clear accountability, and realistic expectations, organizations can reimagine how AI fits into work without sacrificing culture.