AI's Impact on Employees and Organisations
As companies pour billions into AI, leaders often chase headline-grabbing productivity gains. However, the deeper and longer-lasting effects occur within the organisation, particularly in employees’ daily work, morale, skills, and retention.
Ignoring these human elements risks failed deployments, employees leaving, and a weakened company culture. For leaders, measuring AI’s true value means tracking its impact on people and organisational health, not just output numbers.
Conventional benchmarks offer limited insight (if any) into these broader effects. They typically evaluate isolated tasks in controlled settings, which may not fully capture how AI influences roles, workflows, and job satisfaction in real-world workplaces.
Core Areas of Impact
Productivity and Role Changes. Research indicates that AI can substantially improve performance for inexperienced workers. A recent field experiment with customer support agents showed gains of up to 34% for less experienced workers. Experienced workers, however, often experience more modest gains and may find that some of their routine expertise becomes less central as it is automated. Over time, this can shift roles toward greater emphasis on oversight, error-checking, and higher-level judgment, though challenges emerge when AI outputs are not consistently reliable. (Brynjolfsson et al., 2025).
Job Satisfaction and Retention. Employees report mixed experiences: faster task completion and reduced drudgery increase satisfaction for some, while “AI anxiety,” constant tool-switching, and fear of obsolescence drive burnout and turnover for others. High-skill roles are especially vulnerable when workers feel replaced rather than augmented (Microsoft Research, McKinsey & Company 2025).
Skill Development and Upskilling. Successful AI adoption demands new competencies—prompt engineering, data literacy, critical evaluation of outputs. Organisations that invest in training see stronger engagement and innovation; those that don’t risk widening skill gaps and lower adaptability (WEF 2025).
Culture and Collaboration. AI can democratise information and spark creativity across teams, but it can also reduce human interaction, create dependency on tools, or concentrate decision-making power among AI-fluent employees (Harvard Business Review, 2026).
Key Objectives Leaders Should Track
Move beyond generic KPIs to people-focused indicators:
- Employee engagement scores and internal NPS before and after AI rollout.
- Turnover and voluntary exit rates in AI-impacted roles versus control groups.
- Adoption and active usage rates of AI tools (low usage signals resistance or poor fit).
- Time saved on routine tasks versus time spent correcting or managing AI.
- Training participation rates and measurable skill application post-training.
- Segmented productivity metrics (by experience level, department, or role) to spot deskilling risks.
Practical Measurement Framework
- Run small pilots with diverse employee feedback loops before enterprise scaling.
- Combine HR data (engagement surveys, exit interviews) with AI usage statistics.
- Implement surveys incorporating targeted, AI-specific questions,
such as:
- “Does this tool make your work more meaningful?”
- “Do you feel adequately equipped to use it effectively?”
- Include relevant people-centric metrics in AI project reports alongside financial data to provide a more balanced view of project success.
Conclusion
AI’s greatest business value emerges when it empowers people, not when it acts as a disruption or a replacement for them. Leaders who prioritise measuring and optimising employee and organisational impact will achieve higher adoption, stronger retention, and sustained competitive advantage. Focus on your workforce—the real engine of AI success.