Over-automation, human judgement, change fatigue, and security

The final video in this series on risks and limitations addresses four interconnected concerns under AC 3.2 of the FREE02 People Professional Technologies unit: over-automation, the erosion of human judgement, change fatigue, and cyber and security risks. Together these topics form a coherent argument about where technology can undermine rather than support effective people management, and the video draws a clear boundary between processes that benefit from automation and those that require human involvement — sensitive conversations, empathy-dependent interactions, and complex contextual decisions among them.

The video gives particular attention to the relationship between over-automation and the loss of human judgement, examining what happens when algorithmic outputs — such as flight-risk flags from an analytics dashboard — are treated as conclusions rather than starting points. People professionals' contextual understanding, emotional intelligence, and ethical reasoning feature as counterweights to over-reliance on automated decision-making. Change fatigue also receives substantive coverage, framed as the cumulative exhaustion and disengagement that arises from repeated technology implementations, with the pace and spacing of organisational change treated as a professional responsibility rather than a purely operational matter.

Cyber and security risks are addressed in the context of the particular sensitivity of people technology systems, which routinely hold personal details, health information, salary data, performance assessments, and disciplinary records. The video discusses data breaches in terms of financial penalties, reputational harm, and direct harm to employees, and it positions security awareness and access control as responsibilities that extend to HR practitioners, not solely to IT functions. Taken together, the four risk areas covered here complete the indicative content for AC 3.2 and sit alongside the earlier video topics of poor data quality, misleading metrics, surveillance concerns, low adoption, accessibility barriers, and bias in data and AI.