The Impact and Risks of Artificial Intelligence on Employee Skills Development and Daily Work: An Analysis for Human Resources Development

AI's application in the workplace is increasingly defined by its ability to address demands for efficiency, cost reduction, and personalization. Organizations have adopted AI technologies for diverse functions, including human resources, training, and employee development. For example, Intelligent Employee Assistants (IEAs) enhance efficiency by automating repetitive administrative tasks such as scheduling and information retrieval. Similarly, personalized training platforms utilize AI to create customized learning pathways, targeting specific skill gaps and aligning training outcomes with career aspirations (Baki et al. 3). These advancements demonstrate how AI promotes operational efficiency and individual engagement simultaneously. Additionally, the predictive capabilities of AI allow for optimized resource allocation in recruitment and retention, further aligning organizational objectives with employee needs. Despite these benefits, the growing reliance on AI in human resource functions invites scrutiny regarding potential biases in algorithmic decision-making. For example, data-driven hiring tools may inadvertently perpetuate existing inequalities unless adequately monitored and audited for fairness. Tackling such issues requires organizations to establish ethical guidelines and maintain robust accountability frameworks to mitigate risks associated with AI deployment. AI's rapid adoption has also introduced ethical and operational challenges, particularly regarding algorithmic biases and transparency. Flaws in training datasets often result in biased AI outputs that reflect and potentially exacerbate societal inequalities. In recruitment processes, for instance, these biases can lead to discriminatory practices if not addressed. Transparency challenges also arise, especially with "black-box" syste