AI and the Changing Nature of Work: Employability, Job Performance, and Fairness

The rapid integration of artificial intelligence (AI) into organizational contexts presents opportunities and challenges for workers, managers, and organizations. Despite ongoing implementation of AI at work, critical questions remain underexplored: How does AI reshape the competencies and motivations employees need to remain effective? What are the implications for performance evaluation, career development, and equitable access to opportunity? Related, are these implications different across diverse ability and background profiles? How do organizations navigate the ethical tensions surrounding effort, originality, and human relationships in AI-mediated environments? This Research Topic seeks to address these gaps by curating rigorous empirical and theoretical contributions that advance our understanding of AI’s multifaceted role in contemporary work. Building on recent advances in theory and empirical research, this collection aims to generate actionable insight for scholars and practitioners working at the intersection of AI, talent, job performance, and organizational effectiveness.

This Research Topic explores how artificial intelligence (AI) is reshaping work, with a focus on employability, job performance, fairness, and employee development in contemporary organizations. We welcome contributions on how AI influences the skills people need, their readiness for work, their motivation, their job performance, and future career prospects. We are also interested in research on perceptions and use of AI at work, its role as a resource and a barrier to job performance, and the implications for organizational justice, including fairness in access, evaluation, and opportunities for workers with different abilities and backgrounds. Ethical questions around effort, originality, leadership, workplace relationships, and AI implementation are also within scope.

We invite submissions that address one or more of the following themes related to artificial intelligence (AI) in work settings:

• AI and employability, including how automation and augmentation reshape skill demands and workforce readiness;
• AI as a performance resource or barrier impacting individual and/or team effectiveness;
• employee motivation, engagement, and well-being in work environments utilizing AI;
• fairness and organizational justice, with particular attention to algorithmic bias, equitable access, and differential impacts across worker demographics;
• ethical dimensions of AI use, including questions of effort, attribution, originality, and relational authenticity;
• leadership and development in the age of AI.

Conceptual, empirical, and methodological contributions are welcome.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Registered Report
• Review
• Systematic Review
• Technology and Code

Keywords: AI, job performance, employability, organizational justice, fairness, employee development, algorithmic bias, leadership

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.