From Adaptation to Agency: Employee Proactivity and Career Development in the AI Era

Dear Colleagues,

Rapid artificial intelligence adoption has fundamentally reshaped work tasks, organizational workflows, professional identities, and long-term career trajectories across all industries. Existing organizational and career scholarship largely frames employee responses to technological disruption as passive adaptation: individuals adjust skills, attitudes, and routines to fit externally imposed AI systems, algorithmic management rules, and automated task restructuring. Yet this one-sided adaptation lens overlooks a critical reality: employees are not merely reactive recipients of technological change. Instead, they can exercise employee agency—volitional, intentional action to shape how AI tools are designed, implemented, deployed, and governed within daily work and personal career planning. This Special Issue centers the theoretical shift from adaptation to agency to unpack how employees actively negotiate, co-create, and even constrain AI-driven workplace transformation for sustainable career growth.

Core Concept Clarification: Employee Agency in the AI Context

We define AI-era employee agency as volitional, self-initiated employee behaviors that go beyond mere adjustment to technological change, through which individuals actively influence the adoption, usage rules, and functional boundaries of workplace AI systems to advance their professional interests, safeguard occupational identity, and shape equitable digital career opportunities.

We explicitly distinguish this construct from related concepts:

1. Employee proactivity: A broader umbrella term capturing all self-starting workplace behaviors; employee agency specifically targets power dynamics and individual influence over technological infrastructure (AI tools, algorithms, automated workflows).
2. Career adaptability: A dispositional resource enabling adjustment to career shocks; adaptability focuses on coping capacity, while agency emphasizes active structural shaping of technological environments.
3. Job crafting: Modification of task, relational, and cognitive job boundaries; job crafting centers individual role redesign, whereas agency extends to participation in organizational AI policy, algorithmic oversight, and cross-level technology implementation decisions.
4. Adaptive performance: On-the-job adjustment to novel task demands; adaptive performance captures compliance with technological change, while agency captures resistance, modification, and co-design of technological systems.

Employee agency therefore moves far beyond “adapting to AI”. It encapsulates proactive acts such as proposing AI workflow adjustments, participating in algorithm governance committees, negotiating reskilling support, pushing back against biased automated performance evaluation, or redesigning human-AI collaboration protocols to reduce occupational precarity.

Theoretically Distinctive AI-Specific Workplace Transformation

General workplace change research cannot fully capture the unique tensions and affordances introduced by AI and algorithmic systems. This Special Issue foregrounds features exclusive to AI-driven transformation that differentiate it from prior technological shifts:

• Algorithmic management and opaque automated decision-making that shift authority from human supervisors to black-box systems;
• Human–AI collaboration trade-offs (productivity gains versus deskilling, identity erosion, or excessive technological dependency);
• Unequal distribution of AI upskilling, reskilling, and digital career resources across gender, occupational, socioeconomic, and sectoral groups;
• Professional identity threats triggered by task automation and AI substitution risks;
• Dual effects of AI infrastructure: simultaneously enabling flexible work while imposing constant digital surveillance and workload amplification;
• Tensions between constructive, productive AI utilization and problematic over-reliance on algorithmic recommendations that erode individual professional judgment.

All submissions must engage with at least one of these AI-unique contextual features to avoid conflation with generic organizational change research.

Centralized Career Development Focus

Consistent with the Special Issue title, career processes and outcomes form the core organizing pillar of all accepted contributions. We prioritize research that explicitly links AI-related agency and proactivity to the following career constructs:

• Short- and long-term employability and career sustainability;
• Occupational identity formation and reconstruction amid automation threats;
• Skill obsolescence, reskilling motivation, and voluntary career transitions triggered by AI;
• Subjective career wellbeing, burnout, and identity threat as hidden costs of digital transformation;
• Objective and subjective career success under algorithmic evaluation regimes.

Studies that only examine general workplace behavior without clear career development implications fall outside the scope of this Special Issue.

Identified Critical Research Gaps Guiding the Issue

The collection addresses four interconnected, underexplored gaps in existing proactivity and career literature:

1. Autonomy assumptions in proactivity research: Most prior work presumes employees hold sufficient discretion to act proactively, yet algorithmic monitoring, rigid AI task protocols, and centralized digital governance frequently restrict the scope for agentic action. Few studies examine when and how AI systems limit proactive employee behavior.
2. One-directional technological determinism in career scholarship: Existing work frames AI as an external, unalterable career shock. Minimal research investigates how employees exercise agency to modify organizational AI adoption roadmaps, algorithmic evaluation standards, and digital training policies.
3. Overemphasis on positive outcomes of proactivity: Dominant literature highlights productivity and career growth benefits of self-initiated action, but neglects hidden costs—work overload, digital anxiety, professional identity threat, labor market exclusion, and chronic burnout stemming from constant mandatory upskilling and algorithmic accountability.
4. Stratified access to AI career resources: Scarce cross-group comparative research addresses disparities in AI reskilling opportunities, digital agency capacity, and career advancement gains across marginalized workers, blue-collar occupations, gig employees, and underrepresented demographic subgroups.

Three Unified Core Research Questions

To ensure cohesive dialogue between all published manuscripts, all submissions must align with one or more of the following central research questions:

1. Under what technological, organizational, and individual conditions does AI workplace transformation enable versus constrain employee agency?
2. Through what psychological, social, and structural mechanisms do employee proactive behaviors foster sustainable career development during digital transformation—and which boundary conditions turn AI-related proactivity into costly, detrimental outcomes?
3. Which employee populations disproportionately benefit from AI-enabled career advancement, and which groups face structural disadvantage and restricted agentic capacity amid algorithmic workplace restructuring?

Accepted Submission Types and Scope of Contributions

We welcome rigorous empirical work that addresses the above research questions, including quantitative, qualitative, mixed-method, longitudinal, and cross-cultural comparative studies. Relevant thematic streams include, but are not limited to:

1. Individual and contextual antecedents of AI-focused employee agency and proactive career behavior: personality traits, psychological capital, inclusive leadership, organizational digital climate, algorithmic transparency policies;
2. Employee agentic strategies to reshape AI implementation: worker co-design of AI workflows, grassroots resistance to harmful algorithmic management, self-directed reskilling and cross-career skill diversification;
3. Dual positive and negative career outcomes of human-AI proactive engagement: employability growth alongside burnout, identity threat, skill precarity, and work overload;
4. Intergroup disparities in AI agency and career development: newcomers, career changers, gig workers, low-skilled labor, and demographically underrepresented employee cohorts.

Dr. Wenjing Cai
Guest Editor

Manuscript Submission Information

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