Generative AI is reshaping how firms are organised: literature mapping shows GenAI creating an 'algorithmic workplace' where decision-making becomes more decentralized and managers shift from micro-managers to curators and coordinators. The emerging discourse links technical constructs (LLMs, GANs) to managerial concepts—implying potential productivity gains, changing skill demands, and new governance challenges, but stopping short of causal evidence.

Main Finding

Generative AI is reshaping organisational architecture and managerial agency by creating an "algorithmic workplace" characterised by hybrid human–machine agency, decentralised decision-making, and blurred managerial boundaries. The field shows a rapid, interdisciplinary convergence between technical constructs (e.g., large language models, GANs) and managerial/behavioural concepts (e.g., autonomy, coordination, decision-making), suggesting a move from command-and-control toward guide-and-collaborate modes of management with GenAI functioning as a socio-technical intermediary in decision support.

Key Points

• Corpus and scope: 212 Scopus-indexed publications (2018–2025) were analysed to map the emergent literature on GenAI and organisational change.
• Tools and mapping: Bibliometric and conceptual tools (VOSviewer, Bibliometrix) were used to identify performance trends, co-word structures, thematic mapping, and conceptual evolution.
• Conceptual structure: Co-word and thematic analyses reveal six coherent conceptual clusters that bridge technical AI topics (LLMs, GANs) with managerial themes (autonomy, coordination, decision-making).
• Evolution and authorship: A Sankey diagram of thematic evolution shows lexical convergence over time and indicates that a small set of authors has disproportionate influence in structuring discourse.
• Conceptual contribution: The article proposes an "algorithmic workplace" framework emphasising hybrid agency (agents composed of humans + GenAI), decentralised decision processes, and erosion of rigid managerial boundaries.
• Managerial shift: Evidence and argumentation point to a transition from hierarchical command-and-control to guide-and-collaborate paradigms, where managers curate, guide and coordinate AI-augmented teams rather than micro-manage tasks.
• Role of GenAI: GenAI acts as a socio-technical intermediary—facilitating interpretation, coordination, and decision support—rather than merely automating discrete tasks.

Data & Methods

• Data source: 212 peer-reviewed articles indexed in Scopus covering 2018–2025.
• Bibliometric software: VOSviewer (for network visualization) and Bibliometrix (for bibliometric statistics and thematic mapping).
• Analyses performed:
  • Performance trends (publication counts, citations, influential authors/journals).
  • Co-word analysis to detect recurring lexical themes and their relationships.
  • Thematic mapping to extract conceptual clusters (six clusters identified).
  • Thematic evolution tracing (Sankey diagram) to show how themes converge/diverge over time.
• Limitations (inherent to method): Bibliometric mapping profiles the intellectual structure and evolution of a field but does not establish causal effects; possible biases from database coverage (Scopus), timeframe cutoff, and publication selection; reliance on keyword co-occurrence may under-represent implicit conceptual nuances.

Implications for AI Economics

• Organizational form and transaction costs:
  • Decentralised decision-making may lower some internal transaction costs (faster local decisions) but raise coordination costs absent new governance mechanisms. Empirical work should estimate net effects on firm-level efficiency and boundaries.
• Labor demand and skill composition:
  • Hybrid agency implies complementarity between GenAI and certain managerial/knowledge-worker skills (curation, evaluation, coordination), potentially increasing returns to those skills while automating routine cognitive tasks—consistent with skill-biased technological change.
• Managerial capital and rents:
  • The erosion of rigid managerial boundaries could reduce the value of traditional supervisory skills while increasing the value of meta-management and AI-expertise, reshaping returns to managerial capital and hiring investments.
• Firm productivity and inequality:
  • Adoption of GenAI may deliver productivity gains for adopters but also generate winner-take-most dynamics (first-mover advantages, network effects), with implications for within- and between-firm wage dispersion and market concentration.
• Incentives, governance and regulation:
  • New incentive designs and accountability frameworks are needed when decision authority is shared with opaque GenAI systems (information asymmetries, attribution of responsibility).
• Market structure and competition:
  • Concentration of influential authors and convergent lexical frameworks in the literature hint at rapid standard-setting; analogous real-world concentration (platforms, model providers) could affect competitive dynamics and access to algorithmic capabilities.
• Research agenda for AI economists:
  • Causal microeconometric studies linking GenAI adoption to productivity, employment composition, wages, and firm boundaries (using difference-in-differences, IVs, RCTs, matched panels).
  • Structural models of firm organisation incorporating hybrid human–AI agents and endogenous delegation/monitoring choices.
  • Measurement work: constructing firm- and task-level indicators of GenAI use, managerial practices, and decision decentralisation.
  • Distributional analysis: how GenAI affects income inequality across occupations and firms; policy countermeasures (training, redistribution).
  • Policy evaluation: assessing regulatory approaches for accountability, liability, and access to GenAI as inputs to production.
• Practical takeaways for firms and policymakers:
  • Invest in complementarity: training for managerial roles focused on AI curation, evaluation, and coordination.
  • Reassess organisational design: experiment with decentralised decision rights and governance safeguards.
  • Monitor competitive dynamics: policy should consider market power of model/platform providers and implications for access and inequality.

Summary: The bibliometric study maps a rapidly converging literature showing GenAI’s structural effects on organisations—a shift likely to have broad economic consequences for labour demand, firm organisation, productivity and market structure. AI economics should prioritize causal empirical work, structural modelling of hybrid agency, and policy-oriented analysis to understand and shape these outcomes.