AI is not one thing: predictive, generative, agentic and embodied systems reshape firms in markedly different ways, altering expertise, decision architectures and authority; management scholars must move beyond a flattened ‘AI’ concept to address shifting agency and institutional challenges.

Main Finding

The paper argues that treating “AI” as a single, undifferentiated phenomenon obscures how different computational logics produce different organisational consequences. By distinguishing predictive, generative, agentic, and embodied systems, the authors show that these modes of AI reshape core organisational constructs—expertise, judgement, coordination, authority, and institutional adaptation—and propose a heuristic framework to guide future research and governance.

Key Points

• AI is heterogeneous: predictive (forecasting, classification), generative (content synthesis), agentic (autonomous decision-making/agents), and embodied (robotics/physical interaction) systems have distinct affordances and failure modes.
• Conceptual flattening (treating AI as one thing) leads to poor theorising about organisational effects; different AI modes interact differently with tasks, roles, and institutions.
• Core organisational constructs are being reconfigured:
  • Expertise: shifts from embodied tacit knowledge toward data-derived pattern recognition; credibility and credentialing change.
  • Judgement: human discretion interacts with algorithmic recommendations in varied ways (deference, contestation, deskilling, augmentation).
  • Coordination: AI changes information flows, routinises coordination, and enables new asynchronous or distributed architectures.
  • Authority: decision rights and accountability shift as systems take on more autonomous or advisory roles.
  • Institutional adaptation: organisations and regulatory fields must evolve norms, standards, and governance to accommodate AI capabilities and risks.
• The paper presents a heuristic framework that maps AI modes to organisational affordances and likely consequences.
• Research agenda: investigate shifting loci of agency, emerging decision architectures, accountability, normative challenges, and institutional responses.

Data & Methods

• Method: conceptual review and synthesis of the rapidly expanding management and organisation studies literature on AI; development of a heuristic typology and framework.
• No original empirical dataset: the contribution is theoretical and integrative—identifying distinctions across AI system types and extrapolating organisational implications based on literature.
• Approach includes comparative analysis of computational logics and mapping them to organisational constructs and research questions.

Implications for AI Economics

• Task composition and labor markets:
  • Predictive systems: accelerate automation of routine cognitive tasks, improve matching and screening, shift demand toward tasks requiring contextual judgement—potentially increasing wage polarization.
  • Generative systems: substitute for some content-creation tasks (copywriting, design drafts) while complementing higher-level creative supervision and refinement—affecting skill premiums and occupational boundaries.
  • Agentic systems: enable autonomous decision-making (e.g., trading agents, automated negotiation), altering principal–agent relationships, contract design, and monitoring costs.
  • Embodied systems: substitute for manual and physical tasks, induce capital deepening, change returns to investment in robotics vs. labor.
• Organisation design and production function:
  • New decision architectures (human-in-the-loop, human-on-the-loop, full autonomy) change where value is generated and how firms coordinate activities—affecting firm size, vertical integration, and outsourcing decisions.
  • AI-driven coordination lowers certain transaction costs (faster information, automated enforcement) but raises others (monitoring algorithmic behavior, integration costs).
• Market structure and competition:
  • Differential adoption of AI modes can create asymmetric competitive advantages, reinforce platform dominance (data/compute economies of scale), and raise barriers to entry.
  • Generative and predictive models amplify winner-take-most dynamics when tied to large datasets and user bases.
• Measurement and productivity accounting:
  • Standard productivity metrics may misattribute gains (capital vs. organizational learning), and quality/creative outputs from generative systems pose measurement challenges.
• Contracts, incentives, and regulation:
  • Emergent principal–agent issues require new contracting approaches (algorithmic accountability clauses, performance-based metrics for hybrid human-AI teams).
  • Liability and governance: agentic systems raise questions about legal responsibility, insurance, and regulatory oversight; institutional adaptation is needed to set standards, auditing, and certification.
• Policy implications:
  • Labor market policies (retraining, social safety nets) should account for mode-specific displacement and complementarities.
  • Competition and data governance policy must consider how different AI modes concentrate power via data/compute advantages.
  • Standardisation, transparency, and auditing regimes should be tailored to AI mode (e.g., interpretability for predictive models, provenance tracking for generative outputs, safety certification for embodied agents).

Overall, economic analysis should move from “AI as a single technology” to mode-specific models that capture heterogeneity in substitution/complementarity with labor, effects on firm boundaries and coordination, and distinct regulatory needs.