What question did this study set out to answer?

This paper aims to explore how artificial intelligence is transforming decision-making governance in organizations.

May 22, 2026

Reframing Artificial Intelligence as Organizational Meta-Governance: A Technology Forecasting Framework and Adoption Trajectory

Key Points

This paper aims to explore how artificial intelligence is transforming decision-making governance in organizations.
Developed two constructs: AI Facilitator and AI Chairman for decision structure and coordination.
Applied analogical forecasting and anticipatory governance logic to outline AI's evolution toward meta-governance.
Utilized a four-period technology forecasting trajectory calibrated against Rogers' S-curve.
Proposed mechanisms establish initial plausibility for the shift towards AI-enabled governance structures.
Identified the role of organizational coordination systems in facilitating this transition.
Highlighted the need for forecasting at the governance level rather than just the technological tool level.

Abstract

Organizational decision-making is undergoing structural transformation as artificial intelligence diffuses beyond narrow task applications into the governance processes through which organizations coordinate, prioritize, and execute decisions. A simple observation motivates this paper: AI is no longer just assisting decisions―it is beginning to shape how decisions themselves are structured and governed. Analogical forecasting and anticipatory governance logic together suggest that AI may evolve toward meta-governance, actively organizing the decision process rather than merely informing it. Two constructs operationalize this trajectory: the AI Facilitator, structuring information flows and stakeholder interaction at the operational level; and the AI Chairman―a conceptual shorthand for a meta-governance coordination function, not a literal organizational role―coordinating decisions across organizational units, resolving strategic conflicts, and ensuring long-run alignment at the meta-governance level. Bounded rationality, behavioral theory of the firm, dynamic capability theory, and agency theory together motivate seven propositions―each with specified mechanisms, boundary conditions, and falsification criteria―alongside a four-period technology forecasting trajectory calibrated against Rogers’ S-curve diffusion model and the Bass innovation–imitation framework. Section 7 reviews nascent empirical evidence from platform coordination systems, enterprise AI agent platforms, and clinical AI deployment studies, establishing initial plausibility for the proposed mechanisms. Our analysis suggests that organizations are likely to move toward more AI-enabled governance structures over time―and that understanding this shift requires forecasting at the governance system level, not the tool level. An anticipatory roadmap and structured operationalization guide support AI-ready governance before these systems reach institutional maturity.

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