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A Framework for the Adoption and Integration of Generative AI in Midsize Organizations and Enterprises (FAIGMOE)

Abraham Itzhak Weinberg

TL;DR

FAIGMOE (Framework for the Adoption and Integration of Generative AI in Midsize Organizations and Enterprises) presents a scale-aware, four-phase framework that integrates TOE, TAM, DOI, and organizational change theories to guide GenAI adoption across midsize and enterprise contexts. It offers a modular architecture with four dimensions (Strategic, Operational, Technical, Cultural) and phases for Strategic Assessment, Planning, Implementation, and Operationalization, including governance templates and risk management tailored to scale. The paper outlines validation strategies, illustrative scenarios, and expert validation, while highlighting limitations, empirical validation needs, and future research directions. Its practical contribution lies in scalable protocols, assessment instruments, and governance templates designed to enable responsible, value-driven GenAI integration across organizational contexts. The framework aims to democratize GenAI capabilities while addressing scale-specific constraints and governance requirements, advancing both theory and practice in AI-powered digital transformation.

Abstract

Generative Artificial Intelligence (GenAI) presents transformative opportunities for organizations, yet both midsize organizations and larger enterprises face distinctive adoption challenges. Midsize organizations encounter resource constraints and limited AI expertise, while enterprises struggle with organizational complexity and coordination challenges. Existing technology adoption frameworks, including TAM (Technology Acceptance Model), TOE (Technology Organization Environment), and DOI (Diffusion of Innovations) theory, lack the specificity required for GenAI implementation across these diverse contexts, creating a critical gap in adoption literature. This paper introduces FAIGMOE (Framework for the Adoption and Integration of Generative AI in Midsize Organizations and Enterprises), a conceptual framework addressing the unique needs of both organizational types. FAIGMOE synthesizes technology adoption theory, organizational change management, and innovation diffusion perspectives into four interconnected phases: Strategic Assessment, Planning and Use Case Development, Implementation and Integration, and Operationalization and Optimization. Each phase provides scalable guidance on readiness assessment, strategic alignment, risk governance, technical architecture, and change management adaptable to organizational scale and complexity. The framework incorporates GenAI specific considerations including prompt engineering, model orchestration, and hallucination management that distinguish it from generic technology adoption frameworks. As a perspective contribution, FAIGMOE provides the first comprehensive conceptual framework explicitly addressing GenAI adoption across midsize and enterprise organizations, offering actionable implementation protocols, assessment instruments, and governance templates requiring empirical validation through future research.

A Framework for the Adoption and Integration of Generative AI in Midsize Organizations and Enterprises (FAIGMOE)

TL;DR

FAIGMOE (Framework for the Adoption and Integration of Generative AI in Midsize Organizations and Enterprises) presents a scale-aware, four-phase framework that integrates TOE, TAM, DOI, and organizational change theories to guide GenAI adoption across midsize and enterprise contexts. It offers a modular architecture with four dimensions (Strategic, Operational, Technical, Cultural) and phases for Strategic Assessment, Planning, Implementation, and Operationalization, including governance templates and risk management tailored to scale. The paper outlines validation strategies, illustrative scenarios, and expert validation, while highlighting limitations, empirical validation needs, and future research directions. Its practical contribution lies in scalable protocols, assessment instruments, and governance templates designed to enable responsible, value-driven GenAI integration across organizational contexts. The framework aims to democratize GenAI capabilities while addressing scale-specific constraints and governance requirements, advancing both theory and practice in AI-powered digital transformation.

Abstract

Generative Artificial Intelligence (GenAI) presents transformative opportunities for organizations, yet both midsize organizations and larger enterprises face distinctive adoption challenges. Midsize organizations encounter resource constraints and limited AI expertise, while enterprises struggle with organizational complexity and coordination challenges. Existing technology adoption frameworks, including TAM (Technology Acceptance Model), TOE (Technology Organization Environment), and DOI (Diffusion of Innovations) theory, lack the specificity required for GenAI implementation across these diverse contexts, creating a critical gap in adoption literature. This paper introduces FAIGMOE (Framework for the Adoption and Integration of Generative AI in Midsize Organizations and Enterprises), a conceptual framework addressing the unique needs of both organizational types. FAIGMOE synthesizes technology adoption theory, organizational change management, and innovation diffusion perspectives into four interconnected phases: Strategic Assessment, Planning and Use Case Development, Implementation and Integration, and Operationalization and Optimization. Each phase provides scalable guidance on readiness assessment, strategic alignment, risk governance, technical architecture, and change management adaptable to organizational scale and complexity. The framework incorporates GenAI specific considerations including prompt engineering, model orchestration, and hallucination management that distinguish it from generic technology adoption frameworks. As a perspective contribution, FAIGMOE provides the first comprehensive conceptual framework explicitly addressing GenAI adoption across midsize and enterprise organizations, offering actionable implementation protocols, assessment instruments, and governance templates requiring empirical validation through future research.
Paper Structure (46 sections, 1 figure, 1 table)

This paper contains 46 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Existing AI Adoption Frameworks
  4. Generative AI: Characteristics and Organizational Applications
  5. Organizational Readiness and Change Management Theories
  6. Barriers and Enablers Across Organizational Scales
  7. Research Gaps and Synthesis
  8. Theoretical Foundation
  9. Core Theoretical Underpinnings of FAIGMOE
  10. Integration of Organizational Change and Management Theories
  11. Conceptual Model Development
  12. Framework Assumptions and Scope
  13. Methodological Principles Underlying FAIGMOE
  14. The FAIGMOE Framework
  15. Framework Architecture and Core Components
  16. ...and 31 more sections

Figures (1)

  • Figure 1: Phased architecture of the FAIGMOE framework for GenAI adoption in midsize organizations and enterprises, showing both sequential progression and iterative feedback loops.