From Proof-of-Concept to Production: A Techno-Functional Framework for Generative AI Adoption in Enterprise Settings

Source abstract

Purpose: Enterprise generative artificial intelligence (GenAI) is producing a peculiar adoption pattern: many proofs-of-concept, few production deployments. Existing adoption frameworks, designed for technologies whose outputs are deterministic, struggle with foundation models. This paper develops a four-dimensional framework, the techno-functional framework, to guide enterprise GenAI adoption from initial use case screening through structured post-implementation learning. Its dimensions are relevance, operating model, agility, and retrospective. Design and methodology: The paper is conceptual. Its scaffolding rests on a PRISMA 2020-compliant systematic literature review of 60 high-impact peer-reviewed and adjacent works, combined with structured practitioner reflexivity drawn from the authors’ combined enterprise and higher-education AI implementation experience. Each dimension is anchored in established theory. Relevance sits within the technology–organization–environment tradition and Rogers’s diffusion of innovations; operating model draws on dynamic capabilities; agility on organizational ambidexterity; retrospective on absorptive capacity. Findings: Three asymmetries run through the recent literature. Antecedents of GenAI adoption are well studied, but what organizations do after first deployment is barely theorized. Technology-centric and organization-centric framings sit in separate compartments. Iteration is everywhere assumed but rarely specified as a continuous discipline rather than an implementation phase. The framework proposed here addresses each asymmetry. Twelve testable propositions render the path to empirical validation explicit, and four composite vignettes spanning financial services, industrial manufacturing, global retail, and a higher-education institutional setting illustrate the framework’s dimensions in interaction. Originality and value: To the authors’ knowledge, this is the first GenAI adoption framework to place structured retrospection as a first-class dimension grounded in absorptive capacity, and the first to bridge technology and organizational considerations within a single managerial instrument rather than across parallel streams. The framework is research-generative and managerially actionable.