Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
It is concluded that no single framework adequately explains GenAI's enterprise decision-making effects across all analytical levels, and that individual-, organizational-, and task-level frameworks must be combined rather than treated as competing explanations, identifying multi-level theoretical integration as the central future research prospect.
Abstract
Enterprise adoption of generative artificial intelligence (GenAI) has outpaced the development of theoretical frameworks capable of explaining why some organizations successfully integrate the technology into decision-making while others fail to realize comparable value, leaving much of the applied literature to borrow, adapt, or extend theoretical apparatus developed for earlier waves of information technology adoption. This paper reviews the theoretical frameworks and applied evidence base for GenAI in enterprise decision-making, organizing the literature around five analytical lenses: individual-level technology acceptance theory, organizational-level technology-organization-environment adoption theory, the dynamic capabilities framework for strategic integration, the prediction-machine economic framework, and the recently formalized "jagged technological frontier" capability-boundary framework. The review synthesizes foundational adoption theory with large-scale field experimental evidence on GenAI's productivity effects, and with the governance and ethical-framework literature addressing accountability and explicability in AI-assisted enterprise decisions. Distinct comparative tables map each framework onto its unit of analysis and central explanatory variable, cross-reference specific business functions against the framework best suited to explain observed adoption patterns in each, and organize the field's principal future challenges by the theoretical gap each challenge exposes. The paper concludes that no single framework adequately explains GenAI's enterprise decision-making effects across all analytical levels, and that individual-, organizational-, and task-level frameworks must be combined rather than treated as competing explanations, identifying multi-level theoretical integration as the central future research prospect.
Generative artificial intelligence (GenAI) has moved from experimental novelty to a central input in organizational decision-making, with McKinsey's Q1 2026 Global AI Survey finding that 65 percent of organizations now use generative AI in at least one business function, roughly double the adoption rate reported ten months earlier, and 72 percent report at least one AI workload in production. Despite this scale of adoption, the empirical record on business value remains sharply divided. This paper synthesizes recent academic and industry evidence, drawing on 24 sources published primarily between 2023 and 2026, to examine three interlocking questions: what theoretical frameworks currently explain GenAI's role in managerial and strategic decision-making, how enterprises are applying GenAI in practice across functional areas, and what structural challenges limit the translation of GenAI adoption into measurable business value. The paper synthesizes evidence from strategic management research on AI-assisted evaluation of business alternatives, organizational theory on GenAI's emerging roles in decision processes, and empirical field studies on ambiguity handling and sycophantic behavior in AI-generated business advice, alongside a widely cited 2025 MIT study finding that 95 percent of enterprise generative AI pilots fail to deliver measurable profit-and-loss impact. Findings indicate that GenAI functions most reliably as an augmentation tool that aggregates and structures diverse inputs for human judgment, rather than as an autonomous decision-maker, that single-model evaluations of strategic alternatives are frequently inconsistent and biased while aggregated multi-model evaluations approximate expert human judgment, and that the primary barrier to enterprise value is organizational and workflow integration rather than model capability. The paper concludes with a proposed decision-integration framework and implications for executives, AI governance functions, and researchers.
M.SANGEETHA, Una Suman Kumar Patro, K.ARPITHA et al.· International journal of com...· 0 citations
GenAI's current business value is concentrated in augmenting, rather than automating, decision-making, with the strongest evidence for productivity gains among relatively lower-skilled or lower-performing decision-makers, and the mapping of AI capability boundaries within specific decision domains as the central future research prospect.
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Artificial Intelligence (AI) is increasingly recognized as a foundational technology for enterprise competitiveness in the digital economy. However, limited research explains how AI capabilities create enterprise value in emerging economies with uneven digital readiness. This study synthesizes the mechanisms of AI-driven value creation and examines structural barriers to enterprise AI adoption in Vietnam. Using a qualitative conceptual research design based on secondary data, the study integrates the Resource-Based View (RBV) with a four-layer AI framework covering data, algorithms, infrastructure, and applications. The findings suggest that AI creates enterprise value through cognitive automation, decision intelligence, and business model innovation, but their effectiveness depends on data governance, digital leadership, human capital, financial readiness, and regulatory support. In Vietnam, adoption is constrained by fragmented data systems, shortages of skilled AI professionals, limited SME investment capacity, and evolving governance frameworks. This study contributes a conceptual synthesis linking AI capability layers to enterprise value creation and provides implications for managers and policymakers seeking to accelerate responsible AI adoption.
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