Artificial Intelligence Adoption and Sustainable Business Performance: A Management of Technology Perspective

Abstract

Artificial Intelligence (AI) is increasingly recognised as a pivotal factor in achieving sustainable business success; however, the evidence remains inconclusive, with some studies indicating weak or even adverse impacts when organisational factors are considered. This conceptual article introduces an integrated framework that explains how, and under which organisational and technological conditions, AI adoption can enhance economic, environmental, and social performance. It underscores the importance of Management of Technology (Mot) as the central process for transforming AI into value, rather than focusing solely on AI adoption. The study reviews peer-reviewed literature from 2016 to 2026, predominantly from 2021 onward, using a structured narrative literature synthesis approach. It combines the TOE (Technology-Organization-Environment) framework, the Resource-Based View (RBV), and Dynamic Capabilities Theory (DCT) within a management-of-technology perspective. The synthesis shows that AI adoption is conceptually distinct from AI implementation, capabilities, utilisation, and related constructs, which the literature often conflates, impairing causal clarity. A conceptual model shows that AI adoption influences sustainable performance through technology management and innovation capabilities, with organisational agility as a boundary condition. The study formulates ten testable hypotheses and provides an initial measurement instrument for subsequent empirical validation. This research contributes by integrating four theoretical perspectives into a cohesive model, emphasising technology management capability as the vital link between AI and sustainability, and offering a practical roadmap for managers to transform AI investments into sustainable value. Unlike prior studies that treat AI as a generic technology or assess performance solely through financial metrics, this article critically examines AI's sustainability trade- offs such as energy consumption, bias, workforce impact, and governance risks- and contends that AI's sustainability benefits are contingent upon specific conditions, rather than being inherently guaranteed.