RETHINKING BUSINESS MANAGEMENT IN THE AGE OF ARTIFICIAL INTELLIGENCE: FROM HUMAN EXPERTISE TO INTELLIGENT, ADAPTIVE AND SUSTAINABLE ORGANIZATIONS

Abstract

Artificial intelligence (AI) is increasingly positioned as a structural force reshaping how organizations plan, decide and compete, yet much of the scholarly conversation around this shift remains conceptual rather than grounded in a testable, integrated measurement framework. This paper develops and pilot-tests such a framework by examining how AI adoption in business management relates to four organizational dimensions: the integration of AI with human expertise, organizational adaptability and agility, sustainability practices, and overall organizational performance. A structured, five-section questionnaire comprising twenty-five Likert-type items was designed around five constructs AI Adoption in Business Management (AIA), Human Expertise & AI Integration (HEI), Organizational Adaptability & Agility (OAA), Sustainability through AI (SUS), and Overall Impact on Performance (OIP) and was administered to an illustrative sample (n = 200) constructed to mirror the structure and distribution of a genuine cross-industry survey of working professionals. Descriptive statistics, Cronbach's alpha reliability analysis, Pearson correlation, one-way ANOVA, independent-samples t-tests and multiple linear regression were used to examine the proposed relationships. All five constructs demonstrated strong internal consistency (α = 0.84-0.88; overall scale α = 0.95) and were significantly and positively inter-correlated (r = 0.57–0.66). Multiple regression results show that AIA, HEI, OAA and SUS jointly explain approximately 56.9 percent of the variance in OIP (R² = 0.569, F (4,195) = 64.37, p < .001), with HEI (β = 0.270, p < .001) and SUS (β = 0.298, p < .001) emerging as the strongest individual predictors. The paper contributes a validated measurement instrument and a fully worked analytical pipeline that can be directly redeployed on genuine field data, and it discusses the managerial implications of moving organizations from reliance on human expertise alone toward intelligent, adaptive and sustainable ways of working. Consistent with sound research practice, the paper is explicit that the dataset used here is an illustrative, simulated pilot sample built to demonstrate the framework end to end, and it sets out the steps required before the model can be treated as an empirical finding about real organizations.