Adoption of AI Tools in Business Analytics: Determinants and Organizational Impact

Abstract

The use of Artificial Intelligence (AI) in Business Analytics is rapidly changing how organizations analyze data and make decisions. AI-based tools help businesses move beyond traditional reporting by enabling predictive insights, automation, and faster, more accurate decision-making. Despite these advantages, the level of AI adoption differs across organizations due to various internal and external factors. This study explores the key determinants that influence the adoption of AI tools in business analytics using the Technology–Organization–Environment (TOE) framework, supported by the Resource-Based View (RBV) and Technology Acceptance Model (TAM). It examines how technological factors such as system compatibility and data quality, organizational factors like management support and employee skills, and environmental factors including competition and regulations affect adoption decisions. In addition, the study analyzes how AI adoption impacts organizational outcomes, particularly in terms of operational efficiency, financial performance, and strategic flexibility. A quantitative research approach is proposed, where data will be collected through structured questionnaires and analyzed using Structural Equation Modeling (SEM). The expected findings suggest that organizations with better preparedness and strong leadership support are more likely to successfully implement AI tools, resulting in improved performance and a stronger competitive position in the market.

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