AI Maturity Model for Business Intelligence Adoption

Abstract

The rapid expansion of digital technologies has transformed the way organizations generate, manage, and utilize information for decision-making. Business Intelligence (BI) has emerged as a critical organizational capability that enables enterprises to convert large volumes of raw data into meaningful insights that support strategic and operational objectives (Watson & Wixom, 2007). As organizations continue to accumulate information from enterprise applications, customer interactions, social media platforms, and connected devices, the complexity of managing and analysing data has increased significantly. Traditional BI systems have historically focused on descriptive analytics and reporting functions; however, the growing demand for predictive and prescriptive insights has encouraged organizations to integrate Artificial Intelligence (AI) technologies into BI environments (Chen et al., 2012). AI technologies such as machine learning, natural language processing, and intelligent automation have expanded the analytical capabilities of BI systems by enabling organizations to identify hidden patterns, predict future outcomes, and automate decision-support processes (Sharda et al., 2022). Despite increasing investments in AI-enabled analytics platforms, organizations exhibit substantial differences in their ability to successfully adopt and operationalize AI technologies. Successful implementation depends upon factors such as data quality, governance mechanisms, technological readiness, organizational culture, leadership commitment, and workforce capabilities (Yeoh & Koronios, 2010). Consequently, maturity models have emerged as effective tools for evaluating organizational readiness and guiding transformation initiatives. Existing BI maturity frameworks emphasize information management, governance, and analytical capabilities (Tan et al., 2010), whereas AI maturity frameworks focus on scalability, automation, governance, and responsible AI adoption (Ozkaya et al., 2025). This study proposes an AI Maturity Model for Business Intelligence Adoption that integrates concepts from both domains to provide a comprehensive framework for organizational assessment and continuous improvement. The proposed model supports organizations in evaluating current capabilities, identifying maturity gaps, and developing strategic roadmaps toward AI-driven decision intelligence. The findings suggest that organizations achieving higher maturity levels are more likely to realize improvements in innovation, operational efficiency, decision quality, and business value creation (IBM Institute for Business Value, 2024). Furthermore, AI maturity is increasingly becoming a key determinant of organizational competitiveness in digital business environments (McKinsey Global Institute, 2024; Gartner, 2024).