Business intelligence frameworks for predicting product adoption and reducing innovation failure risk

Innovation outcomes remain highly unpredictable, with global estimates showing that a significant proportion of new products fail to reach commercial viability. As markets become more dynamic and data-abundant, Business Intelligence (BI) has emerged as a critical capability for identifying adoption patterns and minimizing innovation failure risk. This paper provides a systematic review of BI frameworks that contribute to product adoption prediction across industries. Drawing on interdisciplinary research in information systems, marketing analytics, innovation management, and machine learning, the review synthesizes theoretical foundations, analytical techniques, and enterprise BI architectures that enhance foresight and strategic decision-making. The findings reveal that BI-enabled prediction models significantly improve early detection of adoption barriers, strengthen market-sensing capability, and enhance innovation performance through continuous learning loops. The paper concludes by highlighting current methodological gaps, proposing an integrated conceptual model, and offering directions for future research.