Smarter Decisions through Predictive Analytics: Integrating Artificial Intelligence and Machine Learning Approaches

Abstract

The rapid expansion of data-driven ecosystems has positioned predictive analytics as a critical enabler of smarter, faster, and more informed decision-making across industries. This paper examines how the integration of Artificial Intelligence (AI) and Machine Learning (ML) enhances predictive analytics and, in turn, strengthens organizational decision-making. Drawing on a systematic literature review of 50 scholarly works spanning banking, business process management, healthcare, marketing, supply chain management, and business intelligence, the study synthesizes key findings on the capabilities, applications, and challenges of AI- and ML-driven predictive analytics. The review reveals that AI and ML significantly improve forecasting accuracy, enable real-time insights, and automate complex analytical tasks, thereby supporting risk management, resource optimization, and customer personalization. Building on these insights, the paper proposes a conceptual framework in which AI and ML act as technological enablers that enhance predictive analytics, which then functions as a mediating mechanism leading to improved organizational decision-making. The framework also recognizes critical moderating challenges, including data quality, algorithmic bias, and model interpretability, which can weaken or distort the benefits of predictive analytics if left unaddressed. The discussion highlights both the transformative potential and the ethical, technical, and organizational constraints associated with AI-powered predictive systems. The study contributes by offering an integrated, cross-sectoral synthesis and a clear conceptual model that can guide future empirical research and practical implementation. It underscores the need for responsible AI practices, robust data governance, and organizational readiness to fully realize the value of AI- and ML-enabled predictive analytics in enabling smarter, responsible, and sustainable decisions.