ARTIFICIAL INTELLIGENCE IN BUSINESS INTELLIGENCE: ENHANCING PREDICTIVE WORKFORCE AND OPERATIONAL ANALYTICS

Abstract

This study systematically examines the integration of Artificial Intelligence (AI) within Business Intelligence (BI), focusing on its role in enhancing predictive workforce and operational analytics across organizational contexts. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 146 peer-reviewed articles were reviewed, providing a comprehensive evidence base spanning human resource management, operational optimization, and global policy implications. The findings reveal that AI augments BI by moving beyond descriptive analytics toward predictive and prescriptive models, enabling organizations to forecast workforce dynamics, optimize operational processes, and strengthen decision-making capacity. In workforce contexts, AI-driven BI enhances recruitment and selection through automated screening and candidate matching, reduces bias in hiring decisions, predicts employee turnover risks, and supports personalized career development through adaptive learning systems. In operational domains, AI facilitates predictive maintenance by analyzing sensor data to anticipate equipment failures, optimizes supply chains through demand forecasting and logistics modeling, and strengthens risk management through real-time crisis simulations and disruption forecasting. At the international level, the literature shows divergent but complementary applications, with developed economies emphasizing advanced applications in finance, healthcare, and retail, while emerging economies leverage AI-BI integration for workforce planning, resource optimization, and developmental challenges. Multinational organizations benefit from cross-border workforce analytics that harmonize performance measurement across diverse regulatory and cultural contexts, and international agencies apply predictive workforce analytics to inform labor policy and socio-economic planning. The study also situates these findings within established theoretical frameworks, including Resource-Based Theory, Socio-Technical Systems Theory, Knowledge-Based View, Decision Support Systems Theory, and Human Capital Theory, demonstrating that AI-augmented BI represents both a strategic resource and a socio-technical innovation. Collectively, the review underscores that the integration of AI into BI is not merely a technological enhancement, but a paradigm shift that enables organizations to transform raw data into actionable knowledge, anticipate challenges, and sustain competitive advantage through predictive workforce and operational analytics.