Artificial intelligence in business management: A systematic thematic literature review of emerging applications, research gaps, and future directions

Abstract

In recent years, Artificial Intelligence (AI) has turned into a game-changer for the way companies manage their affairs in today's world, offering improved strategic decision-making, operational efficiency, innovation, and organizational competitiveness. While there is a large volume of research being focused on AI, the literature is scattered across various business disciplines, making it difficult to understand the intellectual structure of the field, the key themes in AI research, and future research directions. The objectives of this study are to systemically synthesize the existing knowledge on the use of AI tools in business administration, identify the predominant thematic areas, highlight existing gaps in research and propose an extensive research agenda for the future. A Systematic Thematic Literature Review (STLR) was done based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Peer-reviewed publications from 2015 to 2026 were identified from both Scopus and Web of Science databases based on the inclusion and exclusion criteria. Thematic synthesis was used to identify patterns, developments in concepts, and trends in the research in the selected studies. The review reveals eight broad research areas: AI-driven decision making, marketing and consumer analytics, human resource management, financial management, supply chain and operations management, ethical AI governance, human–AI collaboration, and strategic organizational transformation. The results show that AI has progressed from a basic operational automation solution to a key organizational capability which supports the processes of digitalization, improves business performance, and provides sustainable competitive advantage. There are still key research gaps in how cross-functional integration of AI can be achieved, the adoption of Generative AI, Explainable AI (XAI), the governance of AI responsibly, addressing sustainability issues, the implementation of AI in small and medium-sized enterprises (SMEs), as well as empirical data from developing economies.   The current study makes a valuable contribution to the existing literature in the following ways: it integrates the seemingly disconnected research in the field, elaborates on the strategic importance of AI in business management, and suggests a research agenda that is structured and organized. The results provide theoretical insights for researchers and practical guidance for managers and policymakers aiming to foster sustainable organization change while responsibly implementing AI.