Artificial Intelligence in Business Research: A Critical Review of Current Developments, Challenges and Future Directions

Artificial Intelligence (AI) has been at the forefront of technological developments influencing business research. Advances in machine learning, deep learning, natural language processing, predictive analytics and generative artificial intelligence (GenAI) have enabled researchers to process increasingly complex data, automate analytical tasks and generate deeper insights. This study conducts a critical review of the evolution, applications, opportunities and challenges of AI in business research. Drawing upon contemporary literature from management, marketing, strategy, innovation, operations, finance and human resource management, the review synthesises knowledge regarding AI-driven decision-making, predictive analytics and knowledge creation. The review finds that AI improves research efficiency and supports evidence-based managerial decision-making. However, the literature highlights significant concerns relating to algorithmic bias, explainability, transparency, privacy, governance and research integrity. Existing scholarship is fragmented across disciplines and provides limited empirical evidence regarding the long-term implications of generative AI for research quality, organisational learning, governance effectiveness and human-AI collaboration. The study argues that AI should be viewed as a complementary capability that augments rather than replaces human expertise. By integrating evidence across multiple business functions, the review identifies critical knowledge gaps and develops a future research agenda focused on responsible governance, explainable AI and sustainable human-AI collaboration.