The Impact of Artificial Intelligence on Business Management and Organizational Productivity: Opportunities, Challenges, and Future Perspectives

Artificial intelligence (AI) is increasingly embedded in business processes, managerial decisions, and employee workflows, yet the relationship between adoption and organizational productivity is neither direct nor uniform. This paper critically examines how AI affects business management and productivity by integrating peer-reviewed scholarship with evidence from workplace experiments, organizational studies, and reports from the OECD, International Labour Organization (ILO), International Monetary Fund (IMF), and National Institute of Standards and Technology (NIST). The review distinguishes task-level performance from firm-level productivity and separates measured effects from forecasts and conceptual arguments. A randomized writing experiment and a workplace rollout find gains in specific writing and customer-support tasks, but a preregistered meta-analysis shows that human-AI combinations do not reliably outperform the stronger individual agent, particularly on decision tasks. Organizational benefits therefore depend on task fit, data and process quality, complementary skills, work redesign, and accountable governance. AI can reduce routine cognitive effort, broaden analytical capacity, support forecasting and personalization, and improve access to expertise. At the same time, it can introduce errors at scale, intensify work, redistribute control, reproduce historical bias, expose sensitive information, and create costs that are poorly captured by short-term productivity measures. A socio-technical and capability-based framework is proposed, together with research questions and testable hypotheses for future empirical work. The paper concludes that firms should evaluate Al as an organizational change rather than a software purchase, using staged, task-specific implementation and balanced measures of output, quality, resilience, job quality, and risk.