Human-artificial intelligence for organizational hyper-performance: a systematic literature review

Abstract

Human-artificial intelligence (AI) collaboration has become an important topic in organizational management, as AI technologies are increasingly used in decision-making and everyday work activities. Although interest in this topic has grown in recent years, the literature remains fragmented and does not clearly explain how human-AI integration supports performance beyond basic efficiency improvements. This paper explores how this interaction can support organizational hyper-performance. The study is based on a systematic literature review of peer-reviewed business and management research. The analysis incorporates 80 studies, indexed in Scopus and Web of Science from 2019 to 2026. The searches were updated on 16 February 2026. Conceptual, qualitative, and quantitative studies are examined using thematic analysis. The findings show that hyper-performance does not result automatically from AI adoption. Instead, it depends on how organizations design decision-making processes and assign roles and tasks between humans and AI systems. Clear human-AI complementarity and well-defined decision-making structures are key enabling factors. The study concludes that organizational hyper-performance through human-AI integration is possible only under certain conditions and that clearer concepts and practical guidelines are needed for both research and management practice.