Artificial Intelligence Adoption and Employee Performance: The Role of Human–AI Collaboration

Artificial intelligence (AI) adoption has become a defining feature of contemporary digital transformation. Organizations increasingly deploy AI-enabled systems to automate routine work, support decision-making, improve service quality, enhance creativity, and increase productivity. However, the performance effects of AI adoption are not automatic. Recent empirical studies show that AI can significantly improve productivity and work quality, but they also reveal that AI may reduce performance when employees over-rely on inaccurate outputs, use AI for unsuitable tasks, or lack sufficient AI literacy. This study examines the relationship between AI adoption and employee performance, focusing on the mediating role of human–AI collaboration. Using a desk-based qualitative research design, the study synthesizes prior studies from Web of Science, Scopus, EBSCO, and related academic databases. The review focuses mainly on studies published between 2021 and 2026, while retaining several foundational theories. The screening process moved from 489 initial records to 134 studies after preliminary screening and 48 final studies for thematic analysis, including 25 quantitative, 13 qualitative, 6 mixed-method, and 4 theoretical studies. The thematic analysis identifies five major themes: AI adoption as a productivity-enhancing mechanism, human–AI collaboration as an augmentation process, trust and appropriate reliance, task–technology fit, and organizational support for AI-enabled performance. The study argues that AI adoption improves employee performance most effectively when employees and AI systems collaborate through complementary roles. AI provides speed, scale, prediction, and generative capacity, while employees provide contextual judgment, creativity, ethical reasoning, and final accountability. The study contributes to AI management research by positioning human–AI collaboration as the central mechanism linking AI adoption to employee performance.