Artificial Intelligence and Employee Innovation: A Literature Review

Artificial intelligence (AI), particularly generative AI, is increasingly embedded in knowledge work, creative work, customer service, product development, human resource management, and organizational decision systems. Although research shows that AI can raise productivity, support creative output, accelerate searching, and reorganize innovation work, the evidence remains fragmented across innovation management, organizational behavior, information systems, human resource management, economics, human-computer interaction, and behavioral sciences. This framework-based review synthesizes high-quality English-language journal articles on AI, human-AI collaboration, employee creativity, innovative work behavior, algorithmic management, and innovation management. Building on established models of individual innovation, it develops a worker-centered framework linking four innovation stages with three higher-order AI functions: task execution, knowledge support, and organizational governance. The evidence is strongest for searching, idea generation, creativity, and short-term task performance, and remains limited for idea promotion, implementation, diffusion, and realized innovation outcomes. The synthesis identifies three central tensions: performance equalization versus capability differentiation, short-term quality versus long-term capability development, and individual improvement versus collective homogenization. AI can enhance innovation-related work through cognitive expansion, search acceleration, productivity release, recombination, feedback, and learning, but it can also reduce output diversity, weaken expertise development, intensify work, increase dependence, and shift managerial control over creative labor. The review positions AI as both a cognitive resource and an innovation-governance mechanism and outlines directions for future research on mechanisms, heterogeneity, novelty, capability accumulation, and responsible governance.