Generative AI and management analytics: survey

Research on generative artificial intelligence (GenAI) has reached a critical point at which widespread organizational uptake is coinciding with a rapid reconfiguration of management analytics as both a technical capability and a managerial interface for decision-making. Yet the scholarly base remains fragmented across functions, levels of analysis, and model families, limiting cumulative theory building and providing insufficient guidance on which research fronts are central, which are emergent, and how they map onto organizational and societal trajectories. To address this gap, we conduct a combined bibliometric science-mapping and systematic review of GenAI-related management research indexed in Web of Science and IEEE Xplore from 2020 to 2026. We organize the literature into interconnected research streams spanning analytics-as-interface for decision support, knowledge and innovation work, human capital and skill transformation, operations and coordination, and governance and risk at the human-AI boundary. Using maturity-centrality diagnostics, the diagnostics suggest that augmentation-oriented streams are relatively mature and structurally central, whereas agentic GenAI constitutes a strategically salient but underdeveloped frontier with direct implications for responsibility allocation, control systems, and organizational design. The study delineates a distinct analytical territory linking GenAI to management analytics as a driver of forecastable organizational and societal change, and it advances an agenda that prioritizes testable mechanisms (data readiness, workflow redesign, and governance) and multi-level outcomes relevant to practitioners and policymakers.