The study examines how artificial intelligence reshapes entrepreneurial activity and new-product trajectories across discovery, design, launch, and post-launch learning. The contribution lies in consolidating recent evidence on AI’s impact on opportunity formation, decision flows, and governance in risk-sensitive and regulated domains such as mental health, education, and organizations and products across sectors. The review describes performance gains from generative systems in knowledge work, the structuring value of multi-agent orchestration, and the strategic implications for data provenance, evaluation discipline, and go-to-market execution. Special attention is paid to cross-phase integration, where insight artifacts travel intact from qualitative discovery to modeling and telemetry-guided iteration. The goal is to derive an operator’s framework that aligns research signals with venture routines and product leadership choices. Methods include comparative synthesis of peer-reviewed sources, critical appraisal, and concept mapping. The conclusion outlines a reproducible stack for AI-native entrepreneurship that balances speed with auditability and trust, relevant for founders and product leaders.

AI entrepreneurship, multi-agent systems, product data and user- and process-interaction signals, innovation management

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