Human–AI Collaboration in Product Management: A Systematic Review of AI-Augmented Decision-Making Across the Product Lifecycle

Artificial Intelligence (AI) is gradually changing the landscape of product management by improving how PMs gather, process, and evaluate data and options to manage products across their lifecycle. This review explores the new paradigm of Human-AI collaboration in the product management domain and looks at how AI functions effectively on the side of the product manager. It merges a variety of machine learning, predictive analytics, NLP, recommender systems, generative AI, and emerging agentic AI applications in customer and market discovery, product ideation, strategy and prioritization, road mapping, product development, product launch, and post-launch optimization. Specific focus is placed on task distribution, responsibilities and decision-making power between product manager and AI system. The study suggests that AI can offer many benefits in areas such as data analysis, pattern recognition, prediction, information synthesis, content creation, recommendations, and automation, while human involvement is still crucial for aspects of context interpretation, strategic decision-making, creativity, empathy, ethical considerations, and accountability. Yet, collaboration is hindered by issues such as algorithmic bias, hallucination, opacity, automation bias, over-reliance, privacy issues, deskilling and unclear accountability. The review thus suggests a framework of integration between product lifecycle activities, AI capabilities, human expertise, collaboration mechanisms, governance and organizational outcomes. The framework also emphasizes how product managers will be moving away from AI-enabled product management and toward agentic systems where product managers will take on more supervisory, orchestrating, evaluating and governing roles. Lastly, the review pinpoints research priorities related to human-AI task allocation, trust calibration, autonomous agents, measurement of collaboration, responsible AI, and end-to-end empirical evaluation.

Artificial Intelligence, Human–AI Collaboration, Product Management, AI-Augmented Decision-Making, Generative AI, Agentic AI, Decision-Making, Product Lifecycle, AI Governance.

S. Majumder and N. Dey, AI-Empowered Knowledge Management. Berlin/Heidelberg, Germany: Springer, 2022.

M. A. Ferrag, N. Tihanyi, and M. Debbah, “From LLM reasoning to autonomous AI agents: A comprehensive review,” IEEE Access, 2026.

D. Kandemir and N. Acur, “Examining proactive strategic decision-making flexibility in new product development,” Journal of Product Innovation Management, vol. 29, no. 4, pp. 608–622, 2012.

N. L. Rane et al., “Artificial intelligence, machine learning, and deep learning for advanced business strategies: A review,” Partners Universal International Innovation Journal, vol. 2, no. 3, pp. 147–171, 2024.

J. Ferdousi, M. Shokran, and M. S. Islam, “Designing human–AI collaborative decision analytics frameworks to enhance managerial judgment and organizational performance,” Journal of Business and Management Studies, vol. 8, no. 1, pp. 1–19, 2026.

A. Witkowski and A. Wodecki, “Where does AI play a major role in the new product development and product management process?,” Management Review Quarterly, pp. 1–38, 2025.

Y. Han et al., “Measuring the impact of human–AI collaboration on knowledge diffusion in new product development projects,” Frontiers of Engineering Management, vol. 12, no. 4, pp. 899–915, 2025.

S. Herath Pathirannehelage, Y. R. Shrestha, and G. von Krogh, “Design principles for artificial intelligence-augmented decision making: An action design research study,” European Journal of Information Systems, vol. 34, no. 2, pp. 207–229, 2025.

P. Hemmer et al., “Complementarity in human-AI collaboration: Concept, sources, and evidence,” European Journal of Information Systems, vol. 34, no. 6, pp. 979–1002, 2025.

C. Gonzalez and H. Heidari, “A cognitive approach to human–AI complementarity in dynamic decision-making,” Nature Reviews Psychology, vol. 4, no. 12, pp. 808–822, 2025.

Y. Ren, X. Deng, and K. D. Joshi, “Unpacking human and AI complementarity: Insights from recent works,” ACM SIGMIS Database: The DATABASE for Advances in Information Systems, vol. 54, no. 3, pp. 6–10, 2023.

Z. He et al., “Interaction of thoughts: Towards mediating task assignment in human-AI cooperation with a capability-aware shared mental model,” in Proc. 2023 CHI Conf. Human Factors in Computing Systems, 2023.

L. Wang et al., “Artificial intelligence in product lifecycle management,” The International Journal of Advanced Manufacturing Technology, vol. 114, no. 3, pp. 771–796, 2021.

A. T. Rosário and J. C. Dias, “AI-driven consumer insights in business: A systematic review and bibliometric analysis of opportunities and challenges,” International Journal of Marketing, Communication and New Media, vol. 15, 2025.

L. Pretorius and C. Pretorius, “Exploring ChatGPT’s potential as a qualitative research partner: Researcher and participant perspectives on AI-generated insights,” Qualitative Research in Psychology, pp. 1–33, 2025.

Z. Cai, Z. Wang, and M. Murad, “AI-enabled decision making and social intrapreneurial opportunity identification in SMEs: The roles of circular economy application and pro-social environmental orientation,” Corporate Social Responsibility and Environmental Management, 2026.

A. Castrounis, AI for People and Business: A Framework for Better Human Experiences and Business Success. Sebastopol, CA, USA: O’Reilly Media, 2019.

S. Hussain, S. Shahid, and M. A. Hamza, “When brands listen back: Adaptive marketing systems, consumer feedback loops, and the emergence of responsive market intelligence,” Inverge Journal of Social Sciences, vol. 5, no. 1, pp. 215–226, 2026.

A. Raj and R. Deora, “AI and ML powered feature prioritization in software product development,” International Journal of Data Mining & Knowledge Management Process (IJDKP), vol. 15, no. 1, pp. 23–30, 2025.

A. Morozumi and H. Hayashi, “LLM-based risk scenario generation and mitigation for AI systems: A case study approach,” in Proc. International Joint Conference on Computational Intelligence. Cham, Switzerland: Springer Nature Switzerland, 2025.

H. Meng et al., “A documentation-driven framework for AI-assisted full-circle agile software development,” in Proc. 2025 International Conference on Intelligent Education and Intelligent Research (IEIR). IEEE, 2025.

B. Singh and C. Kaunert, “AI-driven strategies for customer engagement, market segmentation, and resource optimization: Projecting end user satisfaction and futuristic growth of business,” in AI Innovations in Service and Tourism Marketing. Hershey, PA, USA: IGI Global Scientific Publishing, 2024, pp. 104–128.

M. R. Anderson et al., “AI-based product quality monitoring and recall prevention systems.”

I. Ali et al., “Human–AI collaboration in knowledge ecosystems: A multidisciplinary review, integrative framework and future directions,” Journal of Knowledge Management, pp. 1–22, 2025.

G. Romeo and D. Conti, “Exploring automation bias in human–AI collaboration: A review and implications for explainable AI,” AI & Society, vol. 41, no. 1, pp. 259–278, 2026.

O. G. Nwashili, “Scaling AI features in large organizations: A product management perspective,” IRASS Journal of Economics and Business Management, vol. 2, no. 12, pp. 23–30, 2025.

R. Sharma, “AI-augmented marketing decision-making and competitive performance: A resource-based view of capability orchestration,” Acta Psychologica, vol. 268, Art. no. 107351, 2026.

S. Gumber, “The AI-augmented product lifecycle: How generative and agentic AI transform ideation, prioritization, prototyping, and testing,” Power System Protection and Control, vol. 54, no. 3, pp. 290–305, 2026.

N. A. Parikh, “Agentic AI in product management: A co-evolutionary model,” in High Performance Leadership for Organizational Excellence: Innovation, Technology, and Resilience. Hershey, PA, USA: IGI Global Scientific Publishing, 2026, pp. 1–36.

M. A. Al-Bashrawi et al., “Agentic AI systems and the future of entrepreneurship: A perspective on co-agency, innovation, and ecosystem transformation,” International Entrepreneurship and Management Journal, vol. 22, no. 1, p. 27, 2026.

H. Li and F. Tian, “Advancing decision-making through AI-human collaboration: A systematic review and conceptual framework,” Group Decision and Negotiation, vol. 35, no. 2, p. 26, 2026.