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Applied Sciences OPEN ACCESS

The Rise of the "Product-Program" Manager: How AI and Analytics Are Merging Two Critical Roles

Latypov Viacheslav
Senior Project Manager, PMO Lead/Hercules Dynamics Toronto, Canada
DOI Crossref DOI 10.37547/tajas/Volume07Issue11-07
tajas 2025
VOL. 7 / NO. 11 NOVEMBER
FULL TEXT PP. 61-69
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VOLUME 7
ISSUE 11
YEAR 2025
PAGES 61-69
RESEARCH OVERVIEW

Abstract

The article presents a theoretical and analytical review of the transformation of management practices in the context of implementing artificial intelligence and advanced analytics. The study is based on an interdisciplinary approach that combines methods of strategic management, innovation analysis, and digital technologies. Particular attention is paid to the formation of the hybrid role of the "product-program manager," which integrates the functions of product and program management. Scenarios of AI applications are examined, including the automation of routine management processes and predictive resource allocation. A comparative analysis of traditional and hybrid managerial roles is conducted, with an emphasis on the changing role of data and the integration of analytical platforms into business processes. It is established that the implementation of AI ensures a shift from reactive to predictive management, contributes to cost reduction and faster time-to-market, and strengthens the manager’s role as an architect of the innovation cycle. Identified limitations are associated with differences in technological maturity and the need to adapt organizational structures. The article proposes a conceptual model of the evolution of managerial functions based on AI, integrating strategy, tactics, and operational execution into a single analytical framework. This work will be useful to researchers in innovation management, practitioners of digital transformation, analytics specialists, and project leaders interested in building sustainable business models in the digital economy.

RESEARCH TOPICS

Keywords

artificial intelligence data analytics product management program management product-program manager innovation cycle digital transformation

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References

  1. Babina, T., Fedyk, A., He, A., & Hodson, J. (2024). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 151, 103745. https://doi.org/10.1016/j.jfineco.2023.103745
  2. Barcaui, A., & Monat, A. (2023). Who is better in project planning? Generative artificial intelligence or project managers? Project Leadership and Society, 4, 100101. https://doi.org/10.1016/j.plas.2023.100101
  3. Fichtler, T., Kirchberg, L., Grigoryan, K., Koldewey, C., & Dumitrescu, R. (2024). A method for identifying use cases in data-driven product management. Procedia CIRP, 122, 539–544. https://doi.org/10.1016/j.procir.2024.01.079
  4. Georgiev, S., Polychronakis, Y., Sapountzis, S., & Polychronakis, N. (2024). The role of artificial intelligence in project management: A supply chain perspective. Supply Chain Forum: An International Journal, 1–14. https://doi.org/10.1080/16258312.2024.2384823
  5. Grigoryan, K., Fichtler, T., Schreiner, N., Rabe, M., Panzner, M., Kühn, A., Dumitrescu, R., & Koldewey, C. (2023). Data-driven product management: A practitioner-driven research agenda. Procedia CIRP, 119, 290–295. https://doi.org/10.1016/j.procir.2023.03.099
  6. Mariani, M., & Dwivedi, Y. K. (2024). Generative artificial intelligence in innovation management: A preview of future research developments. Journal of Business Research, 175, 114542. https://doi.org/10.1016/j.jbusres.2024.114542
  7. Marnewick, C., & Marnewick, A. L. (2022). Digitalization of project management: Opportunities in research and practice. Project Leadership and Society, 3, 100061. https://doi.org/10.1016/j.plas.2022.100061
  8. Panzner, M., von Enzberg, S., & Dumitrescu, R. (2024). Developing a data analytics toolbox for data-driven product planning: A review and survey methodology. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 38, e18. https://doi.org/10.1017/S0890060424000209
  9. Qin, W., Zhang, Y., Qu, T., & Li, X. (2022). Special issue on data-driven modeling and analytics for optimization of complex manufacturing systems. International Journal of Computer Integrated Manufacturing, 35(10–11), 1025–1027. https://doi.org/10.1080/0951192X.2022.2141948
  10. Roberts, D. L., & Candi, M. (2024). Artificial intelligence and innovation management: Charting the evolving landscape. Technovation, 136, 103081. https://doi.org/10.1016/j.technovation.2024.103081
  11. Stark, D., & Vanden Broeck, P. (2024). Principles of algorithmic management. Organization Theory, 5(2). https://doi.org/10.1177/26317877241257213