AI-Driven Product Management Assistants: Co-Pilot or Competitor for the Future PM?

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 12 | Issue : 02 | Page : –
    By

    Anu Rai,

  1. Research Scholar, Department of Information Technology, M.S. in Information Technology Management, The University of Texas, Dallas, Texas, USA

Abstract

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_185080’);});Edit Abstract & Keyword

The emergence of Artificial Intelligence has led to a big transformation in the field of product management. When AI first came into picture, it was limited to the routine tasks but now AI assistants are able to handle a lot of decision making and streamline the execution process involved in product management. A major question with this emerging technology: can these assistants now or in future handle the work of product managers and replace them? In this study, I am going to address this question, review the major functions of product manager like roadmap prioritization, product vision and strategy, data driven decision making and market research. This study will also define AI and its impacts and how product management can be made efficient by using the AI tools. This study talks about AI becoming copilot for product managers to make them more efficient or AI being a competitor. While AI can help PMs in many tasks, the field of product management is so much more than automating tasks. The core aspects of these roles are collaborating with the cross functional teams, human judgement, navigating through organizational dynamics, innovation and creativity and therefore, this study will go into the depths of impact of AI on product managers.

Keywords: Product management, artificial intelligence, task automation, copilots, AI-agents, artificial intelligence; sentiment analysis; data analytics; machine learning, product managers, product lifecycle management, product development lifecycle, decision making

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Anu Rai. AI-Driven Product Management Assistants: Co-Pilot or Competitor for the Future PM?. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):-.
How to cite this URL:
Anu Rai. AI-Driven Product Management Assistants: Co-Pilot or Competitor for the Future PM?. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):-. Available from: https://journals.stmjournals.com/joaira/article=2025/view=0



document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_185080’);});Edit

References

  1. Witkowski A, Wodecki A. An Exploration of the Applications, Challenges, and Success Factors in AI-Driven Product Development and Management. Found Manag. 2024; 16(1): 139–156.
  2. Khare P, Srivastava S. The Impact of AI on Product Management: A Systematic Review and Future Trends. Int J Res Anal Rev. 2022; 9(4): 736–41.
  3. Wang L, Liu Z, Liu A, Tao F. Artificial intelligence in product lifecycle management. Int J Adv Manuf Technol. 2021 May; 114: 771–96.
  4. Panyam S, Gujar P, Paliwal G. Evolving Product Engineering Teams in the AI Era. IEEE Eng Manag Rev. 2024 Aug 13; 1–6.
  5. Maglyas A, Nikula U, Smolander K, Fricker SA. Core software product management activities. J Adv Manag Res. 2017 Feb 6; 14(1): 23–45.
  6. Tao F, Wang Y, Zuo Y, Yang H, Zhang M. Internet of Things in product life-cycle energy management. J Ind Inf Integr. 2016 Mar 1; 1: 26–39.
  7. Schreck B, Mallapur S, Damle S, James NJ, Vohra S, Prasad R, Veeramachaneni K, Doe J. The AI project manager. In IEEE International Conference on Big Data. 2018 Jan.
  8. Wang X. Analysis of Distributed Nodes of Logistics Management Information System Based on Supply Chain Management. In 2021 IEEE Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). 2021 Nov 11; 984–987.
  9. Ogundipe DO, Babatunde SO, Abaku EA. AI and product management: A theoretical overview from idea to market. Int J Manag Entrep Res. 2024; 6(3): 950–69.
  10. Qin M. Innovation of Enterprise Economic Management Based on Artificial Intelligence Technology. In 2020 IEEE International Conference on Computers, Information Processing and Advanced Education (CIPAE). 2020 Oct 16; 125–129.
  11. McMillan AJ, Swindells N, Archer E, McIlhagger A, Sung A, Leong K, Jones R. A review of composite product data interoperability and product life-cycle management challenges in the composites industry. Adv Manuf: Polym Compos Sci. 2017 Oct 2; 3(4): 130–47.
  12. Cooper RG, Brem AM. Insights for Managers About AI Adoption in New Product Development. Res-Technol Manag. 2024 Nov 1; 67(6): 39–46.
  13. Goyal E, Srivastava S. Study on Customer Engagement Model-Banking Sector. SIES Journal of Management (SIESJOM). 2015 Mar 1; 11(1).
  14. Siau KL, Yang Y. Impact of artificial intelligence, robotics, and machine learning on sales and marketing. MWAIS 2017 Proceedings 48. 2017.
  15. Sazu MH. Does big data drive innovation in E-commerce: A global perspective? SEISENSE Business Review (SBR). 2022 Jun 30; 2(1): 55–66.
  16. Nichols E, Gao L, Vasylkiv Y, Gomez R. Design and analysis of a collaborative story generation game for social robots. Front Comput Sci. 2021 Oct 25; 3: 674333.
Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 17/01/2025
Accepted 15/03/2025
Published 19/04/2025
Publication Time 92 Days
68

[last_name]

My IP

PlumX Metrics