Author
Abstract
Starting today and due to the advent of generative AI, traditional enterprise portfolio management (EPM) is seriously disrupted, creating new opportunities for organizations to accelerate strategic alignment, optimize resource allocation, and foster innovation. This paper explores how generative AI will have the transformative power to create actionable insights and improve decision-making in EPM. Therefore, organizations can surpass traditional portfolio management's limitations and adapt to dynamic and real-time adjustments enabled by the use of predictive analytics and scenario modeling through AI-driven tools. AI's role in enhancing portfolio optimization, risk management, and project prioritization is discussed in this article, alongside challenges centered on data quality, workforce, and ethical considerations. It further explains the integration between AI and the existing enterprise system to provide a more encompassing, flexible approach to portfolio management. The paper applies generative AI in the real world through case studies from some leading organizations, which show how generative AI has been used to optimize portfolio performance, manage risks, and allocate resources efficiently. The paper ends with the vision for the future of EPM through generative AI, where generative AI powers more agile and data-driven decision-making processes. With each growing step of AI technology, it follows that businesses need to invest money to benefit from AI tools and train their staff for the transformation of AI-enhanced portfolio management.
Suggested Citation
Mudappallur Raman Venkateswaran & Sangeetha Govindarajan & Nithya Krishnan, 2024.
"The Future of Enterprise Portfolio Management in the Age of Generative AI,"
Journal of Business and Management Studies, Al-Kindi Center for Research and Development, vol. 6(4), pages 286-303, June.
Handle:
RePEc:alk:jbms00:v:6:y:2024:i:4:id:8757
DOI: 10.32996/jbms.2024.6.4.21
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:alk:jbms00:v:6:y:2024:i:4:id:8757. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Al-Kindi Center for Research and Development (email available below). General contact details of provider: https://al-kindipublisher.com/index.php/jbms .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.