Leveraging Artificial Intelligence for Predictive Change Management in Information Systems Projects
Keywords:
Artificial Intelligence, Predictive Change Management, Information systemsAbstract
This research paper explores the application of artificial intelligence (AI) in predictive change management for information systems (IS) projects. Change management is a critical aspect of IS project success, yet it remains challenging due to the complex and dynamic nature of organizational environments. This study investigates how AI technologies can be leveraged to predict and proactively manage change in IS projects, potentially improving project outcomes and reducing resistance to change. This research builds on existing literature and methodologies, particularly inspired by the work of Ambati et al. (2020), which examines factors influencing AI adoption in organizations. The study employs a mixed-methods approach, combining quantitative data analysis with qualitative case studies to evaluate the effectiveness of AI-driven change management strategies. Our findings suggest that AI can significantly enhance change management effectiveness by providing data-driven insights, identifying potential risks, and enabling personalized change strategies. However, successful implementation requires addressing technical, organizational, and ethical considerations. This research contributes to the growing body of knowledge on AI applications in project management and offers practical implications for IS project managers and change management practitioners.
Downloads
References
Ananiadou, S., Thompson, P., & Nawaz, R. (2013). Enhancing search: Events and their discourse context. In International Conference on Intelligent Text Processing and Computational Linguistics (pp. 318-334). Springer.
Ambati, L. S., Narukonda, K., Bojja, G. R., & Bishop, D. (2020). Factors influencing the adoption of artificial intelligence in organizations–from an employee’s perspective.
Benbya, H., Nan, N., Tanriverdi, H., & Yoo, Y. (2020). Complexity and information systems research in the emerging digital world. MIS Quarterly, 44(1), 1-17.
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 7, 3-11.
Chui, M., Manyika, J., & Miremadi, M. (2018). What AI can and can't do (yet) for your business. McKinsey Quarterly, 1, 96-108.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707.
Hornstein, H. A. (2015). The integration of project management and organizational change management is now a necessity. International Journal of Project Management, 33(2), 291-298.
Kotter, J. P. (1996). Leading change. Harvard Business Press.
Lewin, K. (1947). Frontiers in group dynamics: Concept, method and reality in social science; social equilibria and social change. Human Relations, 1(1), 5-41.
Markus, M. L., & Benjamin, R. I. (1997). The magic bullet theory in IT-enabled transformation. Sloan Management Review, 38(2), 55-68.
Pinto, J. K., & Winch, G. (2016). The unsettling of "settled science:" The past and future of the management of projects. International Journal of Project Management, 34(2), 237-245.
Rivard, S., & Lapointe, L. (2012). Information technology implementers' responses to user resistance: Nature and effects. MIS Quarterly, 36(3), 897-920.
Standish Group. (2020). CHAOS Report 2020: Beyond Infinity. The Standish Group International, Inc.
Williams, T., & Gong, J. (2014). Predicting construction cost overruns using text mining, numerical data and ensemble classifiers. Automation in Construction, 43, 23-29.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of research papers submitted to Distributed Learning and Broad Applications in Scientific Research retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agree to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. Scientific Research Canada disclaims any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
If you have any questions or concerns regarding these license terms, please contact us at editor@dlabi.org.
