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Exploring the Integration of Artificial Intelligence and Sustainability Practices in Project Management: Challenges and Opportunities

by Chekole Sete Demeke
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 73
Year of Publication: 2025
Authors: Chekole Sete Demeke
10.5120/ijca2025924600

Chekole Sete Demeke . Exploring the Integration of Artificial Intelligence and Sustainability Practices in Project Management: Challenges and Opportunities. International Journal of Computer Applications. 186, 73 ( Mar 2025), 34-42. DOI=10.5120/ijca2025924600

@article{ 10.5120/ijca2025924600,
author = { Chekole Sete Demeke },
title = { Exploring the Integration of Artificial Intelligence and Sustainability Practices in Project Management: Challenges and Opportunities },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2025 },
volume = { 186 },
number = { 73 },
month = { Mar },
year = { 2025 },
issn = { 0975-8887 },
pages = { 34-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number73/exploring-the-integration-of-artificial-intelligence-and-sustainability-practices-in-project-management-challenges-and-opportunities/ },
doi = { 10.5120/ijca2025924600 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-03-25T22:41:34.294060+05:30
%A Chekole Sete Demeke
%T Exploring the Integration of Artificial Intelligence and Sustainability Practices in Project Management: Challenges and Opportunities
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 73
%P 34-42
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The convergence of artificial intelligence (AI) and sustainability is rapidly transforming various sectors, and project management is no exception. This literature review explores the burgeoning intersection of AI and sustainability practices in project management, examining the challenges and opportunities presented by their integration. The study is a comprehensive review of existing literature; it does not involve any primary data collection or experimentation. Key themes emerging from the reviewed literature include the potential of AI to enhance sustainable project planning, optimize resource management, automate monitoring and reporting, and improve stakeholder engagement. However, the integration is not without significant obstacles, such as technical complexities in data handling, organizational resistance to change, and ethical considerations relating to bias and transparency in AI algorithms. This review also identifies gaps in current research and the need for further investigation into ethical implications and the development of standardized methodologies for assessing the impact of AI-driven sustainability solutions in projects. The findings of this review are crucial for project managers, researchers, and organizations seeking to leverage AI to drive sustainable project outcomes and navigate the complex landscape of technological advancements in project management.

References
  1. Silvius, A.J.G., 2020. Sustainability in project management: a practice-based perspective. Routledge.
  2. Gareis, R. and Huemann, M., 2023. Sustainable Project Management: Theory and Practice. Routledge.
  3. El-Diraby, T.E. and Abdelrahman, M., 2021. AI in Construction Project Management. In Handbook of Construction Management (pp. 785-804). Edward Elgar Publishing.
  4. Bansal, S. and Kumar, A., 2023. Artificial intelligence in project management: a review of applications, benefits, challenges, and future research directions. International Journal of Managing Projects in Business, 16(2), pp.450-475.
  5. Sivakumar, R.V. and Shanmuganathan, M., 2021. Role of artificial intelligence in sustainable development. Materials Today: Proceedings, 37(2), pp.1260-1262.
  6. Marnewick, C.J., 2021. Incorporating sustainability into project management: A literature review. Project Management Journal, 52(4), pp.358-372.
  7. Vinuesa, R., Azizpour, H., Le Quere, C., Fyson, C. et al., 2020. The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), pp.1-10.
  8. Liu, Y., Zhu, M., Yuan, B. and Zhang, W., 2023. Enhancing sustainable project management using digital technology: a case study on BIM-enabled green building assessment. Sustainability, 15(13), p.10367.
  9. Wamba, S.F. and Akter, S., 2022. Artificial intelligence for sustainable supply chain management: challenges and opportunities. International Journal of Production Economics, 248, p.108481.
  10. Elkington, J. 1997. Cannibals with Forks: The Triple Bottom Line of 21st Century Business. Oxford: Capstone Publishing.
  11. Darko, A., Zhang, D. and Chan, A.P.C., 2021. Developing a machine learning model for predicting greenhouse gas emissions of construction projects. Journal of Cleaner Production, 312, p.127762.
  12. Hilty, L. M., & Aebischer, B. (2015). Digital infrastructure and sustainability: A conceptual framework. In Sustainability in the digital age: From ideas to implementation (pp. 193-214). Springer.
  13. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645-3660.
  14. Zhao, J. and Shi, C., 2023. AI-powered project risk management for sustainable construction: a bibliometric review and future research directions. International Journal of Environmental Research and Public Health, 20(2), p.1067
  15. Tzempelikos, A. (2023). The Limits of AI: A Critique of the Promise of Artificial Intelligence. AI & SOCIETY, 1-15.
  16. Sinding, K., Jørgensen, A., & Hauschild, M. Z. (2021). Data quality in life cycle assessment: Challenges and options. The International Journal of Life Cycle Assessment, 26(10), 2083-2096.
  17. Amponsah, F., Chen, Y., Wang, Y. and Opoku, A., 2024. Artificial Intelligence for Environmental Sustainability: A Review of Applications, Challenges, and Future Directions. IEEE Access, 12, pp.28706-28722.
  18. Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy and Buildings, 40(3), 394-398.
  19. Sinha, S., Kumar, S., & Verma, A. (2022). Application of machine learning techniques for waste reduction in manufacturing process. Materials Today: Proceedings, 64, 675-678.
  20. Bawden, D., & Robinson, L. (2024). The Role of Data Literacy in the Era of Artificial Intelligence. Journal of the Association for Information Science and Technology, 75(1), 1-14.
  21. Aghaei, S., Salimi, A., & Maleki, M. (2024). The cybersecurity risk analysis of artificial intelligence. Future Generation Computer Systems, 150, 1-10.
  22. Jain, S., & Jain, C. (2023). Applications of Artificial Intelligence in Sustainable Material Management. Materials Today: Proceedings, 77, 99-104.
  23. Lees, L., Chen, X. & Jones, N. (2023) ‘Data governance for AI-enabled sustainability performance monitoring: ethical principles and practical challenges’. Journal of Sustainable Development, 16(2), 56-73.
  24. Liang, J., Wang, L. & Zhao, B. (2024) ‘The role of natural language processing in enhancing sustainability reporting accuracy and reliability’. Journal of Environmental Management 345, 123895.
  25. Dimitrov, R. (2023). Artificial Intelligence and Misinformation: Implications for Regulation and Trust. Cambridge University Press.
  26. Reed, M. S., Graves, A., Dandy, N., Posthumus, H., Hubacek, K., Morris, J., ... & Stringer, L. C. (2009). Who's in and why? A typology of stakeholder analysis methods for natural resource management. Journal of environmental management, 90(5), 1933-1949.
  27. Li, X., Zhang, J., Zhang, X., & Wang, X. (2018). Text analysis and topic extraction on stakeholder feedback for enhancing project management. Applied Sciences, 8(11), 2191.
  28. Crawford, K. (2017). The trouble with bias. The New York Times, 26(6).
  29. Pfeffer, J. (2018). Dying for a paycheck: How modern management harms employee health and company performance—and what we can do about it. Harper Business.
  30. Gupta, R., & Kumar, R. (2020). Artificial intelligence in stakeholder engagement for sustainable infrastructure projects: A systematic review. Journal of cleaner production, 276, 123400.
  31. O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
  32. Agrawal, A., Gans, J. S., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.
  33. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2013). Disruptive technologies: Advances that will transform life, business, and the global economy. McKinsey Global Institute, 170, 1-144.
  34. Hagras, H., 2021. Artificial intelligence and sustainability: challenges and opportunities. International Journal of Information Management, 57, p.102254.
  35. Van Der Aalst, W. M. P. (2023). Process mining: data science in action. Springer.
  36. Brous, P., & Janssen, M. (2023). Addressing data quality issues for AI in data-driven government: Towards a data quality maturity model. Government Information Quarterly, 40(2), 101797.
  37. Chui, M., Manyika, J., & Miremadi, M. (2022). What it means to be a data - driven organization. McKinsey Quarterly, 2022(2), 66-77.
  38. Jain, S., Kumar, A., & Sharma, A. (2022). Robust and reliable AI through a multidisciplinary approach toward validation and verification. International Journal of Machine Learning and Cybernetics, 13(7), 1873-1886.
  39. Papadopoulos, T. and Gunasekaran, A., 2023. Emerging technologies for sustainable operations and supply chain management: a review. Production Planning & Control, 34(2-3), pp.119-140.
  40. Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2022). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 60, 102374.
  41. O'Leary, D. E. (2022). Artificial intelligence, and big data, and organizational strategy, and more. International Journal of Intelligent Systems in Accounting, Finance & Management, 29(1), 9-14.
  42. Wagstaff, K. L., Eickhoff, C., & Huszar, F. (2022). Mitigating Bias in Machine Learning. Synthesis Lectures on Human Language Technologies, 15(1), 1-386.
  43. Brynjolfsson, E., Mitchell, T., & Rock, D. (2023). The Coming Wave: Technology, Power, and the Twenty-first Century's Greatest Dilemma. The MIT Press.
  44. Manyika, J., Lund, S., Chui, M. and Bughin, J., 2022. The net-zero transition: What it would cost, what it could bring. McKinsey Global Institute.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence Sustainability Project Management

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