AI-Driven tools and their influence on project decision-making in U.S. technology enterprises

https://doi.org/10.35912/gams.v2i1.3599
Published: Oct 29, 2025

Abstract:

Purpose: This study investigates the impact of AI-driven tools on project decision-making in U.S. information technology (IT) companies, focusing on the roles of predictive analytics, natural language processing (NLP) assistants, and AI dashboards in improving decision quality, project outcomes, and stakeholder collaboration.

Research Methodology: A mixed-methods design combining a systematic literature review and an empirical survey was used. Data were collected from project managers at major U.S. IT firms, including Microsoft, Oracle, and Google, who utilize AI-enabled platforms such as Microsoft Project, Jira, and IBM Watson. Quantitative data were analyzed through regression modeling and descriptive statistics, while qualitative insights were examined using thematic analysis.

Results: The results show that AI technologies significantly improve the accuracy of project decision-making, minimize budget deviations, and strengthen cross-team communication. Predictive analytics enhanced early risk identification, NLP assistants streamlined scheduling and reporting, and AI dashboards increased real-time visibility and stakeholder engagement. Companies demonstrating higher AI maturity achieved superior performance across key project indicators.

Conclusions: Integrating AI into project management enhances decision-making by combining automation with data-driven intelligence. Strategic AI adoption improves efficiency, reduces scope creep, and boosts managerial satisfaction within U.S. IT contexts.

Limitations: The study focuses exclusively on large U.S.-based IT firms, limiting its applicability to smaller or global enterprises. The rapid evolution of AI restricts long-term generalization.

Contribution: This research enriches project management and information systems literature by contextualizing AI’s role in high-tech decision-making and offering practical guidance for managers, executives, and policymakers driving digital transformation.

Keywords:
1. Artificial Intelligence
2. Decision-Making
3. Project Management
4. Predictive Analytics
5. U.S. IT Companies
Authors:
1 . Umme Zinia Zahan Nodia
Trine University, Angola, Indiana, USA
2 . Md Minar Hossain
Trine University, Angola, Indiana, USA
3 . Falguni Rahman
University of Bremen, Germany
4 . Md Mehedi Hasan Antor
National University, Bangladesh
5 . Mohammad Tahmid Ahmed
Trine University, Angola, Indiana, USA
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How to Cite
Nodia, U. Z. Z. ., Hossain, M. M. ., Rahman, F. ., Antor, M. M. H. ., & Ahmed, M. T. . (2025). AI-Driven tools and their influence on project decision-making in U.S. technology enterprises. Global Academy of Multidisciplinary Studies, 2(1), 61–80. https://doi.org/10.35912/gams.v2i1.3599
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Issue & Section
Vol. 2 No. 1 (2025): August
Articles
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  1. Adebayo, Y., Udoh, P., Kamudyariwa, X. B., & Osobajo, O. A. (2025). Artificial intelligence in construction project management: A structured literature review of its evolution in application and future trends. Digital, 5(3), 26. doi:https://doi.org/10.3390/digital5030026
  2. Ajiga, D., Okeleke, P. A., Folorunsho, S. O., & Ezeigweneme, C. (2024). Enhancing software development practices with AI insights in high-tech companies. IEEE Software Engineering Institute, Technical Report TR-2024-003. doi:https://doi.org/10.51594/csitrj.v5i8.1450
  3. Al Naqbi, H., Bahroun, Z., & Ahmed, V. (2024). Enhancing work productivity through generative artificial intelligence: A comprehensive literature review. Sustainability, 16(3), 1166. doi:https://doi.org/10.3390/su16031166
  4. Ali, M. M. (2019). Impact of management information systems (MIS) on decision making. Global Disclosure of Economics and Business, 8(2), 83-90. doi:https://doi.org/10.18034/gdeb.v8i2.100
  5. Aliu, J., Oke, A. E., Kineber, A. F., Ebekozien, A., Aigbavboa, C. O., Alaboud, N. S., & Daoud, A. O. (2023). Towards a new paradigm of project management: a bibliometric review. Sustainability, 15(13), 9967. doi:https://doi.org/10.3390/su15139967
  6. Almalki, S. S. (2025). AI-Driven Decision Support Systems in Agile Software Project Management: Enhancing Risk Mitigation and Resource Allocation. Systems, 13(3), 208. doi:https://doi.org/10.3390/systems13030208
  7. Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International journal of information management, 57, 102225. doi:https://doi.org/10.1016/j.ijinfomgt.2020.102225
  8. Bouschery, S. G., Blazevic, V., & Piller, F. T. (2023). Augmenting human innovation teams with artificial intelligence: Exploring transformer?based language models. Journal of product innovation management, 40(2), 139-153. doi:https://doi.org/10.1111/jpim.12656
  9. Cahyana, L., Pratama, A. P., & Welly, J. (2025). Proposed improvements on performance management system at PT JIEP by using balanced scorecard. Journal of Digital Business and Marketing, 1(1), 15-31. doi:https://doi.org/10.35912/jdbm.v1i1.3315
  10. Chen, L., Nath, R., & Rocco, N. (2024). Key Issues of Predictive Analytics Implementation: A Sociotechnical Perspective. Journal of International Technology and Information Management, 32(1), 239-270. doi:https://doi.org/10.58729/1941-6679.1565
  11. Cooper, R. G., & Brem, A. M. (2024). The adoption of AI in new product development: Results of a multi-firm study in the US and Europe. Research-Technology Management, 67(3), 44-53. doi:https://doi.org/10.1080/08956308.2024.2324241
  12. Dennehy, D., Griva, A., Pouloudi, N., Dwivedi, Y. K., Mäntymäki, M., & Pappas, I. O. (2023). Artificial intelligence (AI) and information systems: perspectives to responsible AI. Information Systems Frontiers, 25(1), 1-7. doi:https://doi.org/10.1007/s10796-022-10365-3
  13. El Khatib, M., & Al Falasi, A. (2021). Effects of artificial intelligence on decision making in project management. American journal of industrial and business management, 11(3), 251-260. doi:https://doi.org/10.4236/ajibm.2021.113016
  14. Endriyono, E., Gunarto, T., & Murwiati, A. (2025). Measuring the achievements of smart economics in the smart village program in Lampung Province 2020-2024. Journal of Multidisciplinary Academic and Practice Studies, 3(3), 133-150. doi:https://doi.org/10.35912/jomaps.v3i3.3524
  15. Ernst, E., Berg, J., & Moore, P. V. (2024). Artificial intelligence and the future of work: humans in control (Vol. 7, pp. 1378893): Frontiers Media SA.
  16. Funda, V., & Francke, E. (2024). Artificial intelligence-powered decision support system for operational decision-making in the ICT department of a selected African university. African Journal of Science, Technology, Innovation and Development, 16(5), 689-701. doi:https://doi.org/10.1080/20421338.2024.2376916
  17. Georgiev, S., Polychronakis, Y., Sapountzis, S., & Polychronakis, N. (2024). The role of artificial intelligence in project management: a supply chain perspective. Paper presented at the Supply Chain Forum: An International Journal.
  18. Giachino, C., Cepel, M., Truant, E., & Bargoni, A. (2024). Artificial intelligence-driven decision making and firm performance: a quantitative approach. Management Decision. doi:https://doi.org/10.1108/MD-10-2023-1966
  19. Gómez-García, S., Zamora, R., & Berrocal, S. (2023). New frontiers for political communication in times of spectacularization. Media and Communication, 11(2), 109-112. doi:https://doi.org/10.17645/mac.v11i2.7069
  20. Gumay, N. F., & Syarif, A. (2025). Financial Management and Marketing Technology as Drivers of MSME Performance. Studi Ekonomi dan Kebijakan Publik, 4(1), 57-70. doi:https://doi.org/10.35912/sekp.v4i1.5515
  21. Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2022). Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research, 308(1), 215-274. doi:https://doi.org/10.1007/s10479-020-03856-6
  22. Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda?. Technological Forecasting and Social Change, 162, 120392. doi:https://doi.org/10.1016/j.techfore.2020.120392
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  24. Herath Pathirannehelage, S., Shrestha, Y. R., & von Krogh, G. (2025). Design principles for artificial intelligence-augmented decision making: An action design research study. European Journal of Information Systems, 34(2), 207-229. doi:https://doi.org/10.1080/0960085X.2024.2330402
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  30. Ludlow, K., Westbrook, J., Jorgensen, M., Lind, K. E., Baysari, M. T., Gray, L. C., . . . Georgiou, A. (2021). Co-designing a dashboard of predictive analytics and decision support to drive care quality and client outcomes in aged care: a mixed-method study protocol. BMJ open, 11(8), e048657. doi:https://doi.org/10.1136/bmjopen-2021-048657
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  36. Neri, G., Marshall, S., Chan, H. K.-H., Yaghi, A., Tabor, D., Sinha, R., & Mazumdar, S. (2025). Data Visualisation in AI-assisted Decision-making: A Systematic Review. Frontiers in Communication, 10, 1605655. doi:https://doi.org/10.3389/fcomm.2025.1605655
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  39. Onwujekwe, G., & Weistroffer, H. R. (2025). Intelligent decision support systems: An analysis of the literature and a framework for development. Information Systems Frontiers, 1-32. doi:https://doi.org/10.1007/s10796-024-10571-1
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