AI-Enabled Decision Intelligence in Project Management: A Systematic Review of Efficiency and Productivity Gains
DOI:
https://doi.org/10.15662/IJEETR.2024.0602017Keywords:
Artificial Intelligence, Project Management, Decision intelligence, systematic review, Industry 4.0, Predictive analyticsAbstract
Background: The traditional project management approaches adopted for stabilising a project in the absence of uncertainty are becoming less and less effective in dealing with the rapidly changing information needs and technology in a rapidly evolving world of uncertainties. With the transformation of Industry 4.0 and the operating environment after a pandemic, the challenges of conventional planning models became more evident when a pandemic shook the planning landscape:
Objective: This systematic review mainly evaluates the limitations of the traditional management approaches and examines how AI (artificial intelligence) mainly improves project efficiency, decision intelligence, and adaptive responsiveness in the project environment.
Methodology: Search procedure was systematic, using the PRISMA 2020, conducted in the Web of Science, Science Direct, IEEE Xplore and Scopus databases, covering the period 2011 to 2023. Based on these, an article for final inclusion in the synthesis was obtained from 59 articles, after removing articles that were duplicate articles and applying the eligibility criteria. The Mixed Methods Appraisal Tool (Version 2018) was used to evaluate the methodological quality of the articles. Synthesising evidence was done using thematic analysis with frequency analysis.
Results: The use of AI technologies may be making an impact on the outcomes and quality of a project. Forecasting studies' accuracy proved to be lowered by the estimation/prediction accuracy error of 23% and 41%, while in the context outside of the Digital Twin. Moreover, digital twin applications ensured better optimisation of resources in real time. Using generative AI tools reduced project planning tasks by nearly half, at approximately 3.2 times the process was faster compared to traditional planning. Moreover, progress has been achieved, but challenges to implementation are large, particularly in the capability domain, resistance to change in people and processes, and a lack of strategic fit.
Conclusion: AI is reshaping project management with its introduction of decision intelligence and enabling teams to adapt on the fly. However, consistent enhancement of the project management experience remains a function of structured integration plans, buy-in leadership, ongoing capacity development, and ethical stewardship. In this review, the researcher includes an evidence synthesis framework and indicates some of the key research gaps, particularly relating to how SMEs actually apply AI and to measuring their longitudinal performance.
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