AI in Project Management
Joshua Cambridge; Sky Shen; Yuzhe Wang
RESEARCH BACKGROUND AND
IMPORTANCE
KEY FINDINGS AND APPLICATIONS OF AI
IN PROJECT MANAGEMENT
Challenges of Project Management: Currently, only 35% of
projects in the world are considered successful. One of the
main reasons is the low maturity of project management
technology.
The Role of AI: Artificial Intelligence (AI) is gradually being
applied to the field of project management to help improve
efficiency, optimize risk management, and improve decisionmaking. PMPoised for revolution by 2030.
Research Methodology: This study uses the systematic
literature review (SLR) method to analyze 215 relevant papers
and summarize the main application areas and trends of AI in
project management
Risk Management:
AI helps identify, evaluate, and respond to project risks through predictive
analysis, decision support system (DSS) and other methods.
Main Tools:
Fuzzy Logic, Bayesian Networks, Machine Learning.
Project Planning & Scheduling:
AI can be used to optimize resource allocation and improve the accuracy of
schedule forecasting.
Main technologies:
Monte Carlo Simulation, Optimization.
Decision Support:
AI can assist project managers in making data-driven decisions and improve
project success rates.
Main applications:
Expert Systems, Multi-criteria Decision Analysis (MCDA).
Cost & Budget Control:
AI helps predict the risk of cost overruns and optimize budget allocation.
Main tools: Neural Networks, Regression Analysis.
Research Focus by Sector
This chart shows how AI in project management is studied across
different fields. Most research (50.3%) focuses on the
construction sector, followed by computer science (13.4%) and
other fields such as business, decision sciences, and energy.
The strong presence of construction reflects the complexity and
risk involved in large projects, making it a key area for AI
development.
AI–PM Process Integration Insight
The heatmap shows that AI is most widely applied in the process of risk analysis.
In particular, “Prediction” techniques appear in 18 studies, and “Advising” in 14.
This reflects the strong connection between AI and risk-based decision-making in
project management. AI tools help managers assess risk more accurately and
suggest better response strategies.
CHALLENGES OF AI IN PROJECT
MANAGEMENT
Data Quality & Availability:
AI relies on high-quality data, but project management data is often incomplete
or non-standardized.
Technology Maturity:
The application of AI in project management is still in its early stages, and
many companies have not yet fully accepted or understood its potential.
Ethical and Legal Issues:
AI may bring data privacy issues and affect decision transparency.
AI Categories in Project Management
The chart shows how often different AI types are used in project
management research. “Advising” and “Prediction” are the most
FUTURE DEVELOPMENT TRENDS
common, making up over 70% of all uses. These methods help
managers make better decisions and plan projects more accurately. AI-driven intelligent project management platform:
PM software integrated with AI will be more intelligent and provide automated
Other AI types like “Classification” and “Guiding” are used less
decision support.
often.
AI combined with blockchain:
Enhance the transparency and security of project data.
Contributions
AI-assisted dynamic risk management:
Joshua Cambridge:
Use real-time data to predict potential risks and automatically adjust project
Organised and coordinated all group meetings
strategies.
Acted as meeting note-taker and kept records of key decisions
Sky Shen:
Conducted literature review and summarised relevant info
SOURCE
Drafted the initial version of the poster content and refined it
Nenni, M. E., Felice, F. D., Luca, C. D., & Forcina, A. (2024). How Artificial
Yuzhe Wang:
Intelligence Will Transform Project Management in the Age of digitization: a
Designed the layout and structure of the poster for better visual
Systematic Literature Review. Management Review Quarterly, 1(1).
Assisted in integrating visuals with written content
https://doi.org/10.1007/s11301-024-00418-z