In a recent publication in the journal Applied Sciences, researchers explored how artificial intelligence (AI) will influence project management, particularly in cost, risk, and scheduling, by engaging with experts in the field.
Study: Artificial Intelligence and Project Management: An Outlook. Image credit: Miha Creative/Shutterstock
Background
AI encompasses machine or computer processes that emulate human cognition. AI is poised to substantially impact society, transforming various aspects of business and daily life. However, the specific implications for management remain uncertain due to the intricate, rapidly evolving nature of AI and management practices. Certain management tasks could undergo automation, while AI will enhance others.
Project management, characterized by the diversity of managed projects, may be less suitable for full automation. Nevertheless, AI introduces new application possibilities in this field. Future managers will need to adapt to working alongside machines.
Impact of AI on key project management areas
Fridgeirsson et al. conducted a thorough investigation that explored how AI influences the 10 knowledge domains in project management, as specified by the Project Management Institute (PMI). Their research framework was firmly grounded in the ten distinct knowledge areas delineated in the Project Management Body of Knowledge (PMBOK). The PMBOK's structured guidelines were chosen for their suitability as a research platform. It aimed to explore which aspects of project management, as per the PMBOK, will be influenced by AI in the next decade. The study found AI's potential impact on scheduling, cost management, and risk management. Therefore, these areas are the focus of the current study.
Project schedule management involves six processes, ensuring projects meet deadlines: plan schedule management, define activities, sequence activities, estimate activity durations, develop schedules, and control schedules. Project cost management focuses on planning, estimating, managing, and controlling project costs. It comprises four processes: plan cost management, determine budgets, estimate costs, and control costs. Project risk management aims to optimize project success by managing risks. Plan Risk Management, Identify Risks, Conduct Qualitative and Quantitative Risk Analysis, Plan Risk Responses, Execute Risk Responses, and Monitor Risk are its seven processes.
The utilization of AI extends to domains such as human resources, marketing, customer relationship management, and product innovation. It is important to note that AI's role is not centered on the complete automation of managerial positions; rather, it serves as a valuable augmentation. The synergy between AI and managers can yield superior decision-making capabilities and heightened efficiency in particular functions. Within the realm of project management, AI has the potential to elevate aspects such as scheduling, planning, and risk assessment.
AI tools are useful for project planning because they are flexible and can manage uncertainty in project management. Furthermore, AI can assist in forecasting project duration and predicting cash flow trends, improving project cost management.
Unpacking AI's prognosis in project management knowledge areas
Researchers employed a qualitative cross-sectional approach based on interviews, with a focus on predicting future outcomes. It involves a purposive sample of experts in project management and AI, selected for their knowledge and profession. The interview questionnaire comprises 30 statements related to project cost management, project schedule management, and project risk management, breaking down these areas into technical and social elements. Responses are rated on a Likert scale from "very much" (5) to "very little" (1). Open-ended questions allow additional comments. This research builds on previous work by Fridgeirsson et al., delving deeper into the impact of AI on specific knowledge areas.
Results reveal that most respondents anticipate a substantial impact of AI on project schedule management. Notably, 83 percent of experts believe that AI will significantly affect the schedule baseline and the environmental factors of the enterprise while planning schedules. However, 33 percent perceive AI's impact on the project charter and work performance data as insignificant. Moreover, 75 percent of experts expect AI to significantly impact the scope baseline and schedule management plan while defining, sequencing, and estimating activities.
In project cost management, 91 percent of respondents anticipate a significant effect on cost management estimates and the estimation of resource costs based on resource cost rates, market conditions, inflation, and exchange rates. However, AI's influence on cost negotiation and contracting is perceived as low, with 75 percent of experts considering it insignificant.
In project risk management, the probability and impact matrix are expected to be significantly impacted by AI, with 91 percent of experts concurring. Furthermore, 83 percent believe that AI will significantly affect the advancement of the risk management plan and the establishment of risk thresholds for a project. However, 34 percent view AI's impact on determining roles and responsibilities for managing risk as insignificant.
Overall, experts concur that AI will significantly impact project schedule management, with 61 percent anticipating AI's influence. Similarly, for project cost management, 52 percent believe AI will have a moderate or significant impact. In the case of project risk management, 56 percent of experts anticipate a significant AI impact. It is significant to note that experts all concur that although AI can support project management duties, a human project manager will still be needed.
Conclusion
In summary, the current study highlights the potential of AI to significantly impact project management, especially in tasks that involve historical data and repeatable processes. Further research is suggested to delve into AI's specific applications in project management elements, explore cognitive biases, and assess AI knowledge within the profession to achieve a more comprehensive understanding of AI's future effects on project management.
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