What question did this study set out to answer?

This research aims to develop a System Dynamics model to understand how AI impacts project lifecycle dynamics.

March 19, 2026Applied Sciences0 citationsOpen Access

The Impact of AI Integration on Project Lifecycle Dynamics

AFAdi FuxAfeka College of Engineering SRShai RozenesHolon Institute of Technology YCYuval CohenAfeka College of Engineering

Key Points

This research aims to develop a System Dynamics model to understand how AI impacts project lifecycle dynamics.
Developed and validated a System Dynamics model using PMBOK performance domains.
Operationalized project parameters as interconnected stocks, flows, and feedback loops.
Validated the baseline model structurally and behaviorally against established frameworks and trajectories.
Simulated AI-augmented model to assess its marginal effects on project performance.
AI integration reduced peak uncertainty exposure by up to 33%.
Planning effort decreased by 15% with AI-augmented systems.
Monitoring and risk sensing improved, reducing delays by 25%.
Measurement accuracy trajectories and cumulative delivery accelerated, lowering work completion volatility.

Abstract

The purpose of this study is to develop and validate a System Dynamics (SD) model that illustrates how Artificial Intelligence (AI), including generative AI, alters project lifecycle behavior under a hybrid agile–predictive governance approach. The study method uses SD model to operationalize the PMBOK performance domains as an interconnected system of stocks, flows, and feedback loops. These constructs and their interaction represent delivery progress, stakeholder engagement, team capacity, measurement accuracy, governance alignment, and uncertainty exposure. Planning effectiveness is treated as an emergent performance indicator arising from the interaction of the planning-related feedback structures. The proposed model embeds AI levers for planning, risk, measurement, stakeholder sensing, and team support. A calibrated baseline model representing conventional project dynamics was validated in two ways. First it was validated structurally against PMBOK guidance and the SD literature. Secondly, it was validated behaviorally against stylized project trajectories. The AI-augmented variant was then simulated under identical initial conditions to assess marginal effects. Across multiple scenarios, AI integration reduced peak uncertainty exposure by up to 33%. Also, the AI-augmented system showed reduced planning effort by 15%, and improved monitoring and risk sensing by accelerating feedback and reducing delays by 25%. AI also improved measurement accuracy trajectories and accelerated cumulative delivery while lowering volatility in work completion rates. Governance coherence and development approach alignment improved, while stakeholder engagement and team capacity showed smaller changes. The results demonstrate that AI primarily acts as an enabler that strengthens high-impact feedback loops in planning, monitoring, and risk sensing within a hybrid methodology. AI also delineates boundaries where managerial judgment and cultural change remain critical for effective framework validation.

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