Leveraging Artificial Intelligence for Intelligent Risk Management in Small-Scale IT Projects
Keywords:
Artificial Intelligence, Machine Learning, Project Risk Management, Small-Scale IT Projects, Predictive Analytics, Explainable AI, Agile Project Management, Design Science Research.Abstract
Small-scale information technology (IT) projects are important in ensuring agility to the organization but portend very high mortality rates. Conventional, manual risk management models are usually highly simplistic and reactive in nature and they cannot fit the Agile/DevOps world of small and medium enterprises (SMEs) due to the lack of sufficient data, which is the central focus of this paper.1 Although Artificial Intelligence (AI) has the needed paradigm shift in terms of active and predictive foresight 1, the most current solutions are enterprise-based with an artificial technological and financial barrier.2 We describe how we designed an open- source application based on the framework and empirically validated the application. The PoC, which was created on the zero-cost stack of Streamlit and Scikit-learn, showed a high predictive capability using an interim dataset of N=51 real- world small IT projects. The comparative analysis revealed that the Random Forest model had 60.00% predictive accuracy and the count of Stakeholders is the most conclusive risk factor (Importance = 0.7). This study confirms the possibility of designing a lightweight, available and data-driven risk intelligence tool, giving an empirically supported blueprint of how to improve the resilience of projects within the context of SMEs.
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