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AI-Enabled Technology Project Governance Framework for Early Risk Detection, Performance Forecasting and Adaptive Intervention


Authors : Sehrish Khalil; Md Rafat Hossain; Tamanna Sharmin Mumu; Samina Ahmed

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/55fsh9hu

DOI : https://doi.org/10.38124/ijisrt/26sep462

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Abstract : This work addresses a common problem: technology projects often run late, go over budget, or fail because risks are found too late. Before this framework, managers relied on manual reports and gut feeling, which meant problems were spotted only after damage was done. We built a system that reads project data (like progress, defects, and team speed) every two weeks, uses machine learning to predict which projects are likely to delay, and explains why using simple scores. The system then automatically triggers actions such as resource reviews or stakeholder alerts when risk crosses a set threshold.

Keywords : Artificial Intelligence, Project Governance, Pisk Prediction, Schedule Forecasting, Explainable AI, SHAP, Machine Learning, Adaptive Intervention.

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This work addresses a common problem: technology projects often run late, go over budget, or fail because risks are found too late. Before this framework, managers relied on manual reports and gut feeling, which meant problems were spotted only after damage was done. We built a system that reads project data (like progress, defects, and team speed) every two weeks, uses machine learning to predict which projects are likely to delay, and explains why using simple scores. The system then automatically triggers actions such as resource reviews or stakeholder alerts when risk crosses a set threshold.

Keywords : Artificial Intelligence, Project Governance, Pisk Prediction, Schedule Forecasting, Explainable AI, SHAP, Machine Learning, Adaptive Intervention.

Paper Submission Last Date
31 - October - 2026

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