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<p>This study explores the use of machine learning models to improve strategic decision-making in IT governance, focusing on incident classification and prioritization. As IT systems grow more complex and data volumes increase, traditional methods often prove ineffective. Four ML models including Logistic Regression (LR), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and AdaBoost were evaluated with IT service management data. Results showed that SVC achieved high accuracy, making it suitable for IT governance, while Logistic Regression was effective for simpler issues. KNN and AdaBoost require further optimization for better performance.</p>

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Leveraging machine learning for strategic decision-making in IT governance

  • 期刊论文

  • 发表日期：

    2026年4月21日

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  • Shima Nargesi,
  • Mohammad Reza Keyvanpour

摘要

This study explores the use of machine learning models to improve strategic decision-making in IT governance, focusing on incident classification and prioritization. As IT systems grow more complex and data volumes increase, traditional methods often prove ineffective. Four ML models including Logistic Regression (LR), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and AdaBoost were evaluated with IT service management data. Results showed that SVC achieved high accuracy, making it suitable for IT governance, while Logistic Regression was effective for simpler issues. KNN and AdaBoost require further optimization for better performance.