AI-Driven Automation in Power Utility Management A Scalable Approach Using SPSS

Making solution decisions in the power industry is a complex and multifaceted task that requires careful management of scalability, service reliability, and security vulnerabilities. Traditional manual decision-making approaches are inefficient, time-consuming, and prone to errors, thereby increasing costs and security risks. To address these challenges, this research paper introduces an automated framework based on machine learning that enhances decision analysis for electrical applications. Through rigorous testing with real operational datasets, the framework demonstrates its effectiveness in improving decision-making processes, improving security, and reducing operational overhead. Research Significance: This research is significant as it addresses critical inefficiencies in power utility decision-making by leveraging machine learning automation. The proposed framework improves scalability, mitigates security risks, and reduces human intervention, ultimately leading to cost-effective and efficient power management. This research offers factual understanding of the US OP AI-driven solutions in infrastructure management, providing a replicable model for other industries facing similar challenges. The results demonstrate the potential for widespread adoption in improving operational resilience and decision-making accuracy. SPSS Statistics is a powerful software tool used to analyze data in many fields such as social sciences, healthcare, marketing, and education. It provides a comprehensive set of statistical tools for organizing, evaluating, and interpreting data. SPSS allows users to perform a wide range of analyses such as descriptive statistics, regression, ANOVA, factor analysis, and hypothesis testing. Its sophisticated data manipulation features and user-friendly interface make it popular among researchers and analysts. SPSS also supports the creation of charts and reports, helping to provide data-driven insights. Input Parameters taken as industry, Automation Tool Used, Threat Type Addressed, Incident Response Framework, Remediation Strategy, Organization Size. Evaluation Parameters taken as Efficiency of Automation, Reduction in Response Time, Accuracy of Threat Detection, Compliance Adherence, Cost-effectiveness. The reliability statistics show a 0.533 Cronbach's Alpha and 0.535 based on standardized items, with a total of 5 items analyzed.