Data-Driven Performance Management for AI-Enabled Enterprise Infrastructure: An Integrated Engineering Approach

  • Authors

    • Robert Wilson Independent Researcher, USA. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-V9I2P102

    Published 04-08-2026

  • Data-Driven Performance Management, Artificial Intelligence, Enterprise Infrastructure, AIOps, Predictive Analytics, Infrastructure Observability, Closed-Loop Optimization, AI Governance

    Issue

    Vol. 9 No. 2 (2026)

    Section

    Articles

  • Abstract

    This study proposes a data-driven performance management framework that integrates infrastructure monitoring, enterprise data, AI analytics, performance prediction, causal diagnosis, and feedback-based optimization. The framework consists of five layers: data acquisition and integration, data quality and contextualization, AI-based performance analytics, decision optimization, and continuous monitoring and governance. Based on existing literature, it aims to improve service reliability, operational efficiency, resource utilization, cost management, and business value. The study also identifies challenges such as data quality, model drift, security, explain ability, and organizational alignment. As a conceptual study without experimental validation, it provides a foundation for future research and evidence-based enterprise performance management.

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