Reimagining Deployment and Release Strategies for DCIM Platforms
DOI:
https://doi.org/10.5281/zenodo.22830410Keywords:
AI-Driven Release Management, Smart Deployment Systems, AIOps Frameworks, DCIM Architecture, Predictive Risk Management, Release Automation, Deployment Optimization, Change Failure Reduction, Incident Recovery Optimization, Intelligent IT Operations, ML-Based Deployment Analytics, NLP in IT Operations, Readiness Management Systems, DevOps Automation, Infrastructure Lifecycle Management, Proactive Deployment Monitoring, IT Service Reliability, Automated Change Management, Data-Driven IT Operations, Continuous Deployment Analytics.Abstract
Effective deployment and release management is critical to the reliable delivery of IT infrastructure services that underpin business operations. Traditional approaches, however, remain largely manual or semi-automated and reactive by design, resulting in persistent challenges: delayed release schedules, elevated change failure rates, unplanned production disruptions, prolonged service restoration times, and unexpected outages. Artificial Intelligence (AI) offers a data-driven alternative, using machine learning, deep learning, natural language processing, and intelligent automation to address these limitations. This paper proposes an integrated AI-based Data Center Infrastructure Management (DCIM) framework that embeds AIOps capabilities throughout the deployment and release management lifecycle, augmenting existing tools, platforms, and services rather than replacing them. Central to this framework are AI-driven predictive risk management and readiness management capabilities, supported by structured adoption and outreach strategies to ensure organizational uptake.
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