Strategizing AI in infrastructure management: From insight to implementation

As infrastructure environments have become more distributed, dense, and dynamic, the challenge of managing them effectively has also grown, stretching the limits of traditional data center infrastructure management (DCIM) solutions. Ironically, the technologies contributing to these challenges, particularly AI, are also key to the solution.

As demonstrated by RiT Tech’s Universal Intelligent Infrastructure Management (UIIM) methodology and its supporting platform, XpedITe, AI can transform infrastructure oversight from reactive maintenance to predictive planning toward autonomous operation, driving improved uptime, efficiency, and long-term sustainability.

Establishing a foundation through UIIM

Delivering these outcomes requires a management framework designed to apply AI at scale - the exact foundation that UIIM provides. In contrast to traditional DCIM platforms, which often manage these components in isolation, UIIM supports cross-system orchestration and maintains a consistent operational model that reflects real-time conditions across the entire infrastructure.

Applying this model, the XpedITe platform consolidates facility controls, environmental telemetry, compute infrastructure, and virtualized IT environments into one connected ecosystem. This helps support better synchronized workflows, unified oversight, and improved alignment between physical infrastructure and digital operations, while establishing the process standardization necessary to reduce operational variation and error.

This level of coordination ultimately creates the consistency required for automation and AI to function reliably at scale. When systems communicate in real time and follow unified processes, it becomes possible to generate more accurate insights, automate routine tasks, and base operational decisions on current, validated information. By establishing this foundation, UIIM enables organizations to apply AI with reduced operational risk, greater predictability, and improved responsiveness to dynamic conditions, strengthening overall performance and supporting long-term infrastructure resilience.

Coordinating automation, oversight, and performance improvement

One of the clearest demonstrations of this is when AI is applied to provisioning, where manual planning and limited visibility have traditionally led to inefficiencies, stranded capacity, and delays. XpedITe addresses these concerns through its namesake module, which uses real-time telemetry, historical usage data, and predictive analytics to assess capacity requirements, recommend optimal configurations, and automate essential steps such as work order generation and resource allocation. The results of this approach speak for themselves, having been linked to a reduction in provisioning time by up to 95 percent.

As these new efficiencies take hold, the role of human operators will also need to change more toward complementing and guiding automated systems rather than managing tasks directly. Rather than overseeing every process, operators are positioned to focus on oversight, exception handling, and planning activities that require judgment and situational awareness. This shift, integral to the UIIM methodology, will be essential to creating a coordinated model where AI supports consistency and scale, all while human input ensures adaptability and alignment with broader objectives.

Sustaining this model requires continuous refinement on the part of both AI and human operators. Through its Workflow, Automation, and Analytics capabilities, XpedITe enables organizations to track defined performance metrics- such as availability, efficiency, risk exposure, and responsiveness- and refine operations accordingly, including Install-Move-Add-Change (IMAC) processes that typically require manual scheduling. This creates a feedback loop in which automated systems and human teams can learn from outcomes, adapt to change, and improve overall performance. In this context, AI serves as an integrated component of a responsive and scalable infrastructure strategy.

Applying AI for sustainability, visibility, and scalable operations

This same approach extends to sustainability, where AI contributes directly to operational planning and environmental performance. By continuously analyzing key operational data from the data center infrastructure, the XpedITe Sustainability module calculates and tracks critical KPIs.

This helps identify inefficiencies in power, cooling, and resource utilization, and compares performance against historical data and industry benchmarks, enabling adjustments that support performance and availability targets in alignment with regulatory requirements and internal sustainability objectives. When used in combination with XpedITe’s AI Analytics module, which allows operators to query infrastructure data using natural language, the operator can attain more precise insight into power consumption patterns, equipment efficiency, and maintenance timelines, enhancing visibility across distributed environments and supporting more accurate and informed responses.

These capabilities are most effective when delivered through a tightly integrated operational architecture, with organizations of all sizes set to face increased complexity in maintaining consistent oversight and control. XpedITe addresses this concern through a layered, interoperable framework that combines telemetry from physical assets, environmental systems, and IT infrastructure into a unified model.

This enables real-time correlation of events across facilities and centralized management of distributed resources. Ultimately, by bringing digital twin, sustainability, analytics, provisioning, and automation under one platform, XpedITe helps organizations manage geographically dispersed operations with greater accuracy, reduce manual intervention, and improve compliance with both internal policies and industry regulations. All of this results in minimized costly downtime, which has emerged as an increasingly significant concern, with the Uptime Institute reporting that 60 percent of data center failures in 2022 resulted in losses of at least $100,000, up from 39 percent in 2019.

Building consistent and responsive infrastructure

Collectively, these capabilities position UIIM not simply as an operational upgrade but as a strategic enabler for ongoing transformation. As data centers contend with rising energy costs, growth of scale and complexity, expanding compliance mandates, and demand for transparency, the ability to automate at scale while maintaining control becomes essential.

With its modular structure and AI-powered functionality, the XpedITe platform allows organizations to address immediate operational challenges while laying the groundwork for more adaptive, effectively operated infrastructure. In this context, AI is not an endpoint but a mechanism for continuous alignment, supporting infrastructure that can respond intelligently to change, improve with use, and meet technical and business objectives in a constantly evolving environment.

Discover what UIIM can do for you

As infrastructure environments become increasingly complex, the ability to integrate systems, apply AI effectively, and maintain consistent performance will prove foundational. For a consultation on how RiT Tech’s UIIM methodology and XpedITe platform can support your operational goals, contact RiT Tech today via our dedicated contact form or email us at [email protected].