Operations | Monitoring | ITSM | DevOps | Cloud

Data Center Infrastructure Management (DCIM) software is evolving in three overlapping stages: Now (a unified ingestion and observation layer across power, cooling, and IT systems), New (expanded control functions, including bandwidth management), and Next (generative and agentic AI built on top of that monitoring foundation). Understanding which stage a platform actually operates in is the single most useful filter for evaluating DCIM vendors in 2026 and beyond.

Manual asset entry is the hidden drain on your data center’s productivity. That one missed update causes hours of chasing spreadsheets, hunting down equipment details, and doubting if your inventory matches reality. Hyperview’s agentless asset auto-discovery flips the script, delivering real-time asset data across IT, OT, and virtual environments without the manual hassle. Keep your records current effortlessly and focus on running your data center with confidence.

Agentic AI means a system that acts on behalf of a specific person, within that person’s exact role and access permissions — not a general term for “smart” software. In data center infrastructure, agentic AI only becomes safe to deploy once three things already exist: a complete monitoring pipeline, an analytics pipeline, and a control pipeline governed by strict role-based access control.

NetBox is an open-source platform used for network infrastructure management and documentation, helping organizations track networks, devices, IP addresses, circuits, and racks. Sunbird’s new NetBox connector programmatically pulls device, port, and cabling data from NetBox into dcTrack using a preconfigured base XML connector, driving automation and giving you a single pane of glass and single source of truth across your entire infrastructure.

“AI-powered” has become a marketing label applied to almost any data center software feature. A more useful and precise term is actionable intelligence — a four-level maturity model (descriptive, diagnostic, predictive/prescriptive, and cognitive) that shows whether a platform’s AI claims are backed by real data infrastructure or just a chatbot layered on top of an incomplete system.

Across the United States, data center projects are being stalled, local governments are putting ordinances in place to limit data center buildouts, all because people hate data centers. Due to community pushback, in Q1 2026 alone, over $130 billion’s worth of AI data center projects has been blocked or delayed.

What’s in this article? Modern enterprise workloads are demanding more from IT infrastructure than ever before. AI platforms, advanced analytics, virtualisation clusters and high-performance computing (HPC) environments all require significantly more compute power than traditional business applications. Many organisations are discovering that their existing on-premise facilities were never designed for these demands.

The last few years have seen AI conversations dominated by the need for investment in hyperscale infrastructure as firms race to build ever larger training models. But as those conversations evolve, the emphasis is shifting to the next phase of AI adoption, focusing on the scaling of use cases and real-world value. In line with this shift, organisations are looking beyond where AI is trained to the specifics of where it is actually used.