Operations | Monitoring | ITSM | DevOps | Cloud

AI models can reason over language, summarize findings, and explain patterns. What they cannot do on their own is see the real-time operational state of your environment. Ask a model about a critical incident and it will answer from whatever context it is given, which means the answer is only as trustworthy as the input. In operations and compliance workflows, an answer is only useful if it is grounded in current service context and governed access to the systems that define reality.

A majority of office professionals (72%) believe they understand how to use AI for their job better than the team responsible for managing AI at their company. While it’s encouraging to see employees embrace AI with such confidence, organizations will want to ensure they are providing the tools, guidance, and safeguards needed to help employees use AI safely.

Legacy infrastructure was not designed for the requirements of the AI era. While large-scale model training remains centralized in data centers, test-time inference is rapidly shifting to the edge to reduce latency and bandwidth consumption. This shift creates a new frontier for enterprise AI, but deploying at the edge introduces significant manual complexity, interoperability issues, and security vulnerabilities.

Infrastructure and Operations (I&O) teams have long operated under a familiar paradox: the faster the business scales, the more pressure I&O absorbs. Every new application deployment, every endpoint added, and every cloud workload spun up generates more complexity, more risk and more tickets. The traditional responses to this pressure — more headcount, more tooling, more scripts, more APIs — have delivered incremental relief at best.