The vision is enticing: an IT system that monitors itself, detects, analyzes, and fixes errors \u2013 all without human intervention during ongoing operations. No operations team, no on-call duty, no war rooms. Instead, a symbiotic combination of automation and artificial intelligence (AI) takes over full operational responsibility.<\/p>\n\n\n\n
NoOps \u2013 the idea that IT can function without operations teams \u2013 sounds like a major relief for overloaded infrastructure units. But how realistic is this vision in 2025?Back in 2011, Forrester coined a groundbreaking phrase in its report \u201cAugment DevOps with NoOps\u201d<\/em>: \u201cA DevOps focus on collaboration evolves into a NoOps focus on automation.<\/em>\u201d Since then, IT operations have changed significantly. DevOps \u2013 and later DevSecOps \u2013 teams gradually assumed shared responsibility: \u201cYou build it, you run it, you secure it.\u201d<\/p>\n\n\n\n The next stage of transformation \u2013 AI-powered observability, predictive monitoring, and generative AI for autonomous problem-solving \u2013 is already partially a reality, though widespread adoption in companies and institutions is still lacking.<\/p>\n\n\n\n Despite all progress, one major challenge will persist on the road to NoOps: the complexity of IT infrastructures \u2013 and it’s growing rapidly, not least due to the use of artificial intelligence. These systems have evolved over time, are heterogeneous, distributed, and deeply interwoven with a multitude of technologies, tools, and dependencies. This complexity has long posed significant challenges for performance management.<\/p>\n\n\n\n A look at Atruvia AG \u2013 a financial IT service provider formed in 2015 through the merger of Fiducia IT AG and GAD eG \u2013 illustrates the scale of the challenge: This IT landscape has developed over years, and the numbers make one thing clear: modern enterprise systems can no longer be fully covered by \u201cclassic\u201d monitoring solutions.<\/p>\n\n\n <\/p>\n <\/p>\n To manage performance and resolve incidents in such complex IT landscapes, it’s essential to distinguish between known and unknown problem sources. These can be categorized using the Rumsfeld model:<\/p>\n \u2022 Known Knowns:<\/strong> typical, recurring issues that are well-documented and relatively easy to automate. <\/p>\n <\/p>\n Modern observability systems use these three data types to provide a comprehensive view of system behavior:<\/p>\n \u2022 Metrics<\/strong> answer: “Do we have a problem?” <\/p>\n If these sources are evaluated consistently, completely, and in real time, issues can be reliably detected, accurately classified, and \u2013 in the best case \u2013 automatically resolved. The integration of metrics, logs, and traces today lays the foundation for a new quality of operational IT management.<\/p>\n <\/p>\n \u2022 Mean Time to Recovery (MTTR)<\/strong> reduced from weeks to sometimes under an hour <\/p>\n Observability is thus evolving from a monitoring tool to a strategic enabler of automation, stability, and speed in IT operations.<\/p>\n <\/p>\n With real-time visibility into system behavior, operational processes can be automated and errors detected early. Observability provides the transparency needed to identify and assess dependencies, bottlenecks, and anomalies. Generative AI can suggest manual fixes, while AI-powered systems can handle automated error resolution.<\/p>\n <\/p>\n However, even with this, \u201cunknown unknowns\u201d remain difficult to manage. Predictive analytics can only assist to a limited extent by recognizing patterns, describing trends, and suggesting possible solutions via GenAI \u2013 but fixing the issues still requires human involvement.<\/p>\n <\/p>\n <\/p>\n Technological progress in observability and automation brings us closer to the goal of a largely self-governing IT operation. But the core prerequisites must be met for NoOps to become more than just a buzzword.<\/p>\n <\/p>\n The right mindset is essential: all operational knowledge must be known and accessible to everyone involved. In particular, DevOps and DevSecOps teams must communicate any system changes to other teams. Only when all parties take shared responsibility for the bigger picture and regularly exchange information about evolving system interdependencies can a resilient and automated operation be achieved.<\/p>\n <\/p>\n <\/p>\n AI must be applied purposefully. It only reaches its full potential when it understands context and has access to the right information. Generative AI can help analyze causes, classify anomalies, and offer well-founded solution proposals. Predictive AI enables proactive early warnings, risk assessments, and dynamic optimizations. However, without domain-specific contextual information being fed into the models, AI results remain superficial and thus unusable in operational practice. While AI won\u2019t solve every problem, it plays a critical role in identifying the truly dangerous ones before they become critical.<\/p>\n <\/p>\n <\/p>\n Realistically, NoOps<\/strong> is not a goal that will be fully achievable across the board in the coming years. \u201cUnknown unknowns\u201d will remain \u2013 as will critical situations where human experience is irreplaceable. However, the number of unpleasant surprises can be drastically reduced.<\/p>\n <\/p>\n With the right mindset, excellent data quality, and smart use of AI, a high level of operational stability without human intervention is possible \u2013 at least for large parts of day-to-day operations. The technical feasibility of NoOps is theoretically within reach \u2013 but \u201cNo Responsibility\u201d will likely remain out of reach for a long time.<\/p>\n \u00a0<\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\nComplexity as the main adversary<\/h2>\n\n\n\n
Atruvia operates one of the largest IT infrastructures in the German financial sector, supporting around 155,000 banking workstations. In the background, about 34,000 virtual servers process over 120 billion host transactions per year. 9.3 billion booking entries are reliably handled annually.<\/p>\n\n\n\n
\u2022 Known Unknowns:<\/strong> effects that are generally expected but have not yet been observed, such as the interactions of new components.
\u2022 Unknown Knowns:<\/strong> knowledge that exists within the organization but hasn\u2019t been documented or shared sufficiently, increasing risk in the case of personnel changes or siloed teams.
\u2022 Unknown Unknowns:<\/strong> the most dangerous: completely new, unexpected errors that are unknown and therefore difficult to control. For this critical category, modern observability approaches are indispensable. They go far beyond classic monitoring and allow deeper insights and early detection of anomalies by combining metrics, logs, and traces.<\/p>\nObservability: enabler for automation and NoOps<\/h2>\n
<\/em>\u2022 Traces<\/strong> show: “Where exactly is the issue?”
<\/em>\u2022 Logs<\/strong> explain: “Why did it happen?”<\/em><\/p>\nMeasurable progress includes:<\/h3>\n
\u2022 Root cause detection<\/strong> replaces mere symptom treatment \u2013 real causes become visible
\u2022 Predictive AI<\/strong> identifies foreseeable bottlenecks, such as in database connections
\u2022 Generative AI<\/strong> proposes context-aware solutions quickly and with increasing accuracy<\/p>\nThe prerequisites for NoOps<\/h2>\n
Will NoOps become a reality?<\/h2>\n
\nReference<\/h3>\n