In many organizations, problems with IT systems often stem not from a lack of data, but from teams struggling to understand the data they already have. From outdated legacy systems to modern Smart Ops platforms, the reliability of IT infrastructure is now a key topic in executive meetings. Technologies like Observability, Site Reliability Engineering (SRE), and Artificial Intelligence for IT Operations (AIOps) are reshaping how companies manage their digital operations. For example, one Monday morning at a mid-sized agricultural distributor, the order-taking system slowed down, triggering forty alerts in ten minutes. An emergency committee was assembled, with each team claiming their systems were working properly. After two hours, the root cause was found: a process from a fifteen-year-old application had overflowed into the day, overloading the shared database. Fixing the issue took ten minutes, but the time spent diagnosing it was over a hundred. As a result, two delivery rounds were left incomplete, and client retailers had to notify the sales department. This kind of situation is common across many organizations, revealing a paradox: despite having access to vast amounts of operational data, answering basic questions—what is happening, what it costs the business, and where to act—remains difficult. The number of signals and alerts has grown faster than the ability of teams to interpret them. According to a 2024 survey by the ITIC, one hour of system unavailability costs over $300,000 to more than 90% of mid-sized and large enterprises, not counting penalties or legal issues. For financial directors, this highlights a clear lesson: the cost of diagnostic time is often underestimated. Cutting down on the time needed to diagnose problems can have a significant impact on profitability. Regulatory requirements are also shaping how companies handle IT risks. For banks and insurers, the Digital Operational Resilience Act (DORA) places ultimate responsibility for information risk on the executive team, requiring major incidents to be classified and reported quickly. Similarly, companies in sectors covered by the Network and Information Security Directive 2 (NIS2), such as energy, transport, and healthcare, must have their risk management strategies approved by executives, who may even face personal liability. In both cases, the same question will be asked after an incident: what measures had been put in place to detect, understand, and contain the problem? Traditional monitoring systems answer basic questions, such as whether a threshold has been exceeded or if a server is responding. Observability, on the other hand, allows for more complex and unexpected questions by connecting logs, metrics, and traces throughout a business process, from the customer's screen to the database. In the example, an end-to-end trace would have linked the perceived slowness to the outdated process. However, this requires instrumentation of all applications, including the oldest ones, as an observability platform only sees what is provided to it. Open standards like OpenTelemetry allow this without relying on specific vendors. It is also important to choose what data is collected, as storing everything all the time can increase storage costs without improving understanding. Observability and security detection increasingly rely on the same data. Funding them separately can lead to paying twice for half the insight. SRE provides a shared language between IT and business, recognizing that perfect reliability is too costly and must be balanced. A 99.9% availability target allows about 43 minutes of interruption per month, helping to guide priorities between development and stability. With clear objectives, reports that avoid blame, and automation of routine tasks, reliability becomes a shared commitment. AIOps mainly provides sorting capabilities: grouping alerts, detecting anomalies, and suggesting causes. However, it cannot improve a poorly observed or poorly mapped IT system. With incomplete data, it can create misleading correlations. Therefore, AIOps comes after observability, never in its place. AI agents capable of acting in production are becoming new participants in the IT system, able to restart a service, cancel a deployment, or modify a configuration. It will be necessary to define their rights, track their actions, and control their decisions, as an opaque automation can turn an efficiency gain into a new operational risk. Legacy systems, including mainframes, historical enterprise resource planning (ERP) systems, and night-time processes, will remain central to many IT systems for a long time. The challenge is to make the operations around them more intelligent, in the right order: identify critical paths, instrument them, set reliability targets, automate repetitive and mastered tasks, and then delegate to AI the correlation and, only then, a controlled part of the action. Each member of the executive committee can measure where the company stands with a single question. For the CEO: what is the customer journey whose interruption costs the most, and is its reliability target written down? For the CFO: during the last major incident, how many hours were spent on diagnosis rather than repair? For the RSSI: what automated actions are currently executed in production without human validation, and who authorized them? Three vague answers already outline a roadmap. The reliability of an IT system is no longer measured by the number of alerts handled, but by the company's ability to understand, anticipate, and decide. At this level, it no longer falls under operations. It falls under management.