Microservices, containers, the multicloud and software-defined environments have added another layer of complexity to cloud infrastructure. To understand everything that happens in these constantly changing environments all the time, observability needs to scale, so that teams move away from simply observing and reacting to problems as they arise, to a culture of proactive understanding and infrastructure optimization. Observability should go beyond mere monitoring (even of very complicated infrastructure) and address building visibility into all layers of your business. Increased visibility gives everyone invested in the business a better view of issues and user experience, and creates more time for more strategic initiatives. It's also critical to the overall success of site reliability engineering organization (SRE) or DevOps models. Simply defined, observability is the ability to answer any question about your business or application, at any time, regardless of the complexity of your infrastructure. The way to do this in the context of operations, and application development is simple – by instrumenting systems and applications to collect metrics, traces, and logs and sending all this data to a system that can store it, analyse it and help you gain insights. Automation and intelligence are essential, therefore, to transform how teams work to achieve observability at scale, quickly and efficiently. Mainly, automation of the continuous mapping of components, cloud services and the ever-changing relationships between potentially billions of interdependencies, and the discovery of new components, aimed at avoiding gaps in real-time coverage. As dynamic multi-cloud environments can change in seconds, AI needs to know accurate answers and be able to automatically anticipate and resolve problems before business impact. Some critical AI resources that enable observability at scale are: Auto-adaptive threshold - to prioritise what really matters. Intelligent grouping of anomalies – to eliminate redundant work between teams. Fault tree analysis - to deliver answers instantly. Automation is one of the hottest trends in observability. The goal of AI is to provide rapid responses to engineering, infrastructure, operations and application teams and empower them to focus on the things that matter. In addition, high-quality observability systems have learning algorithms that can understand the past integrity of your services and applications to help predict what will happen in the future. A complete ingestion of all data about your business helps machine learning models gain accurate insights from real-time and historical data – ML helps predict potential future high-probability events and harnesses the power of AI for predictive intelligence. Providing a precise answer to every problem that everyone understands can transform teams away from finger-pointing to effective cross-team collaboration that drives business results. Several important requests will enable teams to collaborate more efficiently for the same technical and business SLIs/SLOs, such as: Single data model to scale observability across all layers and components across the technology stack. Shared context that facilitates collaboration between teams, with flexibility to analyse infrastructure, applications, operations and business data. Perfect combination of the entire software lifecycle, from development resources, testing, releases and continuous optimizations, to innovate faster, with higher quality. There's more: an external perspective is always needed to create a feedback loop from back-end technology teams to product, digital and business teams, ensuring that the entire cloud stack is supporting the expected outcomes. To do this, companies should include user experience in a smarter observability approach, connecting the front-end and back-end perspective to understand the user experience across all channels. Benefits of observability at scale The degree to which a team or company values the ability to inspect and understand systems, their workload and their behaviour will enable observability to look at the outcomes of the system as a whole in a facilitating manner: A comprehensive understanding of complex systems; Smarter planning for code releases and application capacity; Faster problem resolution and shorter MTTR; More insightful incident analysis; Increased uptime with improved performance; More satisfied clients and increased revenue. In other words, once you adopt observability at scale, you will start paying attention to the overall system and user experience, not to individual components of the IT infrastructure. Scaled observability to focus on what really matters.