Until now, the lack of a broadly deployed, modern full stack observability (FSO) solution limited the potential for rapid problem resolution—including automatic remediation capabilities—causing IT operations teams to spend more time in reactive mode, rather than delivering new solutions that support innovation.

FSO offers comprehensive visibility into the entire technology stack, from applications and databases to infrastructure and networks. This capability allows IT teams to proactively identify and resolve issues before they escalate, enhancing both operational efficiency and resiliency.

One of the key advancements in FSO is the integration of AI-driven insights and root cause analysis (RCA). By harnessing the power of machine learning, AI-driven insights can process vast amounts of data to detect anomalies, predict potential issues, and provide recommendations for corrective actions. This enables a shift from reactive problem-solving to proactive management. RCA, in turn, helps IT teams quickly identify the underlying cause of an issue rather than addressing its symptoms, reducing downtime and improving MTTR.

The new generation of AIOps platforms is easier to use and generates more actionable insights for better data-driven decisions and operational outcomes. AIOps-driven platforms increasingly combine functionality of best-of-breed point solutions such as application performance monitoring, continuous network discovery and configuration management database (CMDB) integration, and automated topology mapping.

Organizations teams can benefit from a planful, phased approach to the adoption of automation capabilities to ensure the most efficient, empowering, and non-disruptive implementation. To achieve this, IT and network managers need to follow a structured approach that minimizes risks while maximizing the benefits of automation.

The first step is to conduct a comprehensive audit of your existing infrastructure, provisioning and monitoring tools (and their integrations), and operational processes. By understanding your current state, IT teams can identify areas where AIOps can provide the most immediate value without overlapping or conflicting with existing systems or management domains. Rather than implementing AIOps across the entire infrastructure at once, it is a best practice to plan your AIOps deployment in stages, focusing on specific deployment and management domains. Most organizations start with a pilot that targets specific, non-critical operations and can help finetune the implementation process—while training the operations team on the platform itself. Phased rollouts also allow for a more measured approach to scope expansion, configuration changes, and the adoption of new integrations without impacting mission-critical systems or disrupting established processes and workflows.

A smooth AIOps implementation requires collaboration between IT operations, enterprise architects, application owners, security teams, and change control boards—all while incorporating user experience metrics where possible. These teams must share knowledge to ensure the AIOps solution can most effectively ingest data from various sources and provide holistic and valuable insights. To minimize disruptions, staff should be adequately trained on the new AIOps tools and processes.

Intent-based networking (IBN), Infrastructure as Code (IaC), and process automation are revolutionizing the way IT teams manage and control increasingly complex infrastructures, including edge locations. These technologies offer a more dynamic, responsive, and scalable way to handle modern IT environments by automating and simplifying network and infrastructure management tasks.

Intent-based networking allows IT teams to define the desired outcomes (the "intent") for a network, and then the network automatically adjusts and configures itself to meet those outcomes. This is a significant shift from traditional networking where administrators manually configure network devices. With IBN, the network is not only automated but also “intelligent”—it can continuously monitor itself, scale, adapt to changes (including those introduced by human error), and even correct issues before they impact performance or result in outages.

IaC is a methodology that allows infrastructure to be provisioned and managed through machine-readable configuration files, rather than through manual processes. This approach brings the agility and efficiency of DevOps practices, such as continuous integration / continuous delivery (CI/CD) pipelines and workflows, into infrastructure provisioning and configuration management. With IaC, IT teams can easily spin up and modify infrastructure across cloud and on-premises environments, ensuring consistency and reducing deployment times.

Process automation, especially when integrated with AIOps, plays a critical role in managing complex infrastructures and highly distributed edge locations. Automation tools can handle repetitive tasks like patch management, monitoring and incident response, and even advanced tasks like anomaly detection, event correlation, and root cause analysis via AIOps.

When evaluating AIOps solutions, look out for these critical capabilities:

• Predictive analytics: By analyzing vast amounts of data from various IT systems, AIOps can predict potential issues like performance bottlenecks and degradation, capacity depletion, system failures, or network disruptions before they impact users.
• Real-time monitoring, event correlation, and intelligent alerting: AIOps platforms continuously monitor systems in real-time—while incorporating event correlation, and false positive or duplicate event detection, so that alerts are more effectively consolidated and prioritized—bringing to human focus those anomalies or deviations from nominal behavior that are most impactful.
• Automated root cause analysis: When an issue does occur, some AIOps platforms can automatically pinpoint the root cause, saving IT teams valuable time in troubleshooting and resolving problems.
• Adaptive learning: Some AIOps platforms feature ML-based algorithms that continuously learn and adapt to changing patterns in IT environments, improving their accuracy in identifying—or even preventing—potential issues over time. Some systems incorporate fault detection and analysis data (including predictive analytics) from infrastructures worldwide—effectively crowdsourcing real-world behavioral data—so that operations teams can receive custom-tailored bulletins advising them against certain actions (such as patches or upgrades) that are specific to their environment.

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