As environments grow more complex, companies are increasingly looking to incorporate AI into their observability programs to prevent outages, which are now costing enterprises up to $300,000 per hour. Traditional tools have promised real-time visibility and proactive issue detection but have often fallen short.
Due to siloed data, high costs, and complex microservice environments, simply layering AI onto traditional observability stacks is insufficient to meet the scale and speed of today’s systems. AI is only as effective as the data it ingests, as that data directly influences the quality of its insights. Conversely, when AI is fed incomplete or narrow data, its outputs are equally limited, leading to missed anomalies, noisy alerts, and slower incident response.
To unlock AI’s full potential in observability, tools must incorporate one of the most valuable yet underutilized sources of telemetry data: unstructured logs.
Unstructured data is underutilized despite accounting for approximately 80% of the world’s data and representing a rich source of information about a system’s behavior. Among unstructured data, logs are particularly valuable. As the most granular form of telemetry data, logs provide detailed, event-level insights that capture the full context of what’s happening within applications and infrastructure without requiring special instrumentation or code modifications. This enables unstructured logs to significantly enhance the data from which AI draws its insights by providing massive amounts of event-specific information, resulting in more accurate insights, reduced alert fatigue and faster incident response.
While metrics offer high-level summaries and traces map transaction paths, unstructured logs fill in the crucial gaps with granular, contextual details. Yet, their sheer volume and lack of structure have historically deterred organizations from fully harnessing them.
Constrained by their inability to process unstructured logs, traditional observability tools are missing out on a significant portion of context-rich data, causing them to fall behind. To meet the demands posed by the scale, complexity, and diversity of modern telemetry data, organizations must tap into the data they have available to generate actionable insights. These legacy stacks lack the comprehensive data integration needed to fully realize AI’s potential.
AI transforms observability by automating pattern recognition across telemetry data and surfacing insights faster than humans can. These capabilities enable proactive incident detection by spotting deviations that static thresholds might miss, reducing false positives and mean time to resolution (MTTR).
While the volume and unstructured nature of logs have historically made them challenging to analyze at scale, modern log management solutions have developed sustainable methods to reduce costs and enable AI to unlock the value hidden in unstructured logs in the following ways:
Real-time Pattern Recognition: AI can efficiently process and correlate massive volumes of unstructured log data to uncover hidden patterns and anomalies that would otherwise go unnoticed. It adapts to the unique behavior of each environment, making it particularly effective in dynamic, distributed architectures. Leveraging logs effectively enables organizations to gain deeper visibility, accelerate root cause analysis, and improve the accuracy of incident detection and prevention.
Context-Aware Alerts: By filtering out noise and prioritizing the most critical alerts, AI ensures that teams focus their attention on issues that matter most, reducing alert fatigue. This prioritization helps streamline on-call workflows, ensuring engineers respond only when truly necessary, rather than chasing down false positives.
Faster Root Cause Analysis: AI accelerates the identification of the source of the issue, enabling faster incident response and reducing mean time to resolution.
Scalable Data Integration: AI helps unify and scale diverse telemetry sources, enabling observability to keep up with growing environments. One of our customers Acquia, for example, consolidated multiple tools into a single observability platform, integrating telemetry from over 20,000 EC2 servers, thousands of Kubernetes pods, and a mix of other collection mechanisms, enabling unified insights at scale.
Adaptive Learning: AI continuously updates its understanding of log formats and patterns as systems evolve, maintaining effectiveness without manual rule updates.
Democratized Access: AI-powered observability platforms are transforming who can participate in monitoring and analysis by making complex tasks, such as querying, more approachable for a broader range of users. Organizations such as Navis can empower entry level staff to learn and use the observability platform, without needing to be a query expert.
AI is fundamentally transforming how engineers troubleshoot by automating complex analysis and accelerating MTTR. However, the effectiveness of AI in observability depends not on how it’s integrated, but on the quality and completeness of the data it consumes. Unstructured logs are an underutilized, rich source of information that significantly expands the data available for AI to analyze, unlocking faster, more accurate, and more context-aware insights.
Organizations that invest in observability solutions that process unstructured logs will accelerate incident detection and response, whereas those that use tools that stack generative AI on top of legacy technology will fall behind. The future of observability lies in leveraging AI’s ability to handle scale, complexity, and unstructured data.
