Ned Lidbury, Director, Solutions Consulting, ANZ and ASEAN, New Relic, says traditional monitoring methods are no longer sufficient in managing the complexity and scale of modern IT environments – particularly those driven by distributed systems and AI applications.
Today’s distributed and AI-driven systems have upped the ante for IT teams in maintaining system performance and reliability. Traditional monitoring tools, while effective in simpler contexts, often fail to provide the deep insights required in more complex settings.
Organisations are turning to observability to help capitalise on AI technologies and business applications. The New Relic 2024 Observability Forecast found that adoption of AI technologies was the top driver for observability (38%) among respondents in Southeast Asia (SEA). Those who deployed AI-driven observability reported a 28% higher business value and return on investment (ROI) overall.
These findings reflect not only the growing importance of observability in supporting innovation but also that AI itself is emerging as a symbiotic force – complementing the practice of modern, intelligent observability in more ways than one.
Observability simplifies AI complexities
Observability provides a detailed view of organisational system health and performance. It involves collecting and analysing telemetry data, such as metrics, events, logs and traces (MELT), to understand not just what’s happening within a system, but why. This deeper level of insight is crucial for identifying and resolving issues in real time, ensuring that systems perform optimally under various conditions.
AI-driven systems introduce additional layers of complexity to observability. These systems often involve intricate data pipelines, model training and inference processes, and dynamic scaling based on real-time data. Observability in this context must extend beyond traditional MELT data to include the specific behaviours and performance characteristics of AI components.
As we navigate through an era dominated by advancements in AI technologies, it’s clear that this technology is not only a driving force behind new applications and systems, but also a transformative component in how these systems are managed and monitored.
AI key to intelligent observability
The complexity of modern IT environments, especially those infused with AI, has outpaced the capabilities of traditional monitoring practices. AI has also become the solution, revolutionising how observability is approached, implemented, and utilised in today’s tech landscape. By incorporating AI into observability, it becomes intelligent enough to keep up with ever-growing digital complexity.
Here are some fundamental ways AI is transforming how organisations approach observability:
- Automated anomaly detection – AI significantly enhances the ability to detect anomalies through the automatic analysis of vast amounts of telemetry data and by identifying deviations from normal behaviour. In traditional systems, anomaly detection could involve tracking metrics like CPU usage and then triggering alerts when predefined thresholds are breached. AI goes a step further by learning what “normal” looks like in a dynamic environment and detecting subtle issues that could be missed by static thresholds. For instance, in cloud infrastructure, AI can identify an unusual spike in resource consumption, which could indicate a potential scaling issue or security breach, even if it doesn’t cross standard thresholds.
- Predictive analytics for preventive monitoring – AI doesn’t just help detect current issues; it also plays a crucial role in predicting future problems. Predictive analytics, powered by machine learning, can analyse trends in telemetry data to forecast potential system failures or performance bottlenecks before they occur. By anticipating these issues, teams can take preventive action, such as scaling resources or adjusting configurations, to ensure continuous system performance and reliability.
- Root cause analysis – When issues arise, determining their root cause can be a complex and time-consuming process, especially in distributed systems with many interdependent components. Intelligent observability tools enhance this process by employing AI-driven data correlation techniques that automatically analyse and correlate data from multiple sources, enabling an understanding of the most likely root causes.
- Alerting correlation and noise reduction – In complex IT environments, a single issue can trigger multiple alerts across various components, leading to “alert fatigue” where critical signals are buried in a flood of notifications. By using alert correlation techniques, these individual alerts can be grouped into a single incident, reflecting the broader issue rather than treating each symptom as an isolated problem. Modern observability practices can enhance this process by automatically correlating alerts based on patterns in the data, such as shared infrastructure components, timing, or similar error messages. This approach not only reduces the alert noise but also provides a more coherent view of what’s happening in the system to reduce MTTR.
As AI continues to evolve, it plays an increasingly vital role in the evolution of observability practices.
Traditional monitoring methods are no longer sufficient in managing the complexity and scale of modern IT environments, particularly those driven by distributed systems and AI applications. But simultaneously, AI can be leveraged to gain deeper insights and when integrating these advanced capabilities, organisations can ensure their systems remain reliable, scalable and optimised for performance.
