Alert fatigue is a pervasive challenge in modern IT environments. When teams are inundated with false positives or low-priority notifications, it’s easy to lose sight of real issues. To kick off our blog series on the 5 most common obstacles to observability in 2025, let’s discuss the headache of alert fatigue and how insights from the natural world can offer answers.
What Is Alert Fatigue?
Alert fatigue occurs when excessive notifications overwhelm IT and DevOps teams, slowing response times, driving stress, and increasing the risk of missing critical alerts. The most common causes of alert fatigue in IT include:
- Poorly configured monitoring tools that generate redundant or irrelevant alerts.
- Inadequate prioritization mechanisms giving equal weight to minor and critical alerts.
- Complex environments with siloed monitoring systems, creating fragmented and uncoordinated notifications.
- Lack of intelligent filtering, meaning teams spend more time reacting than proactively addressing root causes.
When IT pros suffer from information overload, maintaining effective observability can become impossible. So, how do we resolve this?
Lessons from Nature Can Help Prioritize the Right Signals
Consider how the human brain handles sensory input. We are constantly bombarded with information—sounds, sights, and stimuli—but our minds instinctively filter out the irrelevant (e.g., background chatter in a busy coffee shop) while staying attuned to vital changes (e.g., someone calling our name). Effective observability solutions should function the same way: reducing unnecessary noise and surfacing only the alerts that truly matter. At a recent event on artificial intelligence, SolarWinds Senior Vice President of Product Management Cullen Childress used a natural analogy to outline a vision for the future of AI-driven observability.
“The human brain (is) the most powerful observability system ever created, if you think about it. You've got your subconscious and your consciousness, and we walk around the earth with our five senses: hearing, smelling, feeling, and seeing. There's so much noise in the environment around us, but our subconscious suppresses all that noise and provokes our consciousness when something relevant happens. When we think about AI and AIOps… reducing alert fatigue and noise while improving signal is really what we're focused on. That's in large part allowing our ITOps professionals—and that subconscious, so to speak–to only be provoked when there are relevant things to focus on.”
Channeling Natural Systems to Tune Out the Noise
Let’s discuss how organizations can take inspiration from natural systems and leverage intelligent alerting strategies to combat alert fatigue.
- Anomaly-based alerting: Artificial intelligence (AI)-driven anomaly detection helps identify deviations from standard patterns rather than triggering alerts based on static thresholds. This is similar to how the brain adapts to familiar surroundings but reacts swiftly to unexpected changes. By leveraging dynamic baselines, teams can reduce false alarms and focus on real issues.
- Predictive and proactive alerting: The best alerting systems don’t just detect problems—they predict issues before they occur. Dynamic thresholding and anomaly detection allow systems to adjust alert criteria based on trends, minimizing reactive firefighting. Forecasting mechanisms enable teams to anticipate capacity issues, latency spikes, and security threats before they impact operations.
- Clustering and contextual alerts: Much like how the human body integrates signals from multiple senses to form a complete picture, observability solutions should consolidate data from different sources. A wise approach to alert management will see notifications correlate across different monitoring layers, from infrastructure and applications to security and compliance. Tools that group related alerts into logical clusters based on time, context, and impact help teams gain a clearer understanding of incidents. Instead of dealing with an overwhelming number of isolated alerts, they see one interconnected picture, helping them diagnose root causes faster.
- Intelligent escalation: In nature, different signals trigger different responses—a sudden movement may alert a prey animal, but a distant rustle might not. IT teams should adopt a similar approach with automated escalation paths, helping ensure only critical alerts reach engineers while lower-priority issues are logged for review.
- Customizability and multi-channel notifications: Not all alerts must be treated equally. Tailorable alerts help ensure that only relevant stakeholders receive critical notifications. The ability to filter, tag, and send alerts through multiple channels (email, SMS, Slack, etc.) optimizes response strategies and reduces unnecessary disruptions.
- Automated remediation and self-healing: Effective alerting is not just about notification—it’s about response. The future of observability lies in automated remediation processes, such as self-healing mechanisms that triage issues without human intervention. Curious? Stay tuned to our latest AI blog series for a deeper dive into where AIOps is headed in the coming years.
Measuring Success in Combatting Alert Fatigue
Effective alert management goes beyond simple notifications—it must be intelligent, predictive, automated, and context-aware. Find an observability platform that integrates some, if not all, of the measures above, and watch your observability strategy transform. Organizations can track success by monitoring key metrics, such as:
- Reduced mean time to resolution (MTTR) through more focused alerting.
- Fewer ignored or dismissed alerts, indicating improved relevance.
- Higher team satisfaction, as engineers experience less burnout from unnecessary notifications.
By studying how natural systems prioritize signals, and incorporating similar cues into their alert management strategies, IT teams can filter out the noise and focus on what matters. Keep an eye out for the next article in this series, where we’ll explore how siloed data and teams hinder progress toward cohesive observability.
For now, check out the 5 most common obstacles to observability today.
