Cut Alert Fatigue: AI-Driven On-Call Tactics for 2025 Teams

Alert fatigue is a state of cognitive overload that on-call engineers experience when flooded with a high volume of notifications. The consequences are serious, leading to engineer burnout, slower response times (increased Mean Time to Resolution or MTTR), and a greater risk of missing critical incidents. As systems become more complex, traditional, rule-based on-call management is no longer enough. For engineering teams in 2025, AI-driven tactics and platforms have become the go-to solution for creating resilient and efficient incident response processes.

The Crippling Cost of Alert Fatigue in On-Call Engineering

Alert fatigue is a state of mental exhaustion caused by an overwhelming number of alerts, which can lead professionals to ignore them [5]. This problem isn't just an IT issue; it’s also a major concern in high-stakes fields like healthcare, where up to 90% of clinical alarms are false or non-actionable [2]. This fatigue has been shown to correlate with a higher tendency to make medical errors, putting patient safety at risk [3].

For engineering teams, the problem is just as severe. Some enterprise Security Operations Centers (SOCs) deal with over 10,000 alerts every single day [1]. This constant barrage of notifications directly impacts on-call teams in several ways:

• Increased risk: Genuine, critical incidents can get lost in the noise of low-priority or false-positive alerts, increasing the chance they are missed.
• Slower response: Constantly switching between tasks and manually sorting through every alert slows down incident response and resolution.
• Engineer burnout: The relentless pressure and mental strain lead to high rates of burnout and employee turnover.

Platforms like Rootly are designed to combat this problem by using intelligence throughout the entire incident lifecycle, not just at the initial alert stage.

Why Traditional On-Call Alerting Is No Longer Enough

Traditional, rule-based alerting depends on engineers manually setting fixed thresholds. For example, an alert might trigger if CPU usage goes above 90% for five minutes. While simple, this method has major drawbacks in today's complex, distributed systems.

• Alert Storms: A single root cause, like a database failure, can trigger dozens or even hundreds of redundant alerts from connected services, overwhelming the on-call engineer.
• Lack of Context: Alerts are treated as separate pieces of information. They don't include the surrounding context of related events or the overall system state, forcing engineers to piece the story together manually.
• High Maintenance: These fixed rules are fragile and need constant manual updates as systems change. This regular alert analysis is needed to maintain quality but also adds to engineering work [8].
• Static Urgency: Priority is often based on simple, predefined fields and doesn't always reflect the true business impact, leading to a mix-up of priorities.

Unlike these older systems, Rootly AI offers a smarter approach that dynamically adjusts to the state of your environment.

The Rise of AI-Driven Alert Escalation Platforms

AIOps (Artificial Intelligence for IT Operations) is the modern way to manage the complexities of cloud-native systems. Instead of just forwarding raw alerts, ai-driven alert escalation platforms use intelligent analysis, correlation, and prioritization to empower on-call teams.

Intelligent Alert Correlation and Noise Reduction

AI platforms can take in alerts from a wide range of monitoring tools, such as Datadog, Prometheus, and Sentry [7]. The AI then uses machine learning to analyze factors like timing, service connections, and alert content to group related notifications. This process turns dozens of noisy alerts into a single, contextualized incident, preventing alert storms and giving responders a clear view of the problem. This is a key part of how Rootly uses machine learning to prioritize alerts faster.

Smart Prioritization with Machine Learning

By training on an organization's past incident data, machine learning models learn to spot the patterns of high-impact incidents versus low-priority noise. The AI can then dynamically judge an alert's true urgency based on its content and the importance of the affected service. This makes sure engineers are only paged for issues that need their immediate attention. This method has been shown to cut alert noise by up to 90% and significantly improve resolution times [7].

Proactive Anomaly Detection

True observability is more than just reactive alerting. AI-driven platforms analyze streams of observability data—metrics, logs, and traces—to set a dynamic baseline of normal system behavior. This allows the system to spot small changes that often happen before a major failure. This feature moves teams from a reactive "firefighting" mode to a more proactive and predictive approach to incident management, which is a key advantage of AI-powered monitoring over traditional methods.

Comparing the Best On-Call Management Tools for 2025

The market for on-call management tools is changing. While older tools focused on scheduling and routing, modern platforms use AI to manage the entire incident lifecycle. This makes finding the best on-call management tools 2025 a matter of comparing intelligence and automation features.

Feature

Rootly

PagerDuty / Opsgenie (Legacy)

Other Alternatives (e.g., Squadcast)

Primary Focus

End-to-end incident orchestration & automation

On-call scheduling & alert routing

Alert routing & noise reduction

AI-Driven Correlation

Yes, core to the platform

Add-on features, often rule-based

Yes, often with a focus on grouping [6]

Automated Workflows

Advanced (e.g., auto-remediation, comms updates)

Basic (escalations, notifications)

Primarily focused on escalation policies

Post-Incident Automation

Yes (AI-generated retrospectives, metrics)

Manual or basic templates

Limited

Integrations

Deep, bi-directional across DevOps toolchain

Broad, often one-way notification

Focused on monitoring tool inputs

Rootly: The AI-Native Incident Orchestration Platform

Rootly is more than just an alerting tool; it's a complete incident management and orchestration platform. This makes it one of the most powerful pagerduty alternatives for on-call engineers who need more than just a notification system. When comparing rootly vs opsgenie on-call management, the main difference is Rootly's AI-native design.

Key features include:

• AI-native engine: Provides deep noise reduction, smart prioritization, and proactive insights across the entire incident lifecycle.
• Workflow automation: Automates escalations, stakeholder communication, and even fixes like running playbooks or executing Kubernetes rollbacks.
• Deep integrations: Connects smoothly with the entire DevOps toolchain, including Slack, Jira, and Datadog, for a unified workflow.

Rootly can be a powerful standalone platform or an intelligent layer on top of tools like PagerDuty to boost their capabilities with smart escalation and automation.

Legacy Tools and Other AI-Powered Alternatives

PagerDuty and Opsgenie are well-known tools that are good at on-call scheduling and basic alert routing. They have been the foundation for many on-call programs. However, they weren't originally designed for the amount of data and complexity that cause alert fatigue today [4]. Other platforms, like Squadcast, also focus on noise reduction through alert grouping and smart routing [6].

The main difference is that Rootly offers a more complete, end-to-end solution by integrating AI across the entire incident lifecycle—from detection and correlation to fixing and learning—not just at the initial alert stage.

Actionable Tactics for Implementing an AI-Driven On-Call Strategy

Adopting a modern on-call strategy is a clear way to learn how to reduce alert fatigue on-call. Here's a step-by-step guide for teams looking to use these tactics.

Step 1: Unify Alerts into a Central Intelligence Layer

Move away from separate alerting systems where notifications come directly from individual tools. Instead, centralize alerts from all monitoring sources into a single platform like Rootly. This provides the unified data that the AI needs to perform effective correlation and analysis.

Step 2: Design Smart, Automated Escalation Policies

Build flexible escalation policies that do more than simple rotations. In Rootly, you can use a combination of triggers, levels, targets, and conditions to create very specific rules. For example:

"If an incident is a SEV1 AND is related to the 'payments' service, escalate directly to the Lead SRE team AND post an update in the #incidents-critical channel."

This level of automation makes sure the right experts are involved immediately for the right problems without any manual steps.

Step 3: Leverage AI for Real-Time Incident Collaboration

During an incident, AI can act as a real-time assistant for the response team. Features that improve collaboration include:

• AI-generated incident summaries to keep stakeholders informed without distracting responders.
• Incident catch-up for team members who join late to get up to speed instantly.
• "Ask Rootly AI" to ask questions about incident data and timelines using plain English.

These powerful features are at the center of the future of AI incident management.

Step 4: Automate Post-Incident Analysis and Learning

Automate the time-consuming parts of creating retrospectives. AI-driven features can automatically draft summaries of fixes, generate key incident metrics (like MTTA and MTTR), and identify contributing factors. This frees up valuable engineering time to focus on learning from incidents and making preventative changes.

Conclusion: Build a More Resilient and Humane On-Call Culture

Alert fatigue is a major operational risk that harms team health and slows down incident response. Traditional, rule-based systems can't handle the scale and complexity of modern software.

AI-driven platforms like Rootly provide a clear solution. By smartly reducing noise, automating tedious tasks, and offering proactive insights, they turn incident management from a chaotic, reactive process into a structured and efficient one. The goal is to support human expertise, not replace it. By handling repetitive tasks, AI allows engineers to focus on high-impact problem-solving, leading to a more sustainable, humane, and effective on-call culture.

Ready to see how an AI-driven approach can transform your team's on-call experience? Explore Rootly AI and learn how to build a more resilient future.

‍