As digital services become more complex, the teams responsible for keeping them running face immense pressure. The sheer volume of data and alerts from modern IT systems can be overwhelming, leading to challenges like alert fatigue and slow incident response times. Traditional, manual on-call management simply can’t keep up. This is where AI-powered on-call software emerges as a critical solution. By embracing Artificial Intelligence for IT Operations (AIOps), teams can automate tedious tasks, reduce manual work, and significantly improve the reliability of their services [8].
Why AI is Transforming On-Call Management
The world of on-call management is shifting from manual, reactive processes to intelligent, automated systems. Instead of simply waking someone up in the middle of the night, modern tools use AI to provide context, filter out noise, and even suggest solutions. This transformation is not just about convenience; it's about building more resilient and efficient operations.
The primary benefits include:
- Proactive issue detection: AI can identify potential problems before they impact customers.
- Accurate alert routing: Alerts are instantly sent to the correct on-call engineer, every time.
- Reduced pager fatigue: AI suppresses noise and groups related alerts, so responders only focus on what matters.
- Faster resolution times: AI-driven insights and streamlined collaboration tools help teams resolve incidents quicker.
By analyzing historical data to find patterns, AI delivers actionable insights that enable a more proactive approach to reliability. This allows teams to move from firefighting to preventing fires in the first place, which is a core part of building a strong incident management practice with tools like Rootly AI.
Key Features of the Best AI On-Call Software
When looking for the best oncall software for teams, it's important to understand the key features that set top-tier platforms apart. These components work together to create a seamless and intelligent on-call experience.
Intelligent Alerting and Routing
At its core, on-call software must handle alerts effectively. AI enhances this by automatically processing alerts from various monitoring tools like Datadog, Grafana, or Sentry. The system intelligently deduplicates, correlates, and enriches this information, providing responders with clear context.
Intelligent routing then uses schedules, escalation policies, and urgency rules to ensure the right person is notified immediately. For even greater control, teams can create custom alert workflows that automate actions based on an alert’s content, such as automatically creating an incident or paging a specific team. This ensures all alerts are handled according to predefined logic, reducing manual effort.
Automated On-Call Scheduling and Escalations
Managing who is on-call can be complicated, especially for global teams. The best software offers flexible on-call scheduling with support for:
- Multi-person rotations
- Layered coverage (e.g., primary and secondary responders)
- Time-zone-aware planning
- Holiday and out-of-office overrides
Automated escalation policies are crucial for preventing missed alerts. If a primary responder doesn’t acknowledge an alert within a set time, the system automatically escalates to the next person or team. Notifications are sent through multiple channels to ensure they are received, including voice calls that can override Do Not Disturb settings, SMS messages, push notifications, and Slack messages. You can learn more about how to create and manage these schedules to fit your team's needs.
AI-Assisted Incident Collaboration
During an incident, cognitive load on engineers is high. AI can act as a real-time assistant, helping teams collaborate more effectively and resolve issues faster. Key features include:
- Generated Incident Titles: AI automatically creates clear, descriptive titles from alert data, so everyone understands the issue at a glance.
- Incident Summarization: On-demand summaries provide a quick overview of an incident’s status and key events, helping newcomers get up to speed without interrupting the team.
- "Ask Rootly AI": Team members can ask questions in plain language to get information about the incident, past incidents, or system runbooks.
These AI capabilities turn your incident channel into a hub of intelligent collaboration.
Proactive Health Checks and Live Call Routing
Modern on-call management isn't just about reacting to failures. Proactive features like Heartbeats allow teams to monitor systems and get alerted if a critical service or backup job fails to check in on time. This helps you discover "silent failures" before they cause a major outage.
Additionally, Live Call Routing gives customers or internal stakeholders a dedicated phone number to trigger a page and connect directly with the on-call responder. This provides a reliable, human-centric way to report urgent issues. You can explore a complete overview of these essential on-call components to see how they fit together.
Comparing the Best AI On-Call Software for Teams
Several platforms are leading the way in AI-powered on-call management. Here’s a look at how they compare.
Feature
Rootly
BigPanda
Datadog Bits AI
LogicMonitor Edwin AI
AI-Native Architecture
Yes (End-to-end platform)
Yes (Focused on Agentic AI)
No (Add-on to Datadog)
Yes (AIOps Agent)
Alert Workflows
Highly customizable, no-code/low-code
Automated correlation
Actionable insights
Predictive analysis
Scheduling Flexibility
High (Layered, multi-timezone, overrides)
Moderate
Basic (Within Datadog)
Moderate
Integrations
Extensive and bi-directional
Strong, with focus on data ingestion
Datadog ecosystem-focused
Extensive library
Rootly: The End-to-End AI-Native Platform
Rootly stands out as a comprehensive, AI-native platform that unifies on-call management, incident response, and retrospectives in one place [5]. Its design philosophy is built on a human-AI partnership, where AI augments engineering expertise rather than just replacing tasks. With a powerful workflow engine and a flexible API for custom automations, Rootly allows teams to deeply customize their incident management process to fit their exact needs. This end-to-end approach ensures a seamless experience from the first alert to the final lesson learned.
BigPanda: Agentic AI for IT Operations
BigPanda focuses on using agentic AI to automate Level 1 IT operations and reduce the manual effort involved in incident response [7]. Its strength lies in its ability to correlate a high volume of alerts from disparate monitoring tools into a manageable number of actionable incidents. BigPanda's IT Knowledge Graph helps unify data and provide context for faster triage.
Datadog’s Bits AI SRE
Bits AI SRE is an AI-powered teammate designed to assist Site Reliability Engineers (SREs) who are already using the Datadog platform [1]. Its primary function is to provide actionable insights and recommendations directly within the Datadog ecosystem, helping teams investigate and resolve issues faster without leaving their primary monitoring tool.
LogicMonitor's Edwin AI
Edwin AI is an AIOps agent from LogicMonitor designed to manage the entire incident lifecycle, from automated root cause analysis to resolution [3]. It has a strong focus on predictive outage prevention and leverages an extensive library of integrations to provide visibility across hybrid IT environments.
How to Choose the Right AI On-Call Software for Your Team
Adopting an AI-powered solution is a significant step. Here’s what to consider when making your choice.
Evaluate Integration and Customization Needs
The platform you choose must fit into your existing ecosystem. Look for a solution that seamlessly integrates with the tools your team already uses, including:
- Monitoring: Datadog, Prometheus, Grafana
- Communication: Slack, Microsoft Teams
- Ticketing: Jira, ServiceNow
Beyond standard integrations, a flexible API is essential for building custom workflows that align with your team’s unique processes.
Assess the Depth of AI Capabilities
Not all AI is created equal. Look beyond basic automation and evaluate the sophistication of the AI features. Ask questions like:
- Does the tool offer proactive insights to prevent incidents?
- Can it generate useful, context-aware summaries during an incident?
- Does it support natural language queries to help engineers find information faster?
Some platforms leverage "agentic AI" to autonomously investigate alerts, which can dramatically reduce the burden on on-call engineers [2].
Prioritize a Unified User Experience
The best tools reduce friction, not add to it. A platform that centralizes the entire incident lifecycle—from alert to retrospective—is invaluable. This unified experience minimizes context-switching and ensures all responders, stakeholders, and subject matter experts are informed and aligned within their primary collaboration tools like Slack or Microsoft Teams.
Conclusion: Build a More Resilient Future with AI
The future of on-call management lies in a powerful partnership between humans and AI. Modern on-call software is no longer just about paging an engineer; it’s about creating a proactive, efficient, and resilient incident management culture. By automating manual tasks, providing intelligent insights, and centralizing collaboration, these tools empower teams to move beyond reactive firefighting.
Solutions like Rootly provide an end-to-end platform that enables teams to build more reliable systems and spend more time on innovation. By unifying on-call scheduling, incident response, and retrospectives, you can create a culture of continuous improvement.
Ready to see how AI can transform your on-call process? Get started with a platform designed for the future of reliability.