What Are AIOps Tools?
AIOps tools use machine learning, big data, and automation to enhance IT operations. These tools analyze IT data, detect anomalies, and automate tasks, improving efficiency and reducing manual effort. Popular AIOps tools include Selector, Splunk, Dynatrace, Datadog, BigPanda, Dell AIOps, IBM Cloud Pak for AIOps, and LogicMonitor.
As traditional monitoring solutions struggle with the scale and speed of modern environments, AIOps tools provide a more intelligent and automated approach to prevent downtime, reduce manual intervention, and accelerate problem resolution. They integrate with various data sources, including applications, networks, and infrastructure, to collect vast amounts of telemetry and turn it into actionable insights.
Key features and functionalities of AIOps tools include:
- Data ingestion and analysis: AIOps tools ingest data from various sources, including logs, metrics, and events, and analyze it to identify patterns and anomalies.
- Anomaly detection: These tools can detect unusual patterns or deviations from normal behavior in IT systems, helping to identify potential issues before they escalate into major problems.
- Predictive analytics: Some AIOps tools can predict future issues based on historical data and trends, enabling proactive problem-solving.
- Root cause analysis: AIOps can automate root cause analysis by correlating events and identifying the underlying causes of issues.
- Automation: AIOps tools can automate various IT tasks, such as incident response, remediation, and resource provisioning, freeing up IT staff for more strategic work.
- Integration: AIOps platforms often integrate with other IT management tools, such as ITSM systems and monitoring platforms, to provide a holistic view of IT operations.
In this article:
- Selector
- Splunk IT Service Intelligence
- Dynatrace
- Datadog
- BigPanda
- Dell AIOps
- IBM Cloud Pak for AIOps
- LocigMonitor
Key Features of AIOps Tools
Data Ingestion and Analysis
Data ingestion enables AIOps tools to automatically collect data from a wide range of sources, such as monitoring platforms, application logs, infrastructure metrics, and event streams. This process must handle extremely high data velocities, as modern IT environments can generate millions of data points per minute. Advanced normalization and preprocessing techniques are also applied to maintain data quality, structure, and relevance—setting a solid foundation for accurate downstream analytics.
Once ingested, the analysis component uses AI and ML algorithms to correlate, categorize, and enrich the data, transforming raw telemetry into usable information. This intelligence is crucial for identifying trends, patterns, and relationships that are otherwise difficult to spot in large, noisy datasets. By automating these processes, AIOps tools make it possible to detect incidents and potential performance issues much earlier than through manual review alone.
Anomaly Detection
Anomaly detection uses statistical analysis and machine learning to identify events or behaviors that deviate from established patterns. Instead of relying on fixed thresholds or rules, AIOps tools learn what constitutes normal activity for systems and flag outliers that might indicate incidents, performance degradations, or security breaches. This adaptive approach minimizes false positives, surfaces genuine threats sooner, and accelerates operational response.
A reliable anomaly detection engine can differentiate between minor fluctuations and significant problems by tracking historical behavior and context. This prevents unnecessary alerts that often lead to alert fatigue among IT staff while ensuring critical anomalies are investigated promptly. By providing real-time visibility into anomalous conditions, AIOps tools enable teams to act before issues escalate into outages or cause widespread impact.
Predictive Analytics
Predictive analytics in AIOps platforms leverages historical and real-time data to forecast potential incidents before they occur. Using time-series analysis, pattern recognition, and regression models, these tools can predict looming resource constraints, potential outages, and performance bottlenecks. The aim is to shift IT operations from a reactive approach to a proactive one, allowing preemptive measures that prevent disruptions.
Organizations benefit from predictive capabilities by reducing unplanned downtime, optimizing resource usage, and improving service reliability. Over time, as the models are refined with more data, the predictions become increasingly precise and actionable. This enables IT teams to prioritize maintenance, scale infrastructure effectively, and plan capacity in line with business objectives.
Root Cause Analysis
Root cause analysis (RCA) is the automated process of quickly determining the primary source of an incident. With multiple systems and dependencies in modern IT environments, identifying the true cause of an issue is time-consuming and often overwhelming. AIOps tools use machine learning models and dependency mapping to correlate symptoms across the stack, drastically narrowing down the scope for human operators.
Automated RCA tools begin by correlating alerts, logs, and events, constructing a contextual view of all related activity. They then apply causality analysis techniques, tracing the chain of events backward to isolate the root problem rather than just the symptoms. This speeds up resolution, reduces the risk of recurrence, and helps teams reallocate resources from troubleshooting to strategic initiatives.
Automation
Automation in AIOps platforms enables the execution of routine operational tasks without human intervention, reducing response times and operational overhead. By integrating with orchestration tools and IT workflows, AIOps can automatically remediate incidents, scale resources, or trigger predefined actions based on detected conditions. For example, when a performance issue is identified, the system might restart a service, allocate additional compute capacity, or roll back a problematic deployment autonomously.
These automated workflows are often driven by AI-assisted decision-making, ensuring actions are context-aware and aligned with operational policies. Runbooks and playbooks can be codified into the platform, allowing repetitive tasks such as log cleanup, patch application, or service restarts to be performed reliably and consistently.
Integration Capabilities
Effective AIOps deployments require extensive integration capabilities, allowing the platform to connect to a broad array of data sources, ITSM (IT Service Management) platforms, and automation tools. This flexibility ensures that data silos are eliminated and that the operational insights generated by the AIOps engine can trigger remediation workflows or third-party notifications as needed. Native or API-driven integrations enable seamless data flow and interoperability between the AIOps platform and critical components of the IT ecosystem.
These connections increase the overall value of AIOps by ensuring its insights are instantly actionable, whether through automated ticket creation, runbook execution, or collaboration tools. The more comprehensive the integration support, the more quickly organizations can realize the benefits of full-stack automation.
Notable AIOps Tools
1. Selector
Selector is an AIOps platform purpose-built for network operations teams seeking full-stack visibility, AI-driven insights, and automated workflows. Designed to simplify complex hybrid and multi-cloud environments, Selector ingests data from telemetry sources, event systems, CMDBs, and collaboration tools to unify observability across the stack.
Key features include:
- Natural Language Interface (Copilot): Query incidents, trends, and telemetry using everyday language via Slack, Teams, or API, enabling fast, intuitive troubleshooting.
- AI-Driven Root Cause Analysis: Correlates logs, metrics, events, and topology to isolate root causes and eliminate alert fatigue.
- Digital Twin Modeling: Dynamically maps services, applications, and infrastructure to create a contextual model of your environment.
- Flexible Integrations: Connects to over 300 platforms and data sources, including ServiceNow, Splunk, PagerDuty, NetBox, and SNMP to deliver unified, enriched context.
- Noise Reduction & Alert Prioritization: Reduces alert volume by 70-90% and ranks incidents by business impact for smarter triage and response.
2. Splunk IT Service Intelligence
Splunk IT Service Intelligence (ITSI) is an AIOps solution that enables IT teams to monitor service health, predict issues, and accelerate incident resolution using analytics and machine learning. Designed for data-rich environments, it consolidates logs, metrics, and events into a unified platform.
Key features include:
- Service-oriented dashboards: Track KPIs, SLAs, and service health using customizable dashboards
- Intelligent incident management: Automate event correlation, incident prioritization, and ITSM integrations
- Predictive analytics: Use machine learning and historical data to detect anomalies and predict potential service degradations
- Root cause analysis: Look into service metrics and logs with tools like Log Observer to isolate issues
- Automated event aggregation: Enrich and correlate events from diverse sources using ML-driven policies to trigger alerts
3. Dynatrace
Dynatrace is an AIOps platform to monitor dynamic cloud environments, detect issues, and deliver automated root cause analysis. Davis®, its AI engine, continuously analyzes dependencies and telemetry to surface insights without manual configuration or model training.
Key features include:
- Davis® AI engine: Continuously analyzes dependencies and provides root cause analysis
- Auto-discovery: Automatically detects and maps infrastructure, services, and dependencies in cloud and Kubernetes environments
- Contextual observability: Combines metrics, logs, traces, user experience, and topology for context and AI-driven insights
- No manual configuration: Starts analyzing environments immediately upon deployment
- AI-driven problem detection: Identifies all symptoms connected to a single root cause
4. Datadog
Datadog’s Event Management platform applies AIOps to simplify incident response by consolidating alerts and reducing noise across complex environments. It centralizes alert data from native and third-party sources, enriches it with observability context, and correlates events to identify root causes faster.
Key features include:
- Unified alert aggregation: Collect alerts and events from Datadog and external tools into a single incident view
- Contextual enrichment: Automatically improve alerts with business-specific data, service ownership, and CMDB metadata
- AI-driven correlation: Use machine learning and rule-based patterns to deduplicate and correlate alerts, surfacing only meaningful incidents
- Centralized event view: Group, filter, and analyze related events in one dashboard
- Automated triage workflows: Speed up investigations by prioritizing and escalating cases based on correlated data
5. BigPanda
BigPanda is an AIOps platform to automate L1 operations and assist incident response teams with detection and correlation. By unifying fragmented data and integrating structured machine signals with operational knowledge, it enables faster incident handling and problem prevention.
Key features include:
- AI detection and response: Automate the identification, prioritization, and resolution of incidents to reduce manual workload and escalate critical issues
- Agentic AI incident assistant: Support escalation teams with AI that automates tasks, surfaces insights, and simplifies collaboration during incident response
- IT knowledge graph: Unify siloed operational data and connect it with human context to enable insights and automated decision-making
- Unified analytics: Track operational metrics, monitor patterns, and identify optimization opportunities to improve resilience
- Preventive intelligence: Detect gaps in monitoring coverage, reduce false positives, and eliminate root causes of recurring issues
6. Dell AIOps
Dell AIOps is a cloud-based observability and automation platform to optimize Dell infrastructure with AI-driven insights and predictive intelligence. It enables IT teams to manage performance, security, and sustainability across their environments.
Key features include:
- AI-driven observability: Continuously monitors system health, detects anomalies, and protects against threats like ransomware
- Predictive intelligence: Uses forecasting and analytics to identify and resolve infrastructure issues
- Proactive automation: Automates routine tasks like cybersecurity checks and system diagnostics to reduce manual workload
- Sustainability insights: Tracks energy usage and system efficiency to support sustainability goals and reduce carbon footprint
- Generative AI assistant: Provides recommendations and simplifies troubleshooting through an integrated AI-based interface
7. IBM Cloud Pak for AIOps
IBM Cloud Pak for AIOps is an AI-driven platform that unifies operations across IT environments by integrating monitoring, event management, and automation. It aims to transform fragmented alerts into contextual insights, helping teams detect, correlate, and resolve incidents more efficiently.
Key features include:
- Unified visibility: Visualizes the IT estate, mapping dependencies between systems to show the impact of incidents
- Event correlation and deduplication: Uses AI to reduce alert noise by clustering related events and identifying root causes
- Anomaly detection: Identifies deviations from normal behavior early, enabling teams to act before issues escalate
- Unified incident management: Consolidates incident data and provides shared context across tools to simplify triage and resolution
- Collaborative war room: Supports cross-team coordination with insights
8. LogicMonitor
LogicMonitor delivers an agentic AIOps solution to help IT teams shift from reactive firefighting to strategic control. It relies on Edwin AI, a self-learning agent that autonomously analyzes structured and unstructured data, detects early signals, and prevents incidents before they occur.
Key features include:
- Agentic AI (Edwin AI): Learns and adapts to provide contextual insights and automated responses
- Event intelligence: Filters out noise and surfaces early indicators of issues
- No manual tuning required: Eliminates the need for rule-writing and static topology maps by adjusting to the IT landscape
- Natural language insights: Converts telemetry into plain-language summaries and guided troubleshooting
- Predictive analytics: Anticipates problems using trends and patterns across datasets
Conclusion
AIOps platforms are becoming essential for managing the complexity of modern IT environments. By combining machine learning, big data analytics, and automation, these tools enable faster incident detection, root cause analysis, and proactive issue prevention. They help IT teams move beyond reactive operations to achieve greater efficiency, resilience, and scalability.
Learn more about how Selector’s AIOps platform can transform your IT operations.
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