Table of contents
Highlights
- AIOps platforms work best when you evaluate them against the telemetry they can ingest and normalize (metrics, events, logs, and traces) and how they turn that data into correlated incidents.
- Event correlation and root cause analysis depend on context, like topology or service mapping and dependency graphs, not just alert grouping. Test whether a platform can answer "why did this happen" before you buy it.
- Enterprise selection should include governance requirements such as RBAC, auditability, and data handling expectations, since explainability influences adoption during incidents.
- ITSM and incident workflow integrations can influence time-to-value as much as model quality, especially for automated ticket enrichment, routing, and remediation triggers.
- After your AIOps platform surfaces an incident, getting to resolution still requires action across systems. Moveworks AI Assistant can help operationalize that last mile, executing requests, routing work, and keeping employees informed, without adding a separate service desk layer.
Your IT environment is likely growing faster than your teams can keep up with. Whether you're balancing on-premises infrastructure with legacy systems or managing multicloud environments, things can get complicated fast.
This reality becomes clear when a single application slowdown triggers dozens of alerts across all your performance monitoring, network, and infrastructure tools simultaneously. If none of these tools communicate with each other, they can create more noise than they help in solving issues.
AI-driven solutions, specifically AIOps platforms, can help reduce that noise while speeding up the resolution of common IT issues.
At a glance: 8 best AIOps platforms
Disclaimer: Feature descriptions and capability claims are based on publicly available product documentation current as of June 2026. These descriptions may not reflect the latest product updates or capabilities. For the most current information, consult each vendor's official documentation.
Platform | Key Feature | Best Fit For |
ServiceNow | Predictive AIOps and cloud-native observability | Large enterprises already using the ServiceNow ecosystem |
PagerDuty | Automated incident response and orchestration | Teams requiring rapid incident coordination and resolution |
ScienceLogic | Automated asset discovery and relationship mapping | Complex hybrid environments and teams wanting to consolidate monitoring |
BigPanda | "Open Box" ML for transparent event correlation | Organizations with high alert volumes that require better auditability |
Splunk ITSI | Massive telemetry ingestion and custom glass tables | Data-rich environments already integrated with Splunk |
BMC Helix | Service modeling and business impact analysis | Mature teams with established service-mapping workflows |
IBM Cloud Pak | Topology-based grouping and NLP incident analysis | Enterprises already using existing IBM infrastructure |
LogicMonitor | Extensive integration capabilities and capacity forecasting | Hybrid environments looking for broader, unified observability |
How to evaluate AIOps platforms for enterprise operations
Look past feature counts when evaluating AIOps platforms. The better question is whether a platform can handle your data the way your environment actually works. Here's a checklist to help you decide which AIOps platform is the best fit for your enterprise:
- Telemetry coverage: The platform should capture logs, metrics, and digital audit trails from various cloud environments and legacy systems.
- Normalization: Look for the ability to aggregate and normalize data across your ecosystem.
- Event correlation: Prioritize advanced analytics capabilities that can group related alerts into a single actionable view.
- RCA and topology: Effective solutions use topology to illustrate how your cloud-native services depend on one another.
- Automation depth: The platform should support automation capabilities that help reduce manual intervention on recurring issues.
- ITSM integration: Make sure the platform can sync with your current service management tools to keep incident workflows automated and consistent.
- Governance: Check for RBAC, audit logging, and data residency to maximize data security.
- Time to value: Validate setup requirements, connector availability, and the pilot scope to understand how quickly the platform can deliver reliable incident context and workflow impact.
What is an AIOps platform?
AIOps platforms combine big data, machine learning, and automation to improve IT operations. By ingesting data from disparate sources, AIOps platforms detect anomalies, correlate events, and surface insights that help teams build more proactive operations strategies.
One of the defining differences is scope. While monitoring tracks your systems and observability helps explain why a problem occurred, AIOps can help teams act on those signals, not just surface them.
Still, AIOps isn't a fix for poor data integrity. These platforms tend to be only as effective as the information they process. Reliable outcomes in hybrid cloud environments depend on clear governance and data consistency.
What can AIOps tools do?
AIOps tools can vary widely in their capabilities. To find the right fit, evaluate how platforms handle your data in a live environment, not just in a vendor demo.
Below are four core AIOps capabilities to test, along with how their inputs/outputs are often structured:
- Data consolidation: Can provide a central hub to aggregate telemetry across your IT environment.
- Input: Infrastructure metrics (CPU, memory), application logs, transaction data, and network traffic.
- Output: Normalized data model that provides a single view of performance across all IT elements.
- Monitoring and alerting: Can use algorithms to identify repeat behavior and filter low-priority notifications before they reach your busy teams.
- Input: Real-time performance metrics and log data.
- Output: Filtered, high-priority alerts that correlate related events into a single view.
- Root cause analysis (RCA): Can map dependencies across your technology stack to support faster identification of likely failure points.
- Input: Topology mapping data, service dependency graphs, and historical data surrounding past incidents and system changes.
- Output: A visualized map highlighting failure points ranked by severity, giving operations teams a clearer starting point for investigation.
- Incident automation: Can use predefined playbooks to speed up incident management while triggering remediation steps with minimal human intervention.
- Input: Triggered incident events combined with integrated data from service management tools and internal knowledge bases.
- Output: Enriched support tickets that contain all relevant diagnostic data and can execute self-healing automation.
1. ServiceNow
ServiceNow is an enterprise-grade cloud platform that works as the primary system of record for your IT environment, letting you manage the incident lifecycle in a single native environment.
While many platforms focus on detecting and correlating events, ServiceNow can help teams detect, correlate, and resolve in a single system. Because its ITSM layer is native rather than bolted on, it can help reduce the tool-switching that typically slows resolution.
Key features
- Event management: Uses machine learning to group related signals, reducing alert fatigue and prioritizing high-impact incidents.
- Anomaly detection: Measures performance metrics and logs in real time to catch issues before they grow.
- Predictive intelligence: Uses historical data to identify patterns in IT systems and surfaces insights into trends.
- Now Assist: Incorporates a generative AI tool to help speed up incident routing.
Best fit
ServiceNow is a strong fit for larger organizations and teams already using ServiceNow workflows that want AIOps insights and ITSM resolution in a single platform.
2. PagerDuty
PagerDuty is an incident orchestration platform that is designed to coordinate handoffs between system alerts and the teams responsible for resolution.
PagerDuty focuses on the "who" and "how" of team responses, helping operations teams move from detection to engagement with less friction.
Key features
- Event orchestration: Groups signals and adds additional context to incidents.
- Automated workflows: Helps manage complex escalation paths and routing across technology stacks, reducing the risk of prolonged downtime.
- Integration ecosystem: Connects to over 700 DevOps and service management tools through API connections.
Best fit
PagerDuty is a good fit for teams looking to improve their mean time to engagement and maintain 24/7 availability. It's also built for organizations that want clean escalation paths and a clean user experience.
Keep in mind that since PagerDuty works as a response layer rather than a telemetry source, it's designed to sit on top of your existing monitoring stack, not replace it.
3. ScienceLogic
ScienceLogic is a visibility-first platform that provides a unified view of your technology stack. One of the platform's greatest benefits is asset discovery capabilities, mapping how IT infrastructure interacts across on-premises and cloud environments.
Key features
- Discovery and mapping: Identifies assets and their relationships to help you visualize your ecosystem.
- Contextual RCA: Helps identify the root cause of IT issues by using relationship mapping to see how a single point of failure can create future problems.
- Maintenance automation: Provides tools to speed up routine maintenance and repair tasks without relying solely on human intervention.
Best fit
ScienceLogic is a strong fit for organizations with hybrid cloud environments that want to consolidate their resource monitoring and get an end-to-end view of their IT service health.
4. BigPanda
BigPanda is an AI-driven event correlation platform built around "Open Box Machine Learning," an approach that provides visibility into how the platform makes correlation decisions.
Having this transparency helps build trust with operations teams, letting them know where multiple events link together — and keeping correlation logic auditable and adjustable over time.
Key features
- Change intelligence: Connects incidents to recent system changes, helping to identify if a recent deployment caused an issue.
- AI incident assistant: Uses generative AI to automate major incident coordination, summaries, and post-mortems across collaboration tools like Slack.
- Knowledge graphs: Unifies tribal knowledge from tickets and AI threads with structured data to provide deeper context into events.
Best fit
BigPanda is a good fit for organizations with large alert volumes and complex, distributed IT systems. It's designed for businesses that want to reduce alert fatigue while maintaining strict governance and explainability across their systems.
To get the most value from its topology-based correlation, upstream data and service mappings should be as accurate as possible.
5. Splunk IT Service Intelligence
Splunk ITSI is an analytics platform designed to support ingestion and visualization of telemetry data from multiple sources. It applies machine learning across IT environments, helping turn raw data into real-time operations insights.
Key features
- Glass tables: Customizable visual dashboards that map key business metrics and service health scores onto a single drawing canvas.
- Event correlation: Groups related events into "Episodes" using predictive analytics and machine learning.
- Incident summarization: Uses generative AI to produce concise, natural-language summaries of complex incidents.
Best fit
Splunk ITSI is ideal for large organizations already using the Splunk ecosystem for security or observability. It's also a strong option for teams that want deep analytics and detailed data governance, including audit trails and role-based access.
6. BMC Helix Operations Management
BMC Helix is a service-aware platform that maps IT infrastructure to the business functions it supports. One of the platform's strengths is its service modeling. These models help surface which business services are likely to be impacted before a ticket is submitted.
Key features
- Service-centric RCA: Uses topology mapping to link technical failures to business impact, helping to speed up the triage process.
- Probable-cause analysis: Analyzes service dependencies and suggests the most likely source of IT issues to reduce mean time to repair.
- Governance and compliance: Provides detailed audit logging and role-based permission awareness to help keep data secure.
Best fit
BMC Helix is ideal for mature organizations that already maintain service models or have the operational maturity to build and sustain them.
7. IBM Cloud Pak for AIOps
IBM Cloud Pak for AIOps is an enterprise-scale solution designed to help teams predict and reduce the likelihood of IT outages by using AI to improve observability across hybrid cloud environments.
By correlating logs and events from isolated sources, IBM Cloud Pak helps operations teams move from reactive firefighting to more proactive operational strategies.
Key features
- Topology-based grouping: Groups related events based on system relationships.
- Dynamic thresholds: Learns from baseline system behavior and adjusts alert thresholds over time, helping teams catch potential issues earlier.
- NLP incident analysis: Uses natural language processing (NLP) to analyze and summarize incidents, giving teams the context they need to speed up resolution.
Best fit
IBM Cloud Pak for AIOps is a strong fit for large enterprises with existing IBM infrastructure and dedicated AIOps engineering resources.
By correlating logs and events from isolated sources, IBM Cloud Pak helps operations teams move from reactive firefighting to more proactive operational strategies.
8. LogicMonitor
LogicMonitor is a hybrid observability platform that can consolidate data visibility across on-premises, cloud, and containerized environments.
One of the platform's primary strengths is integration breadth. With thousands of out-of-the-box plugins, it can ingest telemetry from a wide range of devices or services across your stack.
Key features
- Active discovery: Sources and monitors new IT assets to help speed up initial setups and ongoing maintenance.
- Forecasting and capacity planning: Uses historical data to predict when resources are running low, helping teams plan ahead and avoid performance bottlenecks.
- Edwin AI: Features a conversational AI agent designed to summarize incidents and surface business insights.
Best fit
LogicMonitor can be a good fit for organizations that need to monitor complex hybrid environments from a single location. While the platform helps with automated discovery, the volume of data it collects may require initial tuning of your alert thresholds to reduce false positives.
Complete your AIOps stack with Moveworks
As AI has expanded across enterprise operations, a new category of tools has emerged — agentic AI systems that don't just surface insights, but take action across connected systems.
Moveworks AI Assistant is built on that foundation, designed to serve as a complementary layer to your AIOps stack by focusing on the human-facing execution of workflows your backend tools identify.
Where AIOps platforms focus on telemetry, logs, and incident detection, Moveworks can help bridge the gap between detection and resolution. AI agents within the platform are designed to reason through complex requests and take action across connected systems — going beyond static triggers to support more flexible, cross-system orchestration.
The interface through which employees experience this is Moveworks AI Assistant. This conversational AI interface gives employees a single place to search for information and take action across enterprise systems using natural language.
On the back end, Agent Studio provides the governance and extensibility layer that connects those natural-language requests to your existing technical stack — with role-based controls and policy enforcement built in.
This enables AI agents to take action across connected systems within defined boundaries, whether that means updating a ticket in your ITSM tool or provisioning software through an API.
Optimize your AIOps rollout with agentic automation
Finding the right AIOps platform is an important step toward broader visibility and operational resilience.
But without a clear path to action, the benefits these solutions can bring may start to diminish. Moveworks AI Assistant and Agent Studio can help connect issue detection to resolution.
By serving as an agentic front door to work, Moveworks can help you unify search and action across your existing enterprise systems. Your team can surface the right information and execute the right workflows from a single conversational interface — across the enterprise systems you already use.
For many teams, this approach can contribute to faster resolution cycles, reduced manual handoffs, and more measurable ROI as your AIOps stack matures.
Want to start improving your AIOps rollout with intelligent agentic automation? Schedule a free demo of Moveworks today.
Frequently Asked Questions
An AIOps platform applies machine learning and analytics to operations data such as metrics, events, logs, and traces. It can help IT teams reduce alert noise, detect anomalies, and correlate related signals into incidents that are easier to investigate. Many platforms also support workflow automation, such as ticket enrichment or triggering remediation playbooks, depending on integrations and governance controls in place.
AIOps refers to using AI techniques to support IT operations workflows, especially in environments generating high volumes of telemetry and alerts. In practice, it often includes anomaly detection, event correlation, and analytics that can help teams move from reactive firefighting to proactive operations. Outcomes vary based on data quality, integrations, and how well the platform fits the operating model.
Most enterprise buyers evaluate telemetry ingestion and normalization, event correlation quality, and root cause analysis (RCA) capabilities supported by topology or service mapping. IT teams also tend to look for automation depth (ticket enrichment, routing, remediation triggers), ITSM integration, and governance features such as RBAC, audit logs, and data handling controls. A useful evaluation approach is to test each feature against real incident scenarios rather than marketing demos.
Monitoring and observability tools primarily collect and visualize telemetry, while AIOps typically analyzes that telemetry to detect anomalies, correlate events, and support incident investigation and response. ITSM systems manage service workflows like incidents, requests, and changes, and many AIOps platforms integrate with ITSM to turn insights into actions. In many enterprises, these categories overlap in tooling, so the practical differences are what data you ingest, which decisions the system supports, and where automated actions occur.
AIOps platforms commonly ingest telemetry sources such as metrics, events, logs, and traces, and may also incorporate configuration and dependency data for better correlation and RCA. Integrations matter because they influence both coverage and time-to-value: without reliable connectors into monitoring tools, cloud platforms, and ITSM, correlation outputs may lack context or fail to trigger downstream workflows. For enterprise rollouts, validating ingestion breadth, data normalization, and ITSM case enrichment in a pilot before scaling tends to reduce surprises.
