How IT teams leverage AIOps capabilities to improve efficiency and scale

If Artificial Intelligence is the ultimate multi-tool for IT operations (as discussed in our first article), then DevOps, Network Ops, Site Reliability Engineers (SREs), and SecOps are the teams using it. How each team uses AIOps’ capabilities will improve interconnectivity across an organization’s digital landscape, accelerate the production of high-priority business objectives, and reduce downtime to pave the way for a smoother developer and user experience.

Understanding the teams driving IT operations success

Before we map capabilities to teams, let’s establish some broad team definitions as they may currently exist within IT operations:

• DevOps: Ensure smooth collaboration between development and operations. 

Priorities include automation, issue detection, and optimizing workflows to speed up software development and delivery.

• IT operations: Manage and maintain the organization’s IT infrastructure. 

Priorities include improving operational efficiency, reducing downtime, and improving system reliability.

• Network operations: Manage and maintain the operation’s network infrastructure. 

Priorities include identifying bottlenecks and predicting potential network issues.

• SRE: As an operational group, SREs own the back-end infrastructure responsible for the customer experience and consult with developer teams to ensure the infrastructure can support applications. 

Priorities include avoiding downtime among revenue-critical systems, preventing bandwidth outages, and fixing configuration errors. 

• Security operations: Protects the organization’s systems and data from security threats. 

Priorities include security log analysis and response, as well as identifying anomalies or vulnerabilities.

Establishing a strong foundation: Key AIOps capabilities by team

AIOps uses artificial intelligence, machine learning, and consolidated operational platforms to automate repetitive or mundane tasks and streamline cross-team communications. An AIOps deployment is the scaffolding IT operations use to build evolving workflows so the teams can be more proactive, innovative, and able to accelerate the delivery of high-priority projects. That’s why we are seeing more AIOps success stories about how AIOps can liberate 40% of your engineering time through the automation of labor-intensive analysis or how Managed Service Providers (MSPs) are implementing AIOps’ intelligent alerting capabilities to dramatically reduce downtime. 

So let’s dig into which three AIOps capabilities each team may leverage first:

DevOps

• Enhanced efficiency: Automating repetitive and manual tasks frees up time to focus on higher-value initiatives, increasing efficiency and productivity across the entire team.
• Faster mean time to resolution (MTTR): Streamlining incident management processes ensures faster issue identification, analysis, “next steps,” cross-team communications, and, ultimately, issue resolution. With automation doing the heavy lifting, these steps can happen outside of work hours. This 24/7 approach reduces the time to resolution, minimizing any impact on operations.
• Scalability and adaptability: AI and machine learning’s self-learning properties are made to handle complex and rapidly evolving technology stacks in dynamic environments. 

Watch the 3-minute video below for more on how DevOps can use AIOps for faster issue resolution through integration with open-source provisioning and configuration management tools.

IT Operations

• Incident management: AIOps streamline incident identification and root cause analysis and escalate incidents to the right teams and people who can pinpoint the source of an issue and quickly fix it. Post-incident reviews are used to build resilience in systems to prevent future occurrences of similar incidents. Faster resolution reduces MTTR and operational impact.
• Scalability and adaptability: IT infrastructure has to adapt to business needs. AIOps systems handle the complexity of evolving modern stacks and dynamic environments, including hybrid and multi-cloud architectures. Faster scaling sets ITOps up for success in that they can effectively manage and monitor expanding IT landscapes at any stage of growth.
• Resource and cost optimization: Capacity planning and the automation of tasks lets ITOps teams allocate resources more efficiently, freeing up budget and personnel for new endeavors or headcount strategies.

Network Ops

• Streamlined troubleshooting: Automated root cause analysis capabilities quickly pinpoint the root causes of network issues, accelerating troubleshooting and improving uptime. 
• Capacity planning: Historical and real-time data analysis on network use patterns, forecasted future demands, and resource allocation enables the team to reassign assets as needed to prevent network congestion and keep operations consistent while supporting business growth.
• Network security enhancement: Leveraging AI-driven algorithms that analyze network traffic, detect anomalies, and identify potential security threats enables Network Ops teams to take proactive measures ahead of a breach.

SRE

• Elasticity: As SRE teams manage complex and dynamic environments, including cloud-based systems and microservices architectures, AIOps provides the ability to scale and adapt to changing demands. AIOps ensures the SRE team can effectively monitor, manage, and optimize the system’s performance as it grows and evolves.
• Continuous optimization: AIOps analyzes data from various sources, including logs, metrics, and events, then identifies optimization opportunities that SRE teams can enact. Leveraging AI insights to make data-driven decisions, implement proactive measures, and continuously refine their infrastructure to achieve greater reliability.
• Collaboration and knowledge sharing: By providing a centralized platform for data collection, analysis, and visualization, AIOps facilitates communication and sharing of information so associated teams (such as developers) can align their efforts towards common goals, leading to improved teamwork and faster problem-solving.

SecOps

• Advanced threat detection: AIOps enhances threat detection capabilities by analyzing vast amounts of security-related data from various sources, such as logs, network traffic, and user behavior. AI-driven algorithms can identify patterns, anomalies, and potential security threats in real time, enabling SecOps teams to respond promptly to security incidents, minimizing damage caused by cyber threats.
• Threat intelligence integration: AIOps integrates with threat intelligence feeds and external security sources to enhance the effectiveness of security operations. By leveraging external threat intelligence data, AIOps enriches its analysis and detection capabilities, allowing SecOps teams to stay updated on the latest threats and attack vectors. This integration strengthens the overall security posture and enables proactive defense against emerging threats.
• Compliance and regulatory requirements: AIOps automate compliance monitoring and reporting processes and then compare them against predefined standards and regulations to evolve the automation and compliance process so teams consistently meet compliance and regulatory requirements.

Integrating AIOps for teams with existing tools

Seamless integration for unified operations

One of the standout advantages of AIOps is its ability to integrate with existing IT tools, providing a unified platform for monitoring, automation, and insights. Whether you’re leveraging monitoring tools like LogicMonitor, managing hybrid or multi-cloud environments, or maintaining CI/CD pipelines, AIOps can enhance and extend their functionality rather than replace them.

Compatibility with monitoring tools

AIOps platforms, such as LogicMonitor, act as a central hub, aggregating data from multiple monitoring tools to provide a unified view of IT operations. For example, integrating LogicMonitor with AIOps capabilities allows teams to consolidate alerts, correlate events, and automate responses—all from a single dashboard. This integration reduces manual intervention and provides actionable insights in real-time.

Enhancing cloud platforms

AIOps is designed to operate seamlessly in hybrid and multi-cloud environments. By analyzing data from cloud-native tools, AIOps systems provide predictive analytics, helping IT teams optimize workloads, prevent resource exhaustion, and identify anomalies before they escalate into problems.

Streamlining CI/CD pipelines

For DevOps teams, AIOps tools integrate with CI/CD platforms to enable continuous monitoring and intelligent automation throughout the development lifecycle. This ensures faster feedback loops, reduces downtime caused by deployment errors, and optimizes application performance.

Addressing legacy system concerns

One common concern when adopting AIOps is its compatibility with legacy systems. AIOps platforms are built with integration in mind, offering APIs and connectors that bridge the gap between older systems and modern tools. By applying machine learning to data generated by legacy tools, AIOps can derive valuable insights while extending the life of existing systems.

Laying the groundwork for success

To fully unlock the transformative potential of AIOps, organizations need to establish a strong foundation. These best practices ensure that teams can effectively leverage AIOps capabilities while minimizing disruptions and maximizing impact.

1. Prioritize data quality and accessibility

AIOps thrives on accurate and comprehensive data. Ensure all data sources—whether from legacy systems, monitoring tools, or cloud platforms—are clean, consistent, and consolidated. By breaking down data silos and standardizing formats, teams can enable AIOps to deliver actionable insights with precision.

2. Foster cross-team collaboration

AIOps works best when IT teams such as DevOps, Network Ops, and SREs collaborate seamlessly. Establish shared goals and encourage open communication to align team efforts. Unified dashboards, like those offered by LogicMonitor, help bridge gaps and provide everyone with a clear view of the operational landscape.

3. Start with targeted use cases

Rather than implementing AIOps broadly, begin with specific high-impact applications. Use cases such as automated incident management or anomaly detection are excellent starting points for demonstrating value and gaining stakeholder buy-in.

4. Balance automation with human oversight

While AIOps excels at automating repetitive tasks, human judgment remains critical for nuanced decision-making. Pair automated workflows with manual checks for complex scenarios to ensure both speed and accuracy in IT operations.

5. Commit to continuous improvement

AIOps systems evolve over time. Regularly monitor performance metrics, gather team feedback, and refine algorithms to adapt to changing environments. This iterative approach ensures long-term success and sustained benefits.

AIOps Use Cases

Here are some of the key use cases of AIOps in IT operations: 

1. Identifying problems based on anomalies or deviations from normal behavior

AIOps enhances IT systems by using machine learning to detect anomalies and potential issues, unlike traditional tools that rely on manual configuration and threshold alerts. It analyzes data in real-time, flags deviations from normal behavior, and allows IT teams to address problems before they escalate.

2. Forecasting the value of a certain metric to prevent outages or downtime

AIOps forecasts crucial metrics like server capacity and network bandwidth, alerting IT teams before they reach critical levels. This proactive approach helps prevent outages and disruptions. By using machine learning algorithms, AIOps monitors data trends to predict threshold breaches, enabling preemptive actions to mitigate issues.

3. Improving incident response and resolution times

AIOps substantially improves incident response and resolution times by automatically correlating events from various sources and providing intelligent insights for root cause analysis. Machine learning algorithms effectively process large volumes of data from logs, alerts, and metrics to identify the root cause of incidents. This methodology not only expedites incident response but also reduces the mean time to resolution (MTTR), thereby minimizing the impact on business operations.

4. Enhancing IT operations through automation

AIOps presents substantial benefits by automating routine tasks and processes within IT operations, allowing IT teams to focus on higher-value activities such as strategic planning and problem-solving. This automation ranges from fundamental tasks like ticket routing and categorization to more complex processes such as incident remediation based on predefined rules. Consequently, it enhances efficiency, reduces the risk of human error, and streamlines workflows.

Take your IT operations to the next level

AIOps give teams the tools they need to transform from reactive to proactive. The combination of artificial intelligence and machine learning accelerates issue mitigation, breaks through work silos, improves systems security and scalability, increases productivity, reduces error risk and optimizes resources and costs. Having an AI-empowered IT operation means an organization’s infrastructure is instantly ready to handle roadblocks for a smoother developer and user experience.

LogicMonitor’s AIOps platform empowers businesses to transition from reactive troubleshooting to proactive, intelligent operations. With AI and machine learning capabilities, LogicMonitor provides meaningful alerts, illuminates patterns, and enables foresight and automation. Spend less time resolving issues and more time driving innovation.

LogicMonitor is proud to power the journey to AIOps by offering these free educational resources:

What is AIOps and How is it Changing IT Operations? 

Simplify Troubleshooting with AIOps

Monitoring and Alerting Best Practices Guide 

Sensirion Goes from 8 Monitoring Tools to Just One

Comprehensive AIOps for monitoring 

Unlocking the Path to Automation with LogicMonitor