What is AIOps (artificial intelligence for IT operations)?

How does AIOps work?

AIOps uses advanced analytics to automate and optimize IT operations processes. AIOps typically works by following these steps:

1. Data collection. AIOps platforms collect information from a variety of sources, including application logs, event data, configuration data, incidents, performance metrics and network traffic. This data can be both structured, such as databases, or unstructured, such as social media posts and documents.
2. Data analysis. The gathered data is analyzed using different types of ML algorithms such as anomaly detection, pattern detection and predictive analytics to find abnormalities that might require the attention of IT staff. This step ensures real issues are separated from noise or false alarms.
3. Inference and root cause analysis. AIOps carries out root cause analysis to assist in locating the origin of problems. IT operations teams can attempt to prevent disruptions from recurring by looking into the root causes of current issues.
4. Collaboration. Once the root cause analysis is complete, AIOps notifies the appropriate teams and individuals, providing them with relevant information and promoting efficient collaboration despite any potential geographical distance among them. In addition, this partnership helps to preserve event data that could be essential for identifying future issues of a similar nature.
5. Automated remediation. AIOps can remediate issues automatically, significantly reducing manual intervention and speeding up incident response. These can be automated responses, such as resource scaling, restarting a service or executing predefined scripts to address problems.

Getting started with AIOps

Setting up AIOps in an organization involves the following strategic steps to ensure the successful integration of AI technologies into IT operations:

1. Assessing current infrastructure. Organizations should begin by evaluating their existing IT infrastructure and operations. They must identify the tools, processes and data sources currently in use to understand the gaps and areas that can benefit from AIOps.
2. Defining objectives. Businesses must clearly outline the goals they want to achieve with AIOps. This could include improving incident response times, enhancing system performance or reducing operational costs. Having specific objectives helps guide the execution strategy.
3. Integrating data. This step should identify all relevant data sources across the organization's IT environment, including logs, metrics and events. The organization must craft a plan to integrate this data into a centralized platform. This integration plan might involve using big data technologies to create meaningful insights and business intelligence dashboards.
4. Selecting AIOps tools. Organizations should choose the right AIOps tools that align with their objectives and infrastructure. For example, the tools should offer built-in capabilities including ML, anomaly detection and automated incident management. The tools should also integrate seamlessly with existing systems.
5. Setting up a pilot program. A pilot program to test the execution of AIOps should now be set up on a smaller scale. This approach enables businesses to evaluate the effectiveness of the tools and processes before a full-scale rollout of AIOps. User feedback is also typically gathered at this stage to make any necessary adjustments.
6. Training and change management. The IT staff must be educated on AIOps and its benefits. Companies should address any concerns employees might have regarding their job roles and emphasize that AIOps are designed to enhance human intervention and capabilities rather than replace them. Effective change management will also help in gaining buy-in from the team and stakeholders.
7. Continuous monitoring. Once the execution of AIOps is complete, IT teams should continuously monitor the performance of AIOps tools and processes to help refine and optimize their AIOps strategy. They can achieve this by using the insights gained during the previous steps.

Key AIOps use cases

AIOps is generally used in organizations that also use DevOps or cloud computing as well as in large, complex enterprises. AIOps aids teams that use a DevOps model by giving them additional insight into their IT environment and high volumes of data. This gives the operations teams more visibility into changes in production.

Some common use cases for AIOps include the following:

• Removing hybrid cloud risks. Hybrid cloud platforms have complex architectures and interactions between various components, which can sometimes introduce risks, such as loss of efficiency and accuracy in operations. AIOps can remove these risks by breaking down the operational constraints of the hybrid cloud environment.
• Process automation. Being able to automate processes, recognize problems in an IT environment earlier and aid in smoothing communications between teams can help large companies with extensive or complicated IT environments.
• Anomaly detection. AIOps uses AI to scan large amounts of historical data and categorize patterns more quickly than human operators, making it possible to identify problems and their underlying causes with speed and accuracy.
• Performance monitoring. It can be challenging to determine which underlying resources are supporting specific modern applications because they're frequently divided by numerous abstraction layers. By serving as a monitoring tool for storage, virtualization, cloud infrastructure and reporting on parameters, such as consumption, availability and response times, AIOps can bridge this gap. In addition, AIOps takes advantage of event correlation capabilities to combine and aggregate information, improving end users' access to it.
• Understanding customer needs. AIOps helps businesses better understand the demands of their clients by gathering data from client interactions in real time and using it to deliver an improved customer experience. Businesses can also modify their products in response to client input as well as raise user experience and customer satisfaction levels over time.
• Threat detection. AIOps can help identify security risks, anomalies and patterns of malicious activity. By analyzing log data, network traffic and security events in real time, AIOps can quickly respond to incidents and reduce threats and intrusions.
• Capacity management. AIOps can help companies assess usage trends and predict resource requirements to ensure optimal performance and reduced costs.
• DevOps adoption. AIOps simplifies DevOps adoption by automating incident management and delivering data-driven insights. By reducing alert fatigue and optimizing resource allocation, AIOps enables DevOps teams to concentrate more on delivering value, fostering a more agile and responsive development environment.

AIOps technologies

AIOps uses a conglomeration of various AI strategies, including the following:

• Machine learning. ML uses algorithms to enable computer systems to learn from large data sets and adapt to new information. It can include a variety of techniques such as supervised learning, unsupervised learning, reinforcement learning and deep learning. In AIOps, ML techniques are typically used for anomaly detection, root cause analysis, event correlation and predictive analysis.
• Analytics. AIOps data comes from log files, metrics and monitoring tools, help desk ticketing systems, and other sources. Analytics techniques can interpret the raw data coming from these sources to create new data and metadata. Analytics reduces noise -- unneeded or spurious data -- and spots trends and patterns that enable the tools to identify and isolate problems, predict capacity demand, and handle other events.
• Algorithms. Analytics also requires algorithms to codify the organization's IT expertise, business policies and goals. Algorithms enable an AIOps platform to deliver the most desirable actions or outcomes. They're how IT personnel prioritize security-related events and teach application performance decisions to the platform. The algorithms form the foundation for ML, wherein the platform establishes a baseline of normal behaviors and activities and can then evolve or create new algorithms as data from the environment changes over time.
• Automation. Automation is a key underlying technology to make AIOps tools take action. Automated functions occur when triggered by the outcomes of analytics and machine learning. For example, a tool's predictive analytics and ML determine that an application needs more storage, then it initiates an automated process to execute additional storage in increments consistent with algorithmic rules.
• Visualization. Visualization tools deliver human-readable dashboards, reports, graphics and other output so that users can follow changes and events in the environment. With these visualizations, humans can act on the information that requires decision-making capabilities beyond those of the AIOps software.

AIOps benefits and drawbacks

AIOps comes with the following advantages and disadvantages:

Benefits of AIOps

• Time savings. When properly applied and trained, an AIOps platform reduces the time IT staff spends on mundane and routine alerts. IT staff teaches AIOps platforms, which then evolve with the help of algorithms and machine learning, recycling knowledge gained over time to further improve the software's behavior and effectiveness.
• Automated and continuous monitoring. AIOps tools also perform continuous monitoring without the need for sleep. Humans in the IT department can focus on serious, complex issues, and initiatives that increase business performance and stability.
• Digital transformation. AIOps has the potential to decrease the occurrence of IT incidents and shorten the mean time to repair (MTTR). It can also facilitate digital transformation by providing IT organizations with an IT infrastructure that's more agile, flexible and secure.
• Enhanced visibility. AIOps tools can provide IT teams with greater visibility into their infrastructure and apps, enabling them to proactively identify and address potential issues and outages before they become real problems.
• Expense reduction. By automating and optimizing IT operations and processes, AIOps can help organizations minimize customer service expenses.
• Data correlation. AIOps software can observe causal relationships over multiple systems, services and resources, clustering and correlating disparate data sources. Those analytics and ML capabilities enable software to perform powerful root cause analysis, which accelerates the troubleshooting and remediation of difficult and unusual issues.
• Improved collaboration. AIOps can improve collaboration and workflow activities between IT groups and other business units. With tailored reports and dashboards, teams can understand their tasks and requirements quickly and then interface with others.
• Proactive management. AIOps provides organizations with a proactive approach rather than a reactive one. By predicting issues before they escalate, teams can resolve problems quickly, minimize downtime and improve service reliability.

Drawbacks of AIOps

• Data quality issues. Although the underlying technologies for AIOps are relatively mature, it's still an early field in terms of combining the technologies for practical use. AIOps is only as good as the data it receives and the algorithms it's taught. Therefore, organizations need to ensure their data is up to date and accurate.
• Deployment and integration challenges. The amount of time and effort needed to execute, maintain and manage an AIOps platform can be substantial. The diversity of available data sources as well as proper data storage, protection and retention are all important factors in AIOps results.
• Overreliance on automation. Overreliance on automation can create a single point of failure and reduce an IT team's ability to adapt to new situations.
• Bias and ethical concerns. When adopting AI technologies, there's always a risk of bias and ethical difficulties, since they can perpetuate and even exacerbate existing biases in data sets.
• Long-term maintenance. AIOps tools require long-term maintenance, including regular updates, configuration adjustments and monitoring to ensure they continue to function effectively as environments and technologies evolve. All of this can demand significant resources and ongoing commitment from IT teams.

What capabilities should an AIOps platform provide?

An effective AIOps platform should offer a range of capabilities to enhance IT operations and support DevOps practices.

The following are essential features to look for in an AIOps platform:

• Data aggregation and integration. An AIOps platform should be able to retrieve and aggregate data from multiple sources, such as metrics, logs and other events happening in the IT environment. This capability provides comprehensive visibility into the system's performance and health.
• Advanced analytics and ML. AIOps platforms should be able to integrate ML algorithms to help with anomaly detection through pattern identification and analysis of large data sets.
• Incident management and automation. To reduce manual effort as well as accelerate the mean time to detect and MTTR for incidents, AIOps platforms should be equipped to provide automated incident management, including root cause analysis and alert prioritization.
• Customizable dashboards and reporting. An AIOps platform should be able to create customized dashboards and reports for tracking key performance indicators and metrics that are relevant to different business units or services.
• Unified observability. A comprehensive AIOps platform should deliver a unified view of the entire IT landscape by integrating observability tools. These tools enable teams to monitor applications, infrastructure and network performance all from a single platform.
• Collaboration and workflow management. AIOps platforms should provide tools for workflow management and sharing of automation libraries to promote collaboration between IT teams.
• Security and compliance monitoring. AIOps tools should be able to incorporate security event prioritization and compliance monitoring, which is crucial when trying to identify and respond to security threats in real-time.

AIOps vendors

To demonstrate value and mitigate risk from AIOps deployment, organizations should introduce the technology in small, carefully orchestrated phases. They should decide on the appropriate hosting model for the tool, such as onsite or as a service. IT staff must understand and then train the system to suit the organization's needs and, to do so, must have ample data from the systems under its watch.

AIOps is an emerging area, but according to "The Forrester Wave Process-centric AI for IT operations (AIOps)" report from 2023 and Gartner Peer Insights, there's a growing stable of product offerings for businesses to review and evaluate, including the following:

• Aisera.
• BigPanda.
• BMC Software TrueSight.
• Datadog.
• Dell Technologies Moogsoft.
• Dynatrace.
• Freshworks.
• HCL Software Dryice.
• IBM AIOps Insights.
• New Relic.
• PagerDuty.
• ServiceNow IT Operations Management.
• SolarWinds.
• Splunk IT Service Intelligence.
• Sumo Logic.

Future of AIOps

The future of AIOps looks promising. According to a report from The Insight Partners, the global AIOps platform market is predicted to increase from $4.9 billion in 2023 to $46.2 billion by 2031.

AIOps is expected to assist enterprises in enhancing their IT operations by minimizing noise, facilitating collaboration, offering full visibility and boosting IT service management. The AIOps technology has the potential to facilitate digital transformation by providing enterprises with a more agile, flexible and secure IT infrastructure. In addition, it's expected to mature and gain market acceptance, with enterprises incorporating it into their DevOps initiatives to automate infrastructure operations.

The following are some key trends and predictions for the future of AIOps:

• Increased AI and ML adoption. As organizations continue to generate and collect vast amounts of data, AIOps platforms are increasingly using advanced AI and ML techniques to provide actionable insights and automate complex processes.
• Growing reliance on predictive analytics. There has been a growing focus in AIOps to address IT issues before they arise by integrating advanced predictive analytics, which enables proactive management of IT environments. There's a notable trend among organizations using AIOps to adopt automated machine learning, or AutoML, and neural networks for probabilistic forecasting. For instance, they can predict an 85% chance of running out of storage in the next two days or an 87% likelihood of an e-commerce shopping cart being unavailable within 24 hours.
• Explainable AI. As AIOps becomes more widespread, the demand for explainable AI models is increasing. Organizations are looking for greater transparency and interpretability to understand how AIOps systems arrive at their decisions. Explainable AI techniques can clarify the factors and patterns influencing AIOps recommendations, enabling teams to confidently use AI-driven real-time insights while maintaining oversight and accountability in their operations.
• Autonomous remediation. Along with automatic issue diagnosis, AIOps platforms are predicted to automatically trigger remediation actions based on predefined rules or ML algorithms. This capability will enable them to identify issues and respond proactively, reducing the manual effort needed for incident resolution. As a result, organizations can anticipate quicker problem resolution and enhanced service availability.

Interest in AIOps and observability is growing exponentially in IT, but it doesn't come without its adoption challenges. Learn how to overcome AIOps adoption barriers and get visibility into problem areas for enhanced operations.