Definition

AIOps (artificial intelligence for IT operations)

What is AIOps (artificial intelligence for IT operations)?

Artificial intelligence for IT operations (AIOps) is an umbrella term for the use of big data analytics, machine learning (ML) and other AI technologies to automate the identification and resolution of common IT issues. The systems, services and applications in a large enterprise -- especially with the advances in distributed architectures such as containers, microservices and multi-cloud environments -- produce immense volumes of log and performance data that can impede an IT team's ability to identify and resolve incidents. AIOps uses this data to monitor assets and gain visibility into dependencies within and outside of IT systems.

An AIOps platform should provide enterprises with the ability to do the following:

  1. Automate routine practices. Routine practices include user requests as well as noncritical IT system alerts. For example, AIOps can enable a help desk system to process and fulfill a user request to provision a resource automatically. AIOps platforms can also evaluate an alert and determine that it doesn't require action because the relevant metrics and supporting data available are within normal parameters.
  2. Recognize serious issues faster and with greater accuracy than humans. IT professionals might address a known malware event on a noncritical system, but ignore an unusual download or process starting on a critical server because they aren't watching for this threat. AIOps addresses this scenario differently: prioritizing the event on the critical system as a possible attack or infection because the behavior is out of the norm, and deprioritizing the known malware event by running an antimalware function.
  3. Streamline interactions between data center groups and teams. AIOps provides each functional IT group with relevant data and perspectives. Without AI-enabled operations, such as monitoring, automation and service desk, teams must share, parse and process information by meeting or manually sending around data. AIOps should learn what analysis and monitoring data to show each group or team from the large pool of resource metrics.

How does AIOps work?

AIOps uses advanced analytical technologies such as machine learning 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 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 origins of problems. IT operations teams can attempt to prevent the recurrence of problems in the future 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 the potential geographical distance between 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.
Stages of the AIOps process.
Main elements of AIOps and how they work.

Key AIOps use cases

AIOps is generally used in companies 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, which then 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 help 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 and raise customer satisfaction levels over time.
  • Threat detection. AIOps can assist in identifying 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.

AIOps technologies

AIOps uses a conglomeration of various AI strategies, including data output, aggregation, advanced analytics, algorithms, automation and orchestration, machine learning, and visualization. Most of these technologies are reasonably well defined and mature.

  • 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 information coming from these sources to create new data and metadata. Analytics reduces noise -- unneeded or spurious data -- and spots trends and patterns that allow 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 are how the IT personnel prioritize security-related events and teach application performance decisions to the platform. The algorithms form the foundation for machine learning, 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 on 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. 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 interface with others.

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 IT teams' 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.

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 on site 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 there's a growing stable of product offerings for businesses to review and evaluate, including but not limited to the following:

  • BMC Software TrueSight.
  • Cisco Crosswork Situation Manager.
  • Datadog.
  • Datapipe Trebuchet.
  • Dynatrace.
  • HCL Software Dryice.
  • Moogsoft.
  • New Relic.
  • ServiceNow IT Service Management.
  • Splunk IT Service Intelligence.

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 at a compound annual growth rate from $2.83 billion in 2021 to $19.93 billion by 2028.

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.

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.

This was last updated in June 2023

Continue Reading About AIOps (artificial intelligence for IT operations)

Dig Deeper on IT systems management and monitoring

Software Quality
App Architecture
Cloud Computing
SearchAWS
TheServerSide.com
Data Center
Close