The journey to AIOps is different in every organization. Once you assess where you are in your journey to AIOps, you can start to incorporate tools that help teams to observe, predict and act quickly to IT operational issues. As you consider tools to improve AIOps within your organization, you’ll want to help ensure that they have the following features:

Observability: Observability refers to software tools and practices for ingesting, aggregating and analyzing a steady stream of performance data from a distributed application and the hardware it runs on, in order to more effectively monitor, troubleshoot and debug the application to meet customer experience expectations, service level agreements (SLAs) and other business requirements.

These solutions can give a holistic view across your applications, infrastructure and network through data aggregation and consolidation, but do not take corrective action to address IT issues. Although they do not take corrective action to address IT issues, they do collect and aggregate IT data from various data sources across IT domains to alert users of potential issues, expecting IT service teams to implement the necessary remediation.

While the data and corresponding visualizations from these tools are valuable, they create a dependency on IT organizations to make decisions and respond appropriately to technical issues. Resource optimization that requires an operator to manually update operational systems might not see the benefits in dynamic demand situations.

Predictive analytics: AIOps solutions can analyze and correlate data for better insights and automated actions, allowing IT teams to maintain control over the increasingly complex IT environments and assure application performance. 

Being able to correlate and isolate issues is a massive step forward for any IT operations team. It reduces times to detect issues that might not have otherwise been found in the organization. Organizations reap the benefits of automatic anomaly detection, alerts and solution recommendations, which in turn reduces overall downtime as well as the number of incidents and tickets.

Dynamic resource optimization can be automated by using predictive analytics, which can assure application performance while safely reduce resource costs even during high variability of demand.

Proactive response: Some AIOps solutions will proactively respond to unintended events, such as slowdowns and outages, bringing application performance and resource management together in real-time.

By feeding application performance metrics into predictive algorithms, they can identify patterns and trends that coincide with different IT issues. With the ability to forecast IT problems before they occur, AIOps tools can start relevant, automated process in response, rectifying issues quickly. Organizations are able to see the benefits from intelligent automation, such as improve mean time to detection (MTTD).

This type of technology is the future of IT operations management as it can help the business improve both the the employee and customer experience. Not only do AIOps systems ensure that IT service issues are resolved in a timely manner but they also provide a safety net for IT operation teams, addressing issues that might fall through the cracks due to human oversight, such as organizational silos, under-resourced teams and more. 

The overarching benefit of AIOps is that it enables IT operations to identify, address and resolve slow-downs and outages faster than they can by sifting manually through alerts from multiple IT operations tools. This results in several key benefits:

Faster mean time to resolution (MTTR): By cutting through IT operations noise and correlating operations data from multiple IT environments, AIOps is able to identify root causes and propose solutions faster and more accurately than humanly possible. This enables organizations to set and achieve previously unthinkable MTTR goals. For example, Vivy’s IT infrastructure reduced the mean time to repair (MTTR) for the company’s app by 66%, from three days to one day or less.

Lower operational costs: Automatic identification of operational issues and reprogrammed response scripts reduce operational costs, allowing for better resource allocation. This also frees up staffing resources to work on more innovative and complex work, leading to an improved employee experience. Through optimization, Providence saved more than USD 2 million while assuring app performance during peaks.

More observability and better collaboration: Available integrations within AIOps monitoring tools facilitate more effective cross-team collaboration across DevOps, ITOps, governance and security functions. Better visibility, communication and transparency allow these teams to improve decision-making and respond to issues more quickly. As an example, Dealerware brought more observability to their container-based architecture, which improved app performance during the pandemic and reducing delivery latency by 98%.

Go from reactive to proactive to predictive management: With built-in predictive analytics capabilities, AIOps continuously learns to identify and prioritize the most urgent alerts, letting IT teams address potential problems before they lead to slow-downs or outages. Electrolux accelerated IT-issues resolution from 3 weeks to an hour via faster meantime to detection (MTTD) and saved more than 1,000 hours per year by automating repair tasks.

AIOps incorporates big data, advanced analytics and machine learning capabilities to tackle the following use cases:

Root cause analysis: As the name suggests, root cause analyses determine the root cause of problems in order to remediate with the appropriate solutions. By identifying root causes, teams can avoid unnecessary work involved with treating symptoms of the issue versus the core problem. For example, an AIOps platform can trace the source of a network outage to resolve it immediately and set up safeguards to prevent the similar problems in the future.

Anomaly detection: AIOps tools can comb through large amounts of historical data and discover atypical data points within a data set. These outliers act as signals that identify and predict problematic events, such as data breaches. This capability allows businesses to avoid costly consequences, such as negative PR, regulatory fines and declines in consumer confidence.  

Performance monitoring: Modern applications are often separated by multiple layers of abstraction, making it difficult to understand which underlying physical server, storage and networking resources are supporting which applications. AIOps helps to bridge this gap. It acts as a monitoring tool for cloud infrastructure, virtualization and storage systems, reporting on metrics such as usage, availability and response times. In addition, it uses event correlation capabilities to consolidate and aggregate information, enabling better information consumption for users.  

Cloud adoption/migration: For most organizations, cloud adoption is gradual, not wholesale, resulting in a hybrid multicloud environment (private cloud, public cloud, multiple vendors), with multiple interdependencies that can change too quickly and frequently to document. By providing clear visibility into these interdependencies, AIOps can dramatically reduce the operational risks of cloud migration and a hybrid cloud approach.

DevOps adoption: DevOps speeds development by giving development teams more power to provision and reconfigure infrastructure, but IT still must manage that infrastructure. AIOps provides the visibility and automation IT needs to support DevOps without numerous additional management efforts.

The easiest way to understand how AIOps works is to review the role that each AIOps component technology—big data, machine learning and automation—plays in the process.

AIOps uses a big data platform to aggregate siloed IT operations data, teams and tools in one place. This data can include the following:

• Historical performance and event data.
• Streaming real-time operations events.
• System logs and metrics.
• Network data, including packet data.
• Incident-related data and ticketing.
• Application demand data.
• Infrastructure data.

AIOps then applies focused analytics and machine learning capabilities:

Separate significant event alerts from the ‘noise’: AIOps  combs through your IT operations data and separate signals, significant abnormal event alerts, from noise (everything else).

Identify root causes and propose solutions: AIOps can correlate abnormal events with other event data across environments to zero in on the cause of an outage or performance problem and suggest remedies.

Automate responses, including real-time proactive resolution: At a minimum, AIOps can automatically route alerts and recommended solutions to the appropriate IT teams, or even create response teams based on the nature of the problem and the solution. In many cases, it can process results from machine learning to trigger automatic system responses that address problems in real-time, before users are even aware they occurred.

Learn continually, to improve handling of future problems: AI models can also help the system learn about and adapt to changes in the environment, such as new infrastructure provisioned or reconfigured by DevOps teams.