Defining Artificial Intelligence for IT Operations (AIOps)

Technology has developed rapidly in the last few decades to keep up with the growing demands of digital transformation. Artificial Intelligence has been one of the latest feats to make headlines today – being included with several applications, technologies, and platforms to make use of its automated, simplistic, and efficient capabilities. As more organizations rush to embrace the new era of advancement, the IT operations behind them need to become more effective and reliable – a need that is quickly being filled with the use of AI in IT Operations, or AIOps.

The Use of AI in IT Operations

As mentioned above, the drive towards Artificial Intelligence for IT operations stems from the sudden pressure to process larger amounts of data in less time. By 2025, 181 zettabytes of data are expected to be created, consumed, and stored. Generative AI has become the key to elevating IT operations to become more efficient and advanced. AIOps allows organizations to process complex and vast amounts of data in a streamlined and automated manner while reducing human error and the need for manual labor – this can help lower expenses and boost productivity.

Recent ESG research has shown that 85% of organizations are using, planning to use, or considering artificial intelligence in many functional areas – including IT operations. This move towards AIOps can spell out a new era in IT operations that values optimized processing over legacy infrastructure. According to Forbes, this modernization of legacy systems - especially when powered by AI - isn't just about upgrading but about “future-proofing, strategy, and modifications that allow you to stay ahead in the dynamic tech environment.” Now, let’s get a better idea of the definition of AIOps.

What Is AIOps?

Artificial Intelligence for IT Operations – or AIOps – is a term coined by Gartner as the combination of “big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.” AIOps essentially uses Artificial intelligence to maintain an IT infrastructure in a multi-layered approach that prioritizes automation, real-time insights, and workload efficiency.

AIOps systems use Machine Learning (ML), Natural Language Processing (NLP), advanced analytic capabilities, and several other AI features to provide customized, intelligent, and proactive solutions for smoother processing operations. Apart from making operating processes easier, AIOps can be used to detect anomalies, identify patterns, and enhance performance and service management. AIOps tools can also be used to collect and aggregate data from multiple sources in various forms to be analyzed, managed, and configured accordingly.

The use of AIOps can also help to sift out the “noise” when looking for significant alerts – helping to discern what is important in a vast amount of data. AI for IT operations can also conduct Root Cause Analysis (RCA) as needed to diagnose and send alerts about anomalies to ensure that problems can be automatically flagged and fixed by the IT team. AIOps also uses real-time processing and multiple monitoring tools to provide a unified, proactive, and automated response – reducing latency and visibility issues between challenging or siloed digital landscapes. Now that we’re more familiar with the concept, let’s explore why AIOps is so important for organizations.

Why Is AIOps Important?

Most organizations have to sift through an unimaginable amount of data in a short amount of time to effectively operate in today’s digital climate. With new technologies and the Internet of Things (IoT) ensuring a continuous flow of interconnected data streams, most IT teams and traditional infrastructure might buckle under the pressure of effective processing. This means that a large majority of data might not be processed effectively in such a short period.

With such big amounts of data, IT teams will deal with several alerts that might not even need attention at all. This can lead to ‘alert fatigue’ where important alerts might slip through the cracks in the sea of redundant alerts. Many organizations today also invest in different infrastructure systems at the same time, making data collection more difficult. AIOps can, however, correlate data across different sources – effortlessly bridging the gap across IT environments.

AIOps also prioritizes customer satisfaction and user experience by ensuring a proactive approach to outages and processing latency. This ensures a smooth, end-user experience without disruptions. By making use of Artificial Intelligence for IT operations, organizations can also then allocate more important tasks to workers instead of focusing on common and repetitive ones – boosting innovative productivity and service availability. Let’s look further into the importance of AIOps by listing out some of the main benefits as well.

The 6 Benefits of AIOps

AIOps has been a revolutionary way for companies to leverage AI in a way that improves processing and makes business easier. These are six of the main benefits offered by the use of AI in IT operations:

1. Streamlining Data Management and Resolutions. AIOps can speed up data management from different sources and in different forms to provide actionable solutions in a fraction of the time. This is done by quickly sifting through the noise and focusing on important alerts to ensure fast problem-solving. This also improves the Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR) problems within essential IT systems.
2. Cost-Effectiveness of AI for IT Operations. The use of AIOps can also be much easier on the wallet by automating otherwise expensive tasks. With AI features, IT processing can detect anomalies faster and before they cause too much costly damage. Automation also reduces the need for a larger workforce for repetitive tasks, saving money on staffing resources and allocation.
3. Automation at Scale. A crucial element of AIOps is automation. Using AI for IT Operations allows organizations to automate tasks safely and accurately across every system. Automation can also be scaled or according to your business’s unique needs.
4. Improving Collaboration. With AIOps, different forms of data in different environments are brought together to be analyzed. This means that different teams and departments within an organization can collaborate through a unified view of events – ensuring a combined and coordinated effort to reduce risks and make informed decisions for the entire company.
5. Enhanced Visibility. Another great benefit of AIOps is the enhanced visibility achieved throughout the company. This transparency and observability improve communication and allow teams to respond faster and more effectively to issues.
6. Accelerated Innovation. When using AIOps to automate trivial and repetitive tasks within the organization, you free up valuable workers who can focus their skills and attention on more innovative ideas and productivity strategies. This ensures that your company is optimized to meet the needs of an evolving market.

After noting the main benefits of AIOps, it’s easy to see how it could be used to elevate organizations by streamlining processing, creating a proactive approach to anomalies, and offering real-time valuable insights. Now, let’s go through the specifics of how exactly AIOps works to perform such feats.

How Does AIOps Work?

When understanding the way any technology works, it’s easier to break it down into a set of steps to show how each element fits together to ensure the final result. How AIOps works can be separated into three stages:

1. Detection – This is a general observation stage in which the AIOps will use big data analytics and machine learning to ingest, aggregate, and assess large amounts of data to identify any patterns, anomalies, or issues. The stage is essential for finding bottlenecks and faults through the use of historical analysis, performance analysis, system logs and metrics, and other infrastructure data.
2. Contextualize and Report – In this stage, AIOps tools will correlate events and comb through alerts to find the significant ones. With anomaly detection and other predictive technologies, AIOps can send reports to the human team to handle.
3. Directly Engage – This is where the proactive and automated powers of AIOps come into play to rectify any IT incidents and simplify the problem in any way for the IT team. This involves performing Root Cause Analysis and augmenting any tech support with real-time insights or recommended solutions. The AI also ensures constant learning that helps the system adapt to future events.

AIOps tools can greatly enhance the way your organization handles data and proactive planning. Fortunately, in this dynamic digital landscape, AIOps options are quite varied and accessible.

Best AIOps Tools and Vendors

When it comes to AIOps tools, you can categorize them according to your specific IT operational needs. Generally, there are two groups: domain-centric or domain-agnostic.

• Domain-Centric – These solutions will focus on only a specific domain or part of a system in the organization.
• Domain-Agnostic – These are more comprehensive solutions designed to collect and analyze data from anywhere across the system and solve any issue they find.

Several AIOps vendors and tools on the market can help streamline your organization’s processing powers. These are some of the top AIOps vendors to look for:

• CloudFabrix – This data-centric AIOps platform enables autonomous enterprises and provides alert noise reduction, incident management, predictive analytics, and anomaly detection, among other predictive features. This platform provides actionable insights to help IT teams make more efficient decisions.
• PagerDuty - Organizations of all sizes can use PagerDuty to identify issues and opportunities in real-time. The platform specializes in a SaaS incident response for IT departments and integrates machine data and human intelligence to improve visibility and agility.
• Digitate – This SaaS-based autonomous enterprise software provides agility, assurance, and resiliency for IT and business operations using its Ignio AIOps platform. It combines unified observability, AI-powered insights, and closed-loop automation to deliver better visibility and control over business KPIs in hybrid and multi-cloud environments.
• Splunk – This platform allows organizations to automate the collection, indexing, and alerting of machine data critical to operations. This helps to uncover actionable insights and use artificial intelligence and machine learning for predictive and proactive business decisions.
• Moogsoft – With this AIOps platform, organizations can create an automated connective layer that brings IT operations, observability, and incident management teams onto the same page. This will reduce the mean time to detect (MTTD), investigate, and resolve incidents to operate more efficiently.
• BigPanda – This platform provides event correlation and automation to ensure that IT teams can detect, respond to, and resolve incidents faster. It also allows organizations to turn IT noise into insights and manual tasks into automated actions - reducing IT operating costs, improving service availability, and increasing business velocity without adding risk.
• Better Stack – Using this platform allows organizations to see inside any stack, debug any issue, and resolve any incident. The AIOps platform improves visibility and ensures that your team can aggregate all logs into structured data – allowing you to query everything like a single database with SQL.

All these tools and vendors offer an efficient way to process large amounts of data in a custom manner according to the different needs your business might have. Now, let’s try to uncover some of the ways AIOps can be used.

AIOps Use Cases

The use of AIOps in real life can be varied according to different needs. Many organizations might only need Artificial Intelligence for IT operations in specific ways. Some of the main AIOps use cases include:

• Anomaly Detection: Naturally, one of the key uses of AIOps tools is to find and report anomalies in the system. This is done by combing through the large expanse of data and finding outliers that deviate from regular observed patterns. These can help to detect and predict IT incidents – ensuring informed action can be taken to prevent damage.
• Root Cause Analysis (RCA): With Root Cause Analysis, an organization can figure out what the root cause of every problem is. This allows IT teams to improve in the vulnerable or weaker areas to prevent future incidents.
• Performance Monitoring: AIOps offers multi-layered solutions that can ensure monitoring on a unified layer with metrics and reports that are understood across departments. This improves event correlation and ensures that information is consolidated in a more efficient and palatable way.
• DevOps Adoption: DevOps teams can also make use of AIOps to speed up development by automating certain tasks and leveraging enhanced visibility and predictive tools to improve code quality. AIOps also ensures that bugs are detected ahead of time.
• Cloud Adoption or Migration: Cloud adoption can be a complex issue for many organizations with several environments to invest in at the same time. AIOps offer a multicloud approach that can reduce the operational risks of cloud migration and a hybrid cloud approach. These AIOps solutions can also ensure efficient cloud migration by providing transparency, observability, and automation for workloads.
• Application Performance Monitoring (APM): Most modern applications will use complex software to run and scale across the cloud environment. AIOps ensures that the resulting metrics can be gathered more efficiently and securely to produce actionable insights.

Using Artificial Intelligence for IT operations is an ideal method of improving your organization’s processing, efficiency, and visibility. We’ve covered the importance, benefits, and use cases of implementing AIOps in your company and even listed some of the top AIOps vendors and tools available. However, we can simplify the process further and introduce Sangfor SkyOps as the ultimate AIOps platform for your needs.

Sangfor SkyOps is a cloud-based platform that connects to your on-premises Sangfor Private Cloud (HCI/VDI) and/or Sangfor Managed Cloud. The platform then gathers and analyzes data to continuously monitor its status, health, and performance. Using sophisticated AI algorithms, Sangfor’s SkyOps can predict potential issues and ensure that IT teams can proactively tackle problems before they escalate. With centralized monitoring and management, AI-driven fault detection and prediction, and a wide range of online services, Sangfor SkyOps provides comprehensive visibility to help you stay ahead of potential issues and maintain seamless operations in real time.

To invest in infrastructure or cybersecurity that fully understands your needs, contact Sangfor Technologies today and elevate your organization.