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AI for IT Operations: Everything You Need to Know to Get Ahead

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Written by Ceara Hickerson

“DefinITions” – Artificial Intelligence, IT Operations, AIOps

First, let’s dive into some terminology used in the IT space and make some clear distinctions on three common terms: artificial intelligence, IT operations, and artificial intelligence for IT operations (AIOps).

Related Reading: Learn more about the 5 Levels of AIOps Maturity

What is AI?

Artificial intelligence (AI), also known as machine learning (ML), is the simulation of human intelligence processes by machines, especially computer systems.

AI systems work by receiving data, analyzing it for correlations and patterns, and using those patterns to make predictions about future scenarios. For example, a chatbot that is fed examples of text chats can learn to produce “conversations” with people.

AI processes data and “gains knowledge” in three ways: learning, reasoning, and self-correction.

What’s more, AI has many benefits for enterprises because it can give insight into operations that may have been overlooked, and in some cases, can perform tasks better than humans, especially with tasks that are tedious, repetitive, or detail-oriented; such as analyzing large numbers of legal documents to ensure relevant fields are filled inappropriately.

Artificial intelligence tools can complete these tasks quickly and efficiently where a human eye can easily miss a checkbox.

What is IT Operations?

IT operations (ITOps) are the processes and services that are administered and managed by the IT department within an organization. The ITOps team usually functions as a distinct working group within the broader department; it includes a group of operators led by an operations manager.

The Disciplined Agile framework identifies six classifications for ITOps tasks, as well as the associated activities that correspond to each strategic objective:

  • Manage infrastructure: Keeping infrastructure intact is a key function for IT Operations. It consists of computing and networking hardware, as well as the applications that run on them. This broadens to oversight of cloud environments, application deployment, network security management, facilities management and other hardware IT infrastructure components.
  • Manage configurations: It is important to keep a record of hardware configurations and solution dependencies. This aids ITOps in implementing new configurations as well as maintaining existing ones for optimal performance of the infrastructure and services.
  • Run solutions: The core of ITOps is to run solutions. Operators are responsible for implementing data back-ups, restoring systems after service outages or updates, configuring and tuning servers and other configurations. This ensures the IT infrastructure is not only optimized for performance, but also allocates resources where they are most needed.
  • Evolve infrastructure: While their job is to maintain a functioning infrastructure, ITOps also act to innovate systems and implement changes as needed to benefit the business or organization. This includes: applying software patches, introducing new hardware and software applications, and identifying areas of change.
  • Mitigate disasters: IT operations are always on standby for preventing disaster and implementing recovery plans for enterprises. ITOps teams plan, simulate and practice disaster recovery situations to avoid downtime and lost revenue if an unexpected service outage occurs.
  • Govern ITOps: As a part of mitigating disaster, the ITOps team monitors and measures infrastructure performance, especially as it pertains to the organization’s security posture. It also develops operational metrics to evaluate the performance of key processes and services, manages software license compliance and conducts audits to verify that security and performance goals are met.

What is AI for IT Operations (AIOps)?

Artificial intelligence for IT operations (AIOps) is the application of artificial intelligence (AI) to improve or enhance IT operations. AIOps often uses big data, analytics, and machine learning to achieve the following:

  • Collect and categorize huge volumes of data generated by ever-expanding IT infrastructure applications, components, and performance-monitoring tools.
  • Differentiate “signals” out of the “noise” to identify events and patterns related to system performance and availability issues.
  • Diagnose and report root causes to ITOps for immediate response and mitigation. In some cases, AIOps can and will resolve the issue without human intervention.

AIOps offers a strategic set of conglomerated tools to improve processes and response time for the IT operations team. It can replace multiple, separate, manual systems and manage them with a single, intelligent, automated ITOps platform.

As the IT operations landscape expands and diversifies, it becomes more difficult to fully monitor and respond with agility. AIOps bridges this gap and effectively optimizes IT operations management.

Why do we need AI for IT Operations?

Most organizations are moving from a traditional infrastructure of siloed, static physical systems to a dynamic mix of hybrid cloud and physical environments. The systems are running on virtualized or software-defined resources that scale and reconfigure constantly.

These systems generate a gargantuan amount of data that only continues to grow. According to a Gartner study, IT infrastructure generates two to three times more IT operations data every year. This increased exponentially during the COVID-19 pandemic with everyone working from home – and it is not “going back to normal” anytime soon. As lockdowns ease, 32% of companies plan to continue using AIOps for expanding remote work environments.

To that end, it begs for innovation in the way that IT operations teams work and respond. Traditional domain-based IT management solutions cannot keep stride with the volume; it’s difficult to sift through significant events out of the waves of surrounding data. Also, forget about trying to correlate data across different but interdependent environments.

Furthermore, legacy solutions cannot provide real-time updates and insights or predictive analysis for IT operations teams. This problem leads to an inundated system that lacks the capacity to respond to issues with agility and meet customer service level expectations.

This is where AI for IT operations comes into the picture. AIOps provides the visibility and agility that IT operations teams need to enable them to maintain exceptional service levels. AIOps analyzes performance data and dependencies across all environments extract significant events related to slow-downs or outages and alert IT, staff, to issues, root causes, and the recommended solutions. By and far, AIOps is an efficiency model.

Listen Now: How AIOps Can Advance IT Operations

How does AIOps work?

Not all AIOps solutions are created equal. The easiest way to understand how AIOps works is to review the role that each component of technology — big data, machine learning, and IT automation–plays in the process.

In a nutshell, AIOps uses a big data platform to collect disparate ITOps data in one place, like:

  • Historical performance and event data
  • Streaming real-time operations events
  • System logs and metrics
  • Network data and packet data
  • Incident-related data and ticketing
  • Related document-based data

Then, AIOps applies targeted analytics and ML capabilities:

  • Data Selection & Pattern Discovery: AIOps separates distinct events from the overall “noise”. Using analytics, AIOps creates rule application and pattern matching to explore IT operations data and to separate signals from all other data points.
  • Inference: AIOps identifies root causes of issues and proposes solutions using industry-specific algorithms. Further, AIOps solutions correlate abnormal events with other event data across environments; then target the cause of an outage or performance problem and provide remedies.
  • Automation & Collaboration: At a base level, AIOps can automate responses and route alerts along with recommended solutions to the relevant teams. It can also process results from ML to trigger automatic system responses that resolve critical events in real-time; even before users are aware anything occurred.

Continuous Learning: A true AI system learns and improves the handling of future problems. As the machine collects analytics, the AI changes algorithms or creates new ones to identify issues earlier and recommend other solutions. AI platforms aid the system in learning to adapt to environmental changes (i.e. new infrastructure provisioned or reconfigured DevOps teams).

What are the benefits of AIOps?

The most prominent benefit of AI for IT operations is that it strengthens and enhances the IT operations team. With AIOps, IT operations can identify, address and resolve latencies much faster than manually sifting through alerts and data points.

This results in many benefits across the board for an enterprise as a whole:

  • Achieve faster mean time to resolution (MTTR): Slash the time it takes to respond to an event. AIOps pinpoints anomalies in the data pool and proposes solutions faster than humanly possible.
  • Be proactive and predictive vs. reactive: Since AIOps never stops learning, it only gets better at identifying and alerting, or even resolving, urgent issues. It can provide predictive alerts that let IT teams address potential problems before they lead to slow-downs or outages.
  • Evolve ITOps and ITOps teams: Once upon a time, ITOps responded to every alert from every environment, but that changed with AIOps. Instead, they receive alerts that meet specific service level thresholds or parameters – including the context required to make the best diagnosis and make the optimal corrective action. As AIOps learns and is able to take on the smaller, menial tasks, ITOps teams can evolve and focus on much greater strategic areas that bring value to a business.

Ready to evolve your ITOps team with AIOps?