What is AIOps?

As hardware and software becomes more powerful, it also becomes more intricate, creating increased demand on the IT departments who are responsible for managing it. And with every new advancement and capability, tool complexity increases. Until recently, IT operations teams have had few options when it comes to tackling the expanding complexity of vital technologies—hiring new IT data science talent and increasing department staff being the most obvious, if not the most cost effective, solution.

However, some advances actually do help take certain pressures off of IT Operations (ITOps). Consider the emerging technologies of Artificial Intelligence for Operations (AIOps).

AIOps is a combination of the terms artificial intelligence (AI) and operations (Ops). More specifically, it represents the merging of AI and ITOps, referring to multi-layer tech platforms that apply machine learning, analytics, and data science to automatically identify and resolve IT operational issues.

The term AIOps was first coined by Gartner in 2016, and grew out of the digital-transformation shift from centralized IT to anywhere operations with workloads in the cloud and on-premises across the globe. As the pace of innovation increased, so did the complexities of the technologies. This placed significant strain on IT operations, who would now be responsible for managing and servicing a range of new systems and devices.

AIOps introduced a new model for managing IT operations. Machine learning has revolutionized modern business. In fact, according to The Global CIO Point of View, nearly nine out of ten CIOs are either already employing this technology, or are planning to adopt it soon.

To better understand the capacity and responsibility of AIOps, let’s take a look at its core elements. These include the following:

Extensive IT data
A core mandate of AIOps is breaking down data silos. To do this, it aggregates diverse data from IT service management and IT operations management. This allows for faster identification of root causes, and helps enable automation.
Aggregated big data
Big data sits at the heart of any AIOps platform. By breaking down silos and freeing up available data, AIOps can then employ advanced analytics—both with existing, stored data, as well as data evolving in real time.
Machine learning
With so much data to analyze, AIOps depends on advanced machine learning capabilities that far outstrip manual human ability. Automating analytics and uncovering connections and insights, AIOps scales with speed and accuracy that would be otherwise impossible.
Observation
The AIOps process depends heavily on the platform’s ability to observe data and data behavior. Through data discovery, AIOps collects data from different IT domains and sources, potentially including container, cloud, or virtualized environments, or even legacy infrastructure. Data must be collected in as close to real time as possible, to provide the most up-to-date foundation.
Engagement
AIOps platforms provide configuration, coordination, and management of computer systems and software across multiple IT domains, including ITSM. AIOps analytics allow for more reliability and relevance in the data, incorporating information about the environment and making automation a reality.
Action
The end goal of AIOps is to create a system in which functions are fully automated, closing the loops and fully freeing up IT operations teams to take on other tasks. The reality is that AIOps is still developing, and some teams are resistant to fully embracing the AIOps possibilities. That said, AIOps is capable of handling simple jobs as well as complex ones, and many organizations are becoming more comfortable with AIOps platforms taking on greater responsibilities.

AIOps functions best when it is deployed independently to gather and analyze data from all available IT monitoring sources, providing a centralized system of engagement. To do this, it follows essentially the same process used by the human cognitive function. The five key algorithms at play are as follows:

Data selection

Combing through the colossal amount of available IT data, evaluating it, and identifying relevant data elements, AIOps must be able to locate the significant ‘needles’ hidden in terabyte-sized data 'haystacks,’ based on predetermined selection and prioritization metrics.

Pattern discovery

AIOps puts relevant data under the microscope, locating correlations between data elements and grouping them together so that they may be further analyzed.

Inference

In-depth analysis allows AIOps platforms to clearly identify root causes of problems, events, and trends, creating clear insights to help inform action.

Collaboration

AIOps must also function as a collaboration platform, notifying the right teams and individuals, providing them with relevant information, and facilitating effective collaboration despite possible distance between operators.

Automation

Finally, AIOps is designed to automatically respond to and remediate issues directly, significantly increasing the speed and accuracy of IT operations.

As previously addressed, increased technological complexity is a driving force behind the shift towards AIOps. Here are several specific trends and demands that are behind this evolution:

Expanding IT environments
New, dynamic IT environments have significantly outpaced the capabilities of manual, human oversight.
Exponentially increasing ITOps data amounts
The introduction of APIs, mobile apps, IOT devices, and machine users are creating an influx of valuable data. Machine learning and AI are the only options for effective analysis and reporting.
Increasing need for faster infrastructure-problem resolution
Technology has become a central factor in essentially all areas of business. When IT events occur, every second that it takes to identify and resolve the issue is a risk to an organization’s reputation and bottom line.
More computing power moving to the edge of the network
Networks are becoming decentralized thanks to the introduction of cloud computing and third-party services, creating an IT ecosystem where an increasing amount of budget and computing power exist on the fringes.
Growing developer influence, but not accountability
As applications become more centric, developers are taking a more active role in monitoring and other areas. But at its core, IT accountability still rests squarely on IT. This means that as technologies advance, ITOps is not only having to deal with increased complexity, but also increased responsibility.

An effective approach to AIOps should consist of three phases.

Predicting issues before they occur
Preventing impact to end users
Automating remediation and resolution

Graphic outlining the three-phased AIOps approach.

According to a study by Accenture, front-line customer support functions spend up to 12% of their time managing tickets, and 43% of IT service desk respondents are weighed down by having to choose from 100+ assignment groups. Simply put, there is too much data and information for modern IT and service departments to handle effectively. AIOps helps relieve much of this burden.

Here, we address several key benefits of using an AIOps platform:

Increased data value

AIOps combines intelligent automation with big data, uncovering hidden connections and casual data relationships across services, operations, and resources, and delivering actionable insights. The obvious result is improved usability in your data, and a better return from your data analysis activities.

Reduced costs

AIOps is a cost-effective alternative to hiring an army of IT staff and data scientists. Additionally, it can significantly reduce the time and attention IT operations teams spend on routine tasks and potentially unimportant alerts. This leads to increased efficiency, and reduced costs overall. Finally, AIOps helps protect businesses from costly service disruptions.

Streamlined IT operations

AIOps is both swift and accurate, decreasing error rates while also cutting down on the time to resolution of service impacting issues. At the same time, by breaking down data silos, AIOps offers a single, contextualized view of the entire IT environment. AIOps’ proactive performance monitoring and data analytics allow for faster, better decision making.

Improved employee experience and productivity

Employees are happiest when they have the right tools to do their jobs effectively. AIOps automates a range of important—though repetitive and time consuming—tasks, increasing employee productivity and improving the employee experience.

There are many AIOps platforms available, and each include their own associated tool set. Rather than list each tool here, we will focus on two essential capabilities: Machine learning analysis and AIOps insights.

Utilize AIOps insights

With a robust understanding of data, including logs, metrics, discovery, mapping, and more, you can develop the right foundation for AIOps, and then employ AIOps insights towards benefiting your business. Display dashboards, automation, DevOps tools, and AIOps interfaces all work in conjunction to provide in-depth insight into your operations.

AIOps: Machine learning analysis

By automating analytical model building, organizations can employ machine learning to create intelligent systems capable of learning from data, identifying relevant patterns, and taking actions with minimal human input. Incorporating advanced data collection, ETL, multiple data sources, flows, virtual agents, real-time applications, etc., machine learning analysis builds on the foundation provided by AIOps insights, and then turns those insights into reliable, actionable conclusions.

AIOps powered by ServiceNow

At its heart, AIOps is a platform designed to intelligently collect and analyze IT operational data. But from these two primary tasks, AIOps becomes an invaluable asset in a variety of actions and solutions. Here are nine popular use cases for AIOps:

Incident event correlation

AIOps has the capacity to rapidly process and analyze incident alerts, producing solutions before incidents can spiral out of control.

Anomaly detection

By consistently analyzing data and comparing it to historical trends, AIOps is able to identify data outliers that may be indicative of potential problems.

Predictive analytics

In addition to early identification of issues, AIOps’ data collection and analysis capabilities are able to employ machine learning to current and historical data trends, creating highly accurate forecasts of future outcomes.

Root cause analysis

AIOps may also be instrumental in root cause analysis, correlating millions of data points, providing user and business context, tracking event patterns, and more, for accurate diagnoses of potential causes of problems.

Streamlining support

AIOps root cause analysis capabilities benefit not only businesses, but also customers. Support agents are able to identify and resolve issues more quickly, providing a better experience to customers. At the same time, IT desks can manage more tickets with greater accuracy.

Automated incident response

With the right data and directives, AIOps can be set to automatically address issues as they arise. Automated incident response allows for highly accurate identification, diagnosis, and remediation, much more quickly than is possible with human operators.

Digital transformation

By effectively removing the burden of new technologies and complexities from ITOps, AIOps allows for unrestricted digital transformation. Businesses can enjoy the flexibility of embracing new advances to address strategic goals, without having to worry about whether IT is able to handle the increased load.

Cloud adoption/migration

AIOPs offers clear visibility into the shifting interdependencies of cloud adoption and migration. This significantly reduces the operational risks associated with such a transition.

DevOps adoption

Finally, by providing effective automation and clear data visibility, AIOps empowers IT to better support the DevOps infrastructure.

Launching AIOps is a task that will require a unique approach depending upon your organization, its capabilities, and its needs. However, there are a few basic steps that are generally common across different businesses.

Understand and address common barriers to adoption

Depending on your organization, you may face resistance when promoting an AIOps approach. Common barrier to adoption may include the following:

Absence of team data scientists
Lack of relevant skills
Insufficient or low-quality data
No integrated way to act on insights

Thankfully, the most effective AIOps providers eliminate these issues. ServiceNow provides robust data-science services, supplementing existing skill sets with easy to use tools, and offering valuable next steps. With ServiceNow you don’t need to hire data scientists, and you don’t need to worry about issues preventing successful AIOps adoption.

Create a business case

Help promote management and leadership buyin by creating a business case for AIOps. Identify areas within your IT operations that could be improved upon, and share how AIOps offers reliable, effective solutions.

Select your AIOps stack

Choosing an AIOps platform takes an in-depth knowledge of your business and a dedicated amount of research into available options. Recognize that there are many solutions available, so be sure to view demos and read relevant reviews as you make your choice.

Develop a rollout plan

Once you’ve chosen your preferred AIOps solution, creating a detailed rollout plan will help ensure that you are making the transition at the correct pace, without wasting time or other resources.

Engage employees

Remember, your employees are most interested in how this new approach will benefit them. Demonstrate how intelligent, predictive self-service can offer predictive support, deflecting cases from agents, and how automation will help eliminate time-consuming, repetitive tasks.

The pace of digital transformation is accelerating, and shows not signs of slowing anytime soon. With this growth, the demand for resilient, accurate, and timely IT operations is also increasing. ServiceNow IT Operations Management (ITOM) provides the solution.

The ServiceNow Now platform incorporates comprehensive AIOps capabilities, allowing organizations to turn their ITOps into intelligent, proactive processes. Establish dependable automation, eliminate friction, breakdown data silos and more, with ServiceNow.

What is AIOps, and where did it begin?

Elements of AIOps

How does AIOps work?

Data selection

Pattern discovery

Inference

Collaboration

Automation

What is driving AIOps?