AIOps (an abbreviation of artificial intelligence for IT operations) applies AI, including machine learning and big data analytics, to automate IT operations, enhancing service and performance management through event correlation, anomaly detection, and intelligent insights for ongoing improvement.
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:
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.
AIOps puts relevant data under the microscope, locating correlations between data elements and grouping them together so that they may be further analyzed.
In-depth analysis allows AIOps platforms to clearly identify root causes of problems, events, and trends, creating clear insights to help inform action.
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
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 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.
- Getting the most out of AIOps means implementing a proven strategy. Generally, this revolves around an incremental three-phase approach that leverages the power of machine intelligence to address IT incidents directly—detecting, predicting, and mitigating potential issues to ensure seamless IT operation.
The initial phase focuses on the real-time identification and reporting of IT incidents. Through a combination of historical and performance analysis, AIOps platforms can pinpoint bottlenecks, overloaded devices, and service faults. By correlating and contextualizing various events, logs, and metrics, this phase ensures that incidents are caught as they happen or even before they fully develop, allowing for immediate attention.
Moving beyond detection, the second phase employs advanced analytics and machine learning to foresee potential IT issues before they adversely affect users. Techniques like anomaly detection and change impact analysis play a crucial role here, predicting faults, overloads, or other conditions that could lead to failures. This phase is crucial for proactive capacity management and maintaining optimal IT operations.
The final phase is where AIOps shines in its ability to move beyond identifying and predicting and into actual remediation. Through root cause analysis and automated or assisted maintenance and optimization efforts, AIOps platforms can either directly fix issues or empower IT personnel with actionable insights. This phase significantly reduces downtime and improves overall IT service quality.
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:
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.
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.
AIOps is both swift and accurate, decreasing error rates while improving the mean time to resolution (MTTR) for service impacting issues. A lower MTTR means improved efficiency in incident management, and demonstrates to customers a commitment to providing the best possible service. 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.
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.
AIOps is a widespread term that can include a range of AI applications in IT. More specifically, AIOps tends to refer to two distinct categories:
Domain-centric AIOps
Domain-centric AIOps platforms are specialized AI tools that operate within a specific realm of IT operations. These platforms are tailored for monitoring and managing the performance of networking, applications, and cloud computing environments. By focusing on a particular domain, they empower operational teams to achieve a deep, nuanced understanding and control over specific aspects of their IT infrastructure.
Domain-agnostic AIOps
Domain-agnostic AIOps solutions offer a broader approach to IT operations management. These platforms are designed to transcend individual network or organizational boundaries, applying predictive analytics and AI-driven automation on a larger scale. By aggregating and analyzing event data from a wide array of sources, domain-agnostic AIOps platforms can correlate diverse information to unearth comprehensive business insights. This versatility makes them invaluable for organizations interested in implementing AIOps across a more extensive range of IT environments and applications, facilitating a holistic view of operations and driving strategic decision-making.
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.
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.
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.
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:
AIOps can rapidly process and analyze incident alerts, producing solutions before incidents can spiral out of control.
By consistently analyzing data and comparing it to historical trends, AIOps is able to identify data outliers that may be indicative of potential problems.
In addition to early identification of issues, AIOps’ data collection and analysis capabilities employ machine learning to current and historical data trends, creating highly accurate forecasts of future outcomes.
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. By integrating deeper analytics and more sophisticated AI models, AIOps can sift through complex datasets to identify not just the symptoms of issues but their underlying causes. This enhanced root cause analysis is pivotal in resolving problems more effectively and preventing recurrence, ensuring more stable and reliable IT operations.
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.
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.
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.
AIOPs offers clear visibility into the shifting interdependencies of cloud adoption and migration. This significantly reduces the operational risks associated with such a transition.
APM allows IT teams to proactively detect performance anomalies, optimize application response times, and ensure that user experiences are consistently high quality. Through AI and machine learning algorithms, AIOps can predict potential issues before they affect users, facilitating a more resilient and responsive IT infrastructure.
AIOps significantly contributes to the app development process by providing real-time insights and feedback on application behavior under various scenarios. This supports developers in identifying potential bottlenecks or performance issues early in the development cycle. Additionally, AIOps can facilitate continuous integration and deployment (CI/CD) practices by automating aspects of testing, monitoring, and feedback loops.
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 common across different businesses.
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.
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.
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.
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.
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.
AIOps is positioned among various IT methodologies and practices, each aimed at optimizing different aspects of an organization's technological landscape. Delving into the nuances of AIOps, it is worth exploring how AIOps is connected to (but distinct from) three related concepts:
DevOps represents a collaborative and integrated approach between software development and IT operations teams, designed to enhance agility in software delivery and operations. It focuses on continuous integration, delivery, and feedback loops to rapidly address user needs and improve operational workflows. In contrast, AIOps leverages artificial intelligence to automate and refine IT processes, offering DevOps teams powerful tools for analyzing code quality, optimizing software delivery timelines, and ensuring high operational efficiency.
While DevOps breaks down silos between development and operations, AIOps provides the analytical horsepower to drive smarter decision-making and automation within this collaborative framework.
MLOps is a specialized discipline that streamlines the lifecycle management of machine learning models from development to deployment production environments. It emphasizes the seamless integration of ML models into applications, ensuring that they remain relevant, accurate, and efficient over time.
By comparison, AIOps applies machine learning techniques to the broader scope of IT operations, focusing on generating insights that enhance system processes and operational efficiency across the IT landscape. While MLOps is dedicated to the operationalization of machine learning models, AIOps utilizes these models to improve IT operational workflows and system performance.
SRE is a discipline that employs software engineering principles to address operational challenges, creating scalable and highly reliable software systems. SRE focuses on automating operational tasks, incident management, and enhancing system reliability.
AIOps and SRE share common goals in improving system performance and reliability but from slightly different angles. AIOps equips SRE professionals with data-driven insights and predictive analytics, enabling them to proactively manage system reliability and reduce mean time to resolution for incidents. By integrating AIOps into SRE practices, organizations can achieve a more proactive stance on incident management and system optimization.
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 AI 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.