AI for IT Operations: The AIOps Guide 2023
Transformational technologies such as artificial intelligence (AI), machine learning (ML), and blockchain have begun reshaping the business and creating new customer-focused services.
One of the key trends materializing from the continuing maturation of AI and machine learning is how these technologies disrupt Cloud/IT operations (ITOPs) and IT Service Management (ITSM). AI and ML are making these activities more intelligent and automated, driving higher productivity and continuous improvement.
Definition of AIOps
According to the Glossary of AI Terms, AIOps is artificial intelligence for Cloud & IT operations is a new approach for automating and enhancing IT operations through machine learning and analytics to identify and respond to IT operational issues in real-time.
Just as AI and machine learning have had a tremendous impact on cybersecurity solutions, a similar trend will also play out for ITOps, DevOps, and CloudOps.
The AIOps platform stores the causes and solutions for every fixed incident and uses that knowledge to help Ops teams diagnose causes and prescribe solutions for future issues.
The Impact of AIOps Platforms on IT and Business Processes
In the new world of serverless architectures and microservices-based applications with dynamic and elastic resources, the old IT methods and processes are not just suboptimal – they fail. AIOps becomes necessary for IT organizations to ensure the integrity, stability, and transparency of Cloud and IT operations. For instance, AIOps enables companies to gauge enterprise IT operations’ health proactively, including dynamic cloud activities.
AIOps Platforms with AI-driven multi-cloud operations allow organizations to monitor, detect, and prevent disruptions. Why does this matter? It is because disruptions impact enterprises negatively, causing loss of revenue, unhappy users, negative brand reputation, etc. Operational failures and poor service level availability create the need for enterprise CIOs to leverage AI-driven multi-cloud and DevOps solutions that leverage AI/ML to automate operations and provide real-time visibility to take action.
“Finding the exact root cause of outages and performance issues is the most time-consuming aspect of the incident management process,” says Forrester Senior Analyst Rich Lane. AIOps empowers IT teams with contextualized data and Machine Learning enabling them to anticipate Cloud and IT operational issues before they occur, such as server capacity constraints that need to be addressed immediately without the need for human intervention. Also, the prescriptive use of AIOps helps IT organizations identify and implement the most effective solutions to address Cloud and operational challenges when they arise. AIOps extends to service management, performance management, and automation to revolutionize Cloud & IT operations across many infrastructure systems, storage, networks, and services/applications.
CIOs often lament the number of people and the portion of their budget they must devote to “keeping the lights on.” They are referring to IT operations, the process of operating and maintaining the entirety of the IT environment and its users.
While it may be the least glamorous side of IT work, it’s necessary. Amidst these challenges, Use Cases of Generative AI in IT Operations can potentially revolutionize the efficiency and resource management of these essential tasks.
CIOs would prefer to take charge of innovative projects that bring high value to their organization. However, uptime and performance stats of underlying computer systems, especially systems tied to revenue generation, remain part of ensuring business uptime. Keeping the lights on is quite important to people who don’t want to stare at a blank computer screen, and there’s more than one way to ensure it.
The Roles of AIOps Platforms
AIOps tools use AI to monitor and manage environments under the direction of the operations team. AIOps upends cloud and IT operations through changes to the entire process to make it more proactive, predictive, prescriptive, and personalized.
Proactive. Humans can monitor systems and anticipate problems, but there simply aren’t enough skilled people available to cover an enterprise’s entire environment all the time. Cloud and IT are fertile grounds for AI and machine learning algorithms. Every user, physical or virtual device, and application in the IT environment generates data in logs, events, metrics, and alerts.
This data is collected by AIOps tools to reflect systems’ health status and countless other minute details generated 24 hours a day, every day of the year. AIOps learn the IT environment and then use it, over time, to drive AIOps activity proactively with little to no human intervention. AI and machine learning can augment human effort on mundane tasks, which frees up admins to do more significant, high-value work that requires their intelligence.
Predictive. Predictive AIOps platforms detect a potential oncoming major incident and suggest a corrective course to fix it and avoid downtime, such as rebooting a server or patch an application. By contrast, unintelligent monitoring systems must catch when the failure occurs after the fact, alert IT and support subsequent diagnosis and resolution.
An example is an AIOps platform that could send an event alert about an unstable wireless router to a systems administrator’s or a network engineer’s dashboard with data on the potential problem and particularly recommended actions. If it is left unresolved, users will lose network connectivity. The AIOps tool predicted this outage and recommended a restart of the wireless router. The admin verifies the situation and restarts the router. With the AIOps tool’s aid, users experienced minutes of downtime instead of days or longer under the old process of reactive action.
An AIOps platform’s prescriptive suggestions for issue resolutions can, of course, end up off-base. Humans need to train the system through feedback on the prescribed fix’s accuracy and efficiency. This critical feedback loop between admins and software that can learn from them helps improve the system’s accuracy for the future. An admin who disapproves of a prescribed action can tell the tool what they did instead of resolving the problem. The more information an admin provides about the root cause of a given situation, the more accurately the AIOps tool works. The next time this issue arises, the system is better prepared to offer a helpful suggestion.
Personalized. Every company has a unique IT environment. One enterprise uses a primary public cloud provider, such as AWS, Microsoft Azure, or Google Cloud, and runs Cisco routers and Dell servers; another has Juniper network gear, IBM, and Hewlett Packard Enterprise servers, so on. An AIOps tool must learn the environment in which it operates, and it does this by absorbing the full environment’s data: logs, events, metrics, and alerts.
Benefits of AIOps
AIOps should drive down a critical metric that every service desk lives by – mean time to repair (MTTR) – by reducing how long it takes to identify and fix problems, thereby increasing customer satisfaction and service uptime.
AIOps can supplant – or at least complement – IT staff members who spend too much time on mundane tasks, such as systems monitoring, alert response, problem diagnosis, and course of action determination. If technology can do those things for humans, operations teams can devote staff hours to higher-value work and cut lower-level IT operations tasks. AIOps platforms resolve skilled IT worker shortages and high turnover in entry-level, less stimulating positions.
CIOs must identify ways to use technologies to help disrupt the business and create new business models that can deliver increased value to the enterprise. But technology executives must also continually disrupt the IT organization to identify new ways to achieve improved performance. AIOps Platforms represent an opportunity to extend to service management, performance management, and automation to revolutionize Cloud and IT operations.