What is AIOps?

IT operations started growing in the late 1970s, largely thanks to the invention of the spreadsheet. This simple tool quickly became essential for tasks like accounting and change management. Its biggest advantage? It worked like a database.

Spreadsheets made it easy to store, organize, and get back data (mainly text) without the need for paper records. They changed how we worked, pushing computers into more and more areas of our lives.

However, early computers had their problems. They were big, heavy, and costly. Because they were so expensive, not many people used them at first. But as technology improved, computers got smaller and cheaper, making them accessible to more people. Yet, there was still a major problem: they were hard to use. And, as always, there was human error. Even a tiny mistake could mess everything up.

While the threat of big mistakes has decreased today, small errors still cause big headaches, especially in IT. Someone might spend hours just monitoring data, trying to find a small mistake that’s causing a bigger issue.

Today’s computers are cheaper, more advanced analytics more reliable, and used by many more people than before. And if we believe in Ray Kurzweil’s Law of Accelerating Returns, we can expect major tech advances soon. This means even more data to handle.

This massive growth in data and the complexity of IT systems led to the emergence of AIOps, or “Artificial Intelligence for IT Operations”. AIOps uses artificial intelligence to automate and enhance IT operations.

It helps in predicting potential IT issues, automating routine tasks, and analyzing large amounts of data quickly. This reduces human error and makes IT systems more efficient and reliable.

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.

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 and operations management tasks.

AIOps platforms resolve skilled IT worker shortages and high turnover in entry-level, less stimulating positions.

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, more predictive analytics, 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 to correlate data and, 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 platform detects 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 data analysis 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 the relevant data both 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.

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.

However, there are instances when an AIOps platform’s suggestions might be misguided. It’s crucial for human operators to provide feedback, especially regarding the effectiveness of the recommended solutions. This feedback loop refines the system’s predictive capabilities.

If an administrator finds a suggested solution unsatisfactory, they can inform the tool of their chosen course of action. Root cause analysis becomes vital here. By detailing the root cause of a problem, the system’s future predictions and solutions become more accurate. The next time a similar issue arises, the AIOps tool will be better equipped to suggest an appropriate remedy.

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, across multiple data sources including dynamic cloud activities.
AIOps Platforms with AI-driven multi-cloud operations allow organizations for performance monitoring, 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, application performance monitoring and management, and automation to revolutionize Cloud & IT operations across many infrastructure systems, storage, networks, and services/applications.

In today’s fast-paced digital landscape, the sheer volume of raw data being generated is overwhelming. Traditional data collection methods alone, like spreadsheets, are becoming obsolete in the face of the immense data loads of contemporary systems.

As businesses shift to cloud operations and adopt a remote work culture in the aftermath of the pandemic, it’s not just the tech industry that’s changing; every sector is pivoting to this ‘new normal’.

Enter AIOps: an essential tool in modern ITSM. It’s not just another buzzword but a significant advancement. Leveraging AI, AIOps automates complex processes, offering solutions beyond the basic capabilities of traditional IT tools.

Amidst emerging technologies like IoT, big data, and cloud-native applications, AIOps stands out by integrating disjointed data sources and bridging operational gaps. It harnesses machine learning to enhance the competencies of IT teams, not aiming to replace them but to bolster their efficiency.

With AIOps in their toolkit, IT professionals can sidestep mundane tasks, such as old-school change management methods, and focus on more strategic initiatives. It equips teams to handle the challenges posed by big data’s three Vs: Volume, Variety, and Velocity. The robustness of AIOps platforms allows for real-time processing of diverse and vast datasets.

Given the transformative potential of AIOps, it’s no surprise that Gartner projects a business value of $2.9 trillion from AI augmentation in 2021 alone. As we usher in this promising year, embracing AIOps in ITSM seems not just logical but imperative.

The deployment of any AIOps solution worth its salt should come with three things: domain-agnostic platform, autonomous self-learning capabilities, and Day One value. Many other benefits come with AIOps solutions, but these three are by far the most impactful.

With a domain-agnostic platform, the solution readily integrates operations data across any application stack and will adapt to new applications as the companies’ needs change and grow. Critical to the ability for the solution to be domain-agnostic is its ability to continuously and autonomously self-learn.

A self-learning solution will become fine-tuned to your company’s specific business needs solely on the newly ingested data and historical data housed in the companies’ knowledge center of choice.

Last but very much not least comes the Day 1 value of an AIOps solution. The deployment should give your IT team exact results in hours, not weeks or months. If the platform your enterprise is considering doesn’t guarantee these three components, then it is not the right AIOps solution.

Fortunately, Aisera’s AIOps platform does all three and so much more. Gain full-stack observability, active alert noise suppression, anomaly detection, and even predictive capabilities to spot future major incidents.

The solution runs on Aisera’s world-class AI and comes loaded with 1200+ remediation actions and more than 400 integrations for IT and DevOps. Aisera’s AIOps is the most dynamic, flexible, and easy-to-use solution on the market.

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, event data management, and automation to revolutionize Cloud and IT operations.