The 6 Myths (and Realities) Of AIOps

How to find real business value with machine learning, automation and predictive analytics.

The concept of artificial intelligence (AI) has been a science fiction staple since at least the 1927 German cult classic film Metropolis, in which an evil robot leads the destruction of a city. In fact, from Blade Runner to The Terminator and The Matrix series, AI in movies has mostly been about killer robots.

Thankfully, in business the reality of AI is more killer app than killer robot. AI has changed not only the technology we use in the modern organization, but also the way we think about problem solving. Ever since technology professionals first saw the potential of early AI solutions, the sky has been the limit of their imaginations.

One real-world application of AI operates at a level of speed, intelligence and efficiency that might seem to approach science fiction: artificial intelligence for IT operations, or AIOps for short. AIOps is a real, concrete, powerful tool to manage the enormous volumes of data you need to wrangle so you can start to realize the benefits of digital transformation. (And no, it can’t mix drinks, mow the lawn or solve the Wordle for you. Yet.)

What Exactly Is AIOps?

AIOps applies data, analytics and machine learning to automate IT operations. These new learning systems can analyze massive amounts of network and machine data to find patterns not always identified by human operators. (That should probably be “impossible for human operators to identify,” but we’re trying to go easy on you.) These patterns can both identify the cause of existing problems and predict future impacts. The ultimate goal of AIOps is to automate routine practices in order to increase accuracy and speed of issue recognition, enabling IT staff to more effectively meet increasing demands.

Just like every forward-looking technology topic, there are myths and realities when it comes to AI and AIOps, some simple and some a bit fantastical. (There really is a guy in Japan who married an AI-driven hologram, but we’re not going to address that one.) We’ve addressed some of the more realistic in our ebook, 6 Myths of AIOps Debunked. If you’d like a sense of what you’ll learn from it, read on. Let’s start with one of the more pernicious myths:

Myth 1: AIOps Will Replace IT Professionals

One misconception around AIOps is that these platforms are intended to replace people in the organization with intelligent software systems. Now and for the foreseeable future, there is no substitute for the knowledge and adaptability of human operators and engineers. In short, the robots aren’t coming for our jobs (unless you’re a business writer).

By eliminating mundane tasks and handling routine system alerts, AIOps solutions will improve the focus and working capacity of existing IT professionals, which they can use to adopt the next generation of technologies to drive customer value and business growth. In other words, let the machines do the grunt work so IT teams can do the brainy stuff.

Reality: AIOps will augment existing IT systems and better equip IT pros to handle growth and complexity.

Myth 2: AIOps Is All About Artificial Intelligence

While AIOps does use elements of AI, it’s not the same thing as AI. In fact, many mathematicians and data scientists continue to argue about where algorithms become machine learning and machine learning becomes AI. The good news? You don’t have to worry about it. There’s no debate about the capabilities necessary to define AIOps. An AIOps platform must be able to perform the following tasks:

• Ingest a diverse set of data
• Apply rich algorithms to identify key indicators in the data
• Notify and respond to those indicators when they are identified

Throughout this process, the AIOps solution continues to learn and become more adept not just at identifying issues, but in predicting issues before they happen.

Reality: AIOps uses a combination of machine learning and automation to deliver more effective operations.

Myth 3: AIOps Is Plug and Play

An AIOps platform can only be as good as the IT professionals who implement it. You need engineers to monitor the data being fed into the platform, understand the criticality of the applications and systems and ensure the automated workflows will be effective.

Ultimately, the true value of an AIOps platform is its ability to make human-quality decisions without a human needing to be directly involved. This requires smart algorithms written, trained and refined by smart people. (Good news if you’re a smart person!)

Reality: While many AIOps solutions can deliver quick value, there is still human effort required to fit the platform to the environment.

Myth 4: AIOps Means You Can Relax and Trust the Machines

Maybe you’re ready to welcome our new hyper-intelligent machine overlords, but don’t prostrate yourself just yet. AIOps systems can do incredible things human operators can’t. They see patterns in noise. Their ability to parse and correlate tremendous amounts of data, deduplicate system logs and notifications at large scale and to execute automated responses exceeds any human capacity. This does not mean they’re infallible and can be left unattended. (Especially if the Paperclip Problem is right.)

AIOps systems still require IT professionals to train the systems and then validate conclusions and outputs as they learn. Again, AIOps is only as good as the people who train it and the algorithms that run it.

Reality: IT practitioners and leaders need to build a strong foundation before fully automating responses and reporting.

Myth 5: AIOps Requires Data Scientists to Implement

Including data scientists in your AIOps adoption team will be beneficial as you mature your implementation and apply the platform to more complex applications or systems. (Plus having data scientists around makes your business look smart and classy, especially if they wear lab coats.) Data scientists can also validate the output of the system in coordination with IT professionals. That being said, curated algorithms in AIOps software help IT practitioners implement machine learning without requiring them to know all the nuances of the data science profession, which is a large part of what makes these systems so valuable.

Reality: Most current AIOps platforms support a large, common set of technology and processes that doesn’t require data science mastery.

Myth 6: AIOps Is Just for Operations

IT Operations teams are the primary users of AIOps solutions today, but that doesn’t mean that the value of AIOps is restricted to their use. As the lines blur between IT, Security and Development and more holistic methods are created for accomplishing key technology tasks, the value of AIOps can spread to all parts of the organization. Dev teams can use AIOps solutions to identify issues before they make it into production, for instance. With AIOps, the dev team can take ownership of this process, rather than relying on IT Operations for support.

Reality: AIOps is a new generation of shared services for everyone involved with application development or support.

So, no sci-fi, no killer robots, no hyper-intelligent computer overlords. Just a practical, data-driven tool for helping your IT team achieve its greatest potential. If you’d like more information and a deeper dive into AIOps, be sure to check out our free ebook, 6 Myths of AIOps Debunked.