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Artificial Intelligence

AIOps: What Is It and Why Should Your Enterprise Care?

If your enterprise is wrestling with IT vs Ops challenges, AIOps as a technology and culture choice might be the answer. It may even offer a critical competitive advantage if you become an early adopter.

How’s the performance of your enterprise’s IT department? Are your tech teams always behind the ball when it comes to managing software and infrastructure? Are they struggling to prioritize different types of issues for resolution? Is work being duplicated as tickets route to multiple teams?

If you recognize any of these issues, perhaps, like many enterprises, yours is struggling to manage a rapidly developing IT environment with tools that are no longer fit for purpose. Therefore, now might be an excellent time to consider introducing AIOps into your organization.

Maybe it’s your turn to ask questions now, like “what is AIOps?” “how can it help our business?” and “what will it take to implement it?” You’ll find some of the answers to those questions in this introductory guide to the next big thing in IT operations management.

A Brief Introduction to AIOps

Let’s start with the most obvious first question, because while AIOps as a concept has been around since Gartner coined the term in 2016, it’s only now gaining widespread attention. Indeed, if you have your eye on the industry, you’ll know that it has become something of a buzzword in IT circles, yet only around 5 percent of large enterprises have made inroads into AIOps adoption.

Buzzword or not, AIOps is no idle focus for a new wave of IT hyperboles. It’s a major concept in AI consulting offering significant practical benefits for enterprises managing fast-paced deployments of ever-more-complex and powerful software solutions. You can think of AIOps as being the IT equivalent of fighting fire with fire, or machines to manage machines.

Modern businesses manage operations with the use of increasingly smart and rapid algorithmic solutions, which are already becoming too fast and complex for human engineers to oversee. When issues arise with these solutions, people need the help of technology to identify and resolve them, and that’s the premise behind the ascendency of AIOps.

What Is AIOps?

As a term, AIOps is an amalgamation of AI (artificial intelligence) and Ops (as in operations) and therefore shortens “AI Ops” to a single word. Essentially then, you can define it as the use of artificial intelligence to manage IT operations.

The primary components of an AIOps platform are machine learning and big data. These AI elements are applied in combination with human intelligence to provide full visibility into the performance of modern business management systems.

4 stages of monitoring in AIOps, Gartner

AIOps powers the process in which data from IT ticketing and event reporting is aggregated with observational data from job logs and monitoring solutions inside a big-data application. The data is then scrutinized by advanced analytics and machine learning algorithms, to automate issue resolution and create a real-time stream of continuous insights into business solution performance.

A Tool, Not a Takeover

It’s essential to note that AIOps does not remove the need for human intervention into the maintenance and improvement of business information systems. Instead, it simply reduces such a necessity. AIOps introduces capabilities to automate routine preventative and remedial activities and to display data visualizations for IT technicians to interpret and use for more comprehensive and timely interventions when necessary.

As a term, AIOps is becoming more prominent, but what does it mean for your business and the efficiency of your IT operations?
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The AIOps Story So Far

Although Gartner essentially named AIOps as an entity and brought it to the attention of IT audiences, the history of the technology began well before 2016. Tools like Splunk have been providing business intelligence for security, compliance, and application management teams since the early 2000s.

However, until very recently, enterprises applied these tools on a situational basis, responding to the need for operational automation, identifying a solution, and deploying it in isolation. As demand for these solutions grew in line with the adoption of big data practices, software vendors began to offer IT operations platforms with integrated AI and automation capabilities.

The Catalyst for a New Culture

In 2014, Gartner released a report predicting that enterprises adopting the AIOps culture (as opposed to just deploying the technology) could increase revenue and reduce costs by doing so. That was the catalyst for a new model of IT operations management, which has continued to generate interest in the years following Gartner’s christening of AIOps as a concept.

Today that concept is coming of age, with proponents espousing the necessity to treat AIOps as an overarching IT operations strategy rather than a tactical solution for discrete scenarios. A common strategic goal of AIOps implementation is to automate as many IT Ops workflows as possible.

What Drives the Need for AIOps?

Of course, the act of naming a concept does not create a demand for it. The growth in AIOps interest is fuelled not by its acquisition of a name, but by the need for enterprises to upgrade IT Ops efficiencies. The simple reality is that human IT technicians and engineers can no longer keep up with the maintenance of technology stacks in large enterprises. Manual management is no longer tenable, and it takes a machine to manage another machine.

To illustrate the issue in a little more detail, let’s look briefly at the enterprise IT challenges that increasingly drive the need for AI and automation in operations management.

Enterprise IT Complexity

The enterprise IT landscape has become elastic, dynamic, and complex. On-premises infrastructure is disappearing, usurped by mobile technology, managed and unmanaged cloud solutions, SaaS, and third-party service providers. In such an environment, there is no place for human monitoring of day-to-day operations. Instead of keeping the lights on, IT personnel would be better deployed in the resolution of complex and exceptional issues only.

Rapid Landscape Expansion

Enterprise IT is no longer about computers. More and more formerly dumb items are connected via the internet of things, APIs are growing in abundance, and more users are joining the digital collective. In short, we’re reaching a point where IT is the business. No business can afford IT problems that linger long enough to impact user experience, but again, the human capacity to respond is insufficient, and automation becomes a necessity.

Why Does Your Enterprise Need AIOps?

How many monitoring tools does your enterprise currently employ within its various technological domains? For many companies, the answer is “a lot.” If that’s an answer with which you can identify, you might also have noted that the number of such tools has increased exponentially in recent years.

As enterprises’ technology portfolios have expanded in line with big data and IoT developments, so has the use of domain-based monitoring systems. However, as noted above, today’s businesses are typified by dependence on IT throughout their workflows. Discrete monitoring and analytics solutions can’t provide the end-to-end visibility essential to prevent issues from impacting end users or, even worse, affecting the customer experience.

AIOps, as a culture and practice, addresses this inadequacy. It involves the collection and correlation of data from disparate sources to enable holistic monitoring across all business-information domains.

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The Need for Superhuman Alertness

If bringing all the enterprise data together for correlation is the first step in AIOps implementation, the second step is necessary to address the consequence of step one. No doubt, the number of alerts demanding attention from your IT specialists is already substantial and burdensome. In a recent survey on the state of AIOps, the AIOps Exchange reported that 40% of IT organizations receive over a million alerts daily. Perhaps you can relate!

Alerts will only become more prolific when you begin to achieve end-to-end visibility. Your business will therefore need a solution to help you reduce that burden on your human resources.

An AIOps platform might be the only realistic answer to managing alerts that can number well into the tens of thousands. The use of AI for business automation is becoming essential to reduce the number of monitoring tools, along with the downtime and human effort involved in identifying and responding to system alerts.

You Can’t Be Reactive and Competitive at the Same Time

If your business depends on customer satisfaction, its competitive abilities link directly to its responsiveness—or, at least, that might have been the case once. Today, the most successful enterprises are those that can respond to predictions rather than react to actual events.

In other words, to compete effectively, your IT solutions must include predictive analytics, along with the intelligence to remediate predicted performance issues before they even materialize. An AIOps platform can give those capabilities to your enterprise, reducing the pressure on human IT resources and the risk of customer-experience hiccups.

AIOps: Potential Adoption Pitfalls

AIOps concerns, The State of AIOps Report

As with any maturing technology or concept, AIOps adoption is likely to present obstacles and challenges. However, as mentioned so often in our articles on this blog, forewarned is forearmed, and when the most predominant challenges enter into your awareness, your ability to counter them increases.

In the case of AIOps, the following elements of adoption are likely to present pain-points:

  • Data quality assurance
  • Budgeting and price comprehension
  • Lengthy implementation periods
  • Limitations of size, speed, compatibility, and scalability

It’s Only as Good as Your Data

As advanced as digital technologies have become, the adage of “garbage in, garbage out” still holds. For many companies planning to implement AIOps, data quality will present a barrier to successful deployment. As AIOps solutions must aggregate data before analyzing it, data deficiencies found anywhere in the IT environment will hinder the platform effectiveness as these deficiencies get exposed during the aggregation.

It’s Hard to Determine the True Cost of Implementation

Several AIOps vendors base their pricing on the data volume the tool will handle, and pricing schemes are not necessarily simple to interpret. Then there are all the other costs to consider, such as licensing, training, implementation, and integration.

You might even find it impossible to implement your solution without AI consultants’ help. Furthermore, not all AIOps platforms are cloud-based, so you might need to factor the cost of extra hardware into your budget.

AIOps is Not a Simple Setup

Time too is a critical element of implementation costs, and AIOps tools, in the main, are not simple to get up-and-running. In addition to a possibly lengthy time-to-production, your IT department will need to decide whether the right expertise exists within the organization. If not, it will need new hires or the involvement of third-party service providers. Don’t forget to account for the time your teams will spend on end-user training, too.

AIOps Excellence Is Not Yet ‘A Thing’

While there is already a reasonable choice of available AIOps tools, the technology is not yet at a level where the wrong choice might merely introduce some inconveniences. If you don’t select well, your enterprise could experience significant problems. The last thing you need is a solution that can’t:

  • Handle the volumes of data that you need
  • React at the speed your business requires
  • Play nicely with all your data sources
AIOps is not a light load to carry as there are at least 4 adoption pitfalls to consider. Still, the payoffs are overriding the challenges
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Prepare Your Enterprise for AIOps: Key Steps

So you’re ready to accept that despite some possible but avoidable pitfalls, AIOps is the right approach to optimizing IT management in your enterprise. First though, it will be prudent to prepare for the necessary change in culture and technology required for a successful AIOps strategy.

A methodical approach will help you prepare your business for the introduction of AIOps, and you should consider the following steps as sacrosanct for a successful transition.

Start with the Why

There can be several reasons for an enterprise to graduate from conventional IT operations to an AIOps setup. Knowing your own organization’s reasons for doing so will help you choose the right solution, focus on the correct data, and prepare your people adequately.

For example, you might wish to implement AIOps in response to any of the following issues:

  • Your IT department is overwhelmed by the volume of alerts, many of which are redundant
  • There is excessive performance degradation and service downtimes
  • Customers demand improved IT services
  • There is the need to improve system efficiency and speed up issue resolution

Of course, you might want to tackle all of the above, but there will typically be one area or issue that’s a higher priority than the rest. Before getting an AIOps project or proof-of-concept underway, your prerogative should be to know precisely why you want to do it.

Determine What Success Will Look Like

Countless stories exist about organizations investing in an IT project that failed to deliver as expected, but truth be told, a sizeable proportion of those “victims” didn’t prioritize success criteria. It's easy to categorize something as failed when you haven't articulated what success should look like when you see it.

An AIOps implementation will have far-reaching effects, so setting appropriate success benchmarks is a vital tenet in the preparation. Your success measurements will be specific to the nature of your enterprise, but areas you might focus on for goal-setting and monitoring include:

  • Prediction and prevention of downtime
  • Reduction of IT working hours
  • Reduction of domain-specific applications
  • Mean time to resolution

Deal with Data Quality

Improving the quality of structured and unstructured data is not something your business can achieve overnight. However, the fact remains that for an AIOps platform to effectively make the right automated decisions in prioritizing and responding to alerts, it must have credible data to analyze.

Again, a sensible approach would be to focus on the data that has the biggest effect on the enterprise and its customers. By segmenting your data and prioritizing the most critical segments, you can improve the impact of AIOps deployment, make some quick wins, and increase the appetite for wider-scale adoption.

A New Enterprise Necessity, Not an AIOption

As enterprise IT continues to become bigger, faster, smarter, and more decentralized, the only way to keep the machines running is with other machines. AIOps is all about taking the pressure off the IT people, who will otherwise drown in the rising tide of business digitization.

It might be early days for AIOps, but only cynics can fail to recognize that machine learning and automation will be a necessity for tomorrow’s digitally transformed enterprise. For some large organizations, it’s already the only solution to keep systems running reliably.

Your enterprise should not only know about AIOps but care about it, because it’s coming. By making ready for it today, you’ll have the edge over the cynics, and be the first on the high ground as the floodwaters rise.

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