What Will APM Look Like in the AIOps Era?

Historically, enterprise IT organizations have turned to application performance monitoring (APM) systems to monitor and manage critical applications. However, throughout the world, enterprise organizations are suffering massive and systemic failures at an increasing rate.

One of the main reasons these failures are increasing is that organizations aggressively seek to execute digital transformation initiatives. Unfortunately, this has expanded the complexity and transiency of the technology stack as it is continually growing and changing. This also increases the risk of service disruptions.

Traditional APM: Built for a Different Time

The operational gap caused by this increased complexity is catching many enterprise leaders unprepared. After investing heavily in traditional APM solutions, they thought they were safe. Why then are these solutions no longer sufficient?

The reason is that traditional APM solutions were built for a different time—a time when technology stacks were much less complex and more predictable.

When tech companies first introduced the idea that organizations could monitor critical applications, it was a welcome breakthrough. It would be a deep and invasive undertaking, involving code-level instrumentation, but the return and value it offered were well worth the trouble and cost.

To make traditional APM solutions possible, three things had to be true:

1. The application stack had to be relatively static. The risk of the application suddenly changing or being decommissioned would be low after investing in instrumentation.
2. Since organizations couldn’t monitor every application, they had to choose which ones to monitor. Organizations had to be able to easily distinguish between critical and non-critical applications so they could invest resources only on a small subset of their application stack.
3. The monolithic nature of the technology stack at this early stage meant that it was also a reasonably simple task to identify and map the infrastructure elements supporting those critical applications.

None of that is true in today’s world.

Stacks in modern enterprise technology are in a constant state of change and becoming more intertwined and interconnected, both at the application level and the infrastructure level. This means that essentially, all applications are critical since there are so many points of failure. Therefore, it’s easy to see why the resource-draining instrumentation architectures of APM solutions are struggling to deliver on their promises and why enterprise APM systems must evolve.

The Evolving APM

The broad idea of APM must evolve. However, traditional APM solutions still have a place in the modern enterprise. Traditional tech solutions still provide significant value when an organization has critical applications requiring deep, code-level recording and transmitting data. Most enterprises already have these applications instrumented and monitored with traditional APM solutions, so changing this would be pointless.

The point is that this just isn’t enough anymore.

Many organizations are covering very little of their application stack with traditional APM solutions. The problem is that non-critical, non-instrumented applications or infrastructure elements can cause a serious system disruption, but organizations have little transparency regarding anything past the limited subset of possible critical stacks.

All the while, technology also powers the majority of customer engagement. Parts of the system that would not ordinarily be considered critical may now become critical at various points of the customer experience.

This change compels enterprises to acknowledge that in this complex, interconnected scenario, they need to monitor everything, including every application and their underlying infrastructures, in real-time. This means enterprise leaders must evolve their way of thinking about APM to reflect a more holistic and complete view of instrumenting, monitoring, and managing the entire technology stack from a business perspective.

Organizations can’t leave any room for the unexpected to happen that could have a significant impact.

The AI-Powered Evolution of APM

The biggest challenge for application and operational leaders is accomplishing this eye-on-everything approach without crushing the enterprise under the weight of the overhead of a traditional APM. IT operations faced the same problems with the growing complexity of IT solutions. This challenge has prompted a re-organization in how IT operations are managed and uses various forms of artificial intelligence and automation technology – enter AIOps solutions.

AIOps uses artificial intelligence (AI) to make IT operations management simpler and automate problem resolution in highly complex modern IT environments. This solution embraces technology that works with humans, not in place of them, to improve performance, efficiency, and innovation. By applying intelligent automation across data, workflows, applications, and systems, organizations can effectively optimize costs and securely scale operations.

The same operational patterns that support the transition to AIOps can be used for the AI-powered evolution of APM. Despite the technology stack’s growing complexity, every interaction between the elements leaves a digital breadcrumb representing a pattern of interaction. Even though these minute bits of data are too vast and obscure for a human operator to decipher or employ, they are a goldmine of information for machine learning algorithms that can pinpoint critical patterns, especially those consolidated in an operational data lake.

The tech team can then create consolidated data sources that can be analyzed and patterns identified. It’s critical to identify patterns to enable a more componentized approach to the development and deployment of applications so that technologies, such as micro-services and containers, can be leveraged to their fullest potential. While this is essential during the modernization and transformation stage, this approach also results in greater transiency, making it increasingly difficult to instrument and monitor modern applications.

However, machine learning and the methods employed by AIOps can also be used to identify the context of a relationship between operational patterns and business outcomes. The ability to utilize technical indicators as signals relevant to the business is essential to organizations wanting to instrument and monitor the entire technology stack without overwhelming their operational capacity.

To accomplish this, enterprises need to leverage hybrid management systems that use various techniques to collect operational data and use machine learning tactics and other forms of AI to distinguish repetitive operational patterns and show a connection between them and business outcomes. These types of hybrid systems utilize technology agnostic and data-centric approaches to enable enterprises to tighten the operational gap traditional APM solutions leave open.

Digital Transformation

In the past, members of IT leadership teams would have numerous discussions and debates to figure out which applications were critical and which were not. Sometimes the discussions were straightforward, but with high management overhead and other expenses, instrumenting applications with outdated APM tools could only instrument a small portion of their vast application portfolio.

Most of the time, applications were sitting just at the edge of criticality, and that’s when debates would ensue. These situations could become political and sometimes include backroom deals to make sure certain applications would make the cut. Luckily, those days are long gone.

IT professionals would never think about having that type of conversation today. A complex technology stack embedded into every facet of the business architecture makes it impossible to determine what is the most critical and what might be considered irrelevant. Even if the line could be drawn, it would still be meaningless because the fast pace of change in the market today would mean these conversations and debates would be a constant occurrence.

Application performance monitoring (APM) is still critical. More so than ever, perhaps. The issue is that it has become extremely vital for organizations to monitor and manage the performance of every little piece. Everything is now critical. This makes it nearly impossible for organizations to predict where the next problem might surface or where the next major outage may occur.

All of the traditional approaches to APM are too expensive and cumbersome to make this either an operationally or economically feasible method of operation for this purpose. Therefore, the only real solution is for organizations to reevaluate their view of APM and evolve into a more encompassing and holistic tactical approach that includes all of the traditional, code-instrumentation processes of APM, plus more modern hybrid methods.

This all-encompassing view makes it possible to extend the value and benefits of APM across the entire stack. This makes it very important to acknowledge that this is not so much a technical issue but more of a shift in thinking. Enterprise leaders must move beyond trying to put data into critical and non-critical technology stacks, as was the practice in the past.

Now, they have to accept the fact that today’s complex environment and constantly changing market requires an entirely new, innovative approach to APM. This approach presumes every element is critical, leverages the best of AI to manage the complexities, employs adequate automation to keep up with the fast pace of changes, and puts more focus on business outcomes.

What Is the Current State of AIOps?

The current state of AIOps uses the best of artificial intelligence (AI) combined with machine learning to enhance quality, enable data-driven business decisions, reduce unnecessary manual labor, optimize IT resources, and empower employees. Automation working in conjunction with employee expertise:

• Provides AI-powered observation, precise analytics, and visualization across applications and infrastructure.
• Creates, discovers, and automates business workflows.
• Streamlines machine-to-machine communication and data management processes.
• Accelerates the delivery of applications, deployment, and management configurations.
• Offers preemptive and reactive remediation.
• Lowers the cost of operations, boosts performance, and enables innovative initiatives.

There are a few primary use-cases enterprises are trying to solve with AIOps. First off is a reduction in false positives. When you take into account the amount of time and resources wasted investigating each alert, it is easy to see that there must be a better way of handling telemetry.

Another big one is a better understanding of a digital user. This does not mean just from an IT perspective but also includes sales, marketing, and product development. Having the capability of mapping out the user’s journey and tie this into user sentiment goes a long way in helping to build stronger customer relations and deliver a better customer experience.

However, automation may be the biggest part and greatest benefit of AIOps. Many IT teams spend far too much time and resources doing repetitive, low-level tasks that should be automated remediations. The more situations where an enterprise can implement automation, the more efficient and effective teams can be in clearing incidents.

There are two major challenges organizations face when adopting AIOps – culture and ROI showings. If an enterprise wants to make the transition to AIOps properly, it must change the way teams engage and interact with certain areas of responsibility and the process as a whole. All these things must be carefully planned before the rollout phase begins.

When it comes to ROI showings, the enterprise doesn’t have a choice other than to change to AIOps. Software is expensive, but the cost of not taking action is much greater. Lost productivity from fighting fires, loss of revenue, and the inability to meet requests from customers all add up quickly. An AIOps initiative virtually pays for itself.