The Impact of AI on Proactive Observability in the Workplace The Impact of AI on Proactive Observability in the Workplace
Observability is a critical capability for any engineering team. Getting insight into your system allows you to detect and resolve issues... The Impact of AI on Proactive Observability in the Workplace

Observability is a critical capability for any engineering team. Getting insight into your system allows you to detect and resolve issues quickly — and it’s an essential part of DevOps best practices. However, as your applications grow more complex, detecting problems with static monitoring tools becomes increasingly difficult.

That is why AIOps has emerged as a powerful proactive observability tool. It helps organizations identify problems before they occur.

Here’s a closer look at how AI is helping companies become more proactive in observability.

Better Variables Management

There’s a lot of variability between configurations, architectures, and use cases, and tech teams can use AI and observability to properly manage variables.

From there, AIOps can automatically correlate events according to their context and data across systems using machine learning models. This can eliminate costly learning curves and keep all information organized in a single, curated platform.

Focusing on Higher Value Tasks

Another way AIOps benefits tech teams is by freeing up their time from low-value tasks. That way, they can focus on more duties of higher value.

Most industries are dealing with a skills shortage, but those that aren’t are growing rather quickly and require the right skills. 

For instance, the work of an IT worker or network administrator is planning-oriented and strategic. Other times, it’s routine. An example would be manually intervening to expand application workloads if demand increases for a reason. They also might reduce them to prevent organizations from wasting resources.

AIOps and observability can detect these demand spikes automatically and optimize workloads accordingly. In turn, the top AI talent can focus on more important parts of the highly specialized job.

Preventing Incidents

AI can aid in preventing incidents from happening in the first place. Proactively monitoring systems and gathering data allows AI to provide insights that enable organizations to predict risks and prevent problems from occurring.

This is especially important for companies moving toward DevOps practices, where production environments have become more automated and continuous delivery pipelines are more complex.

AI can help prevent incidents because it can detect issues faster than people who may be focused on other tasks — or unable to identify problems due to insufficient data or access.

Fixing Issues Quicker

AI offers another advantage when it comes time for an organization’s incident response team to fix an issue after it has already happened — a process known as postmortem analysis. It can determine root causes faster than human analysts could do alone.

Root cause analysis (RCA) involves investigating why a problem occurred so you can apply measures against similar issues in the future. However, RCA often takes up valuable resources during critical periods when management teams need them most.

AI enables organizations’ RCA teams to review past events quickly. They use machine learning algorithms trained on historical data sets containing information about previous patterns and outcomes. There’s no need for expensive labor resources dedicated specifically toward this purpose.

Fostering Predictability

A proactive observability model focuses on identifying problems before they happen. You can use various tools and methods to do this.

For example, you can use machine learning algorithms to predict when faults will occur based on historical data. If a problem has been identified as likely to happen, you can use AI techniques such as deep or reinforcement learning to take action immediately.

This might include taking preemptive actions such as shutting down operations until repairs are completed or reallocating resources from areas where there is no perceived risk of failure.

On the other hand, a reactive observability model takes a different approach to monitoring and diagnostics. Problems are identified by analyzing the data generated by the system and then trying to determine the cause.

This can lead to a lengthy, time-consuming process of trial and error where you test various hypotheses about what might have gone wrong. Then, you must wait until you find one that fits your observations.

The Future of Work in a World That’s Highly Automated

The impact of AI on the future of work will be significant as people continue to automate more tasks. In fact, according to a recent report from PwC, AI could add $15 trillion to global GDP by 2030. That’s an increase of 14% over current projections that include no artificial intelligence.

The skills gap is the biggest concern for those worried about how automation will affect their livelihoods and careers. Automation may eliminate some jobs, but others will change in nature. 

As technology advances, it will continue to support humans by augmenting tech teams to make sense of complex architectures. 

AIOps enables companies to make the most of their resources and focus on maximizing conversions and business growth — and become real-time, data-driven organizations.

AI Innovates Better Processes

The move toward proactive observability is good for industries as a whole. Companies can use their resources more efficiently — meaning less time spent on manual tasks and more time spent solving problems.

Engineers also benefit from this shift. They can focus on building better products instead of spending hours looking for bugs or manually investigating errors.

April Miller

April Miller

April Miller is a staff writer at ReHack Magazine who specializes in AI, machine learning while writing on topics across the technology sphere. You can find her work on ReHack.com and by following ReHack's Twitter page.