When it comes to Artificial Intelligence (AI), there is certainly plenty of hype. Many companies have aggressively changed their branding to become a part of this hot space. Yet there are categories that are showing major strides with AI. If anything, the potential may even be under-hyped!
And one such area is AIOps, which is short for “AI for IT operations.” It’s about taking advantage of the enormous of amount of data generated across an organization.
Keep in mind that the Covid-19 pandemic has accelerated the growth in AIOps. After all, there has been a scramble to scale up for the growing workloads. But even as the Covid-19 pandemic fades, the momentum will continue for AIOps. According to Gartner, the exclusive use of this technology will go from 5% of large enterprises in 2018 to 30% by 2023. In other words, AIOps is a true megatrend.
So why is this so? What are the reasons companies are betting on AIOPs? Let’s take a look:
#1 – Data-Driven Decision-Making
Yes, the concept of “data-driven decision-making” is nothing new. It’s a major part of digital transformation.
But unfortunately, getting transparency with IT operations has proven to be a challenge. There are usually complex environments—with a mix of on-premises and cloud applications—and the data that is often scattered across siloes.
However, with AIOps there is a focus on solving these problems. At the core of this is getting a 360-degree view of things, which means that managers can get a better handle on what’s really happening.
But AIOps can go much further. Consider that there is something called root cause analysis. By using sophisticated AI, it is possible to find the true reasons for why IT functions are not working properly. This essentially takes much of the guess work out of the process.
#2 – Real-Time Monitoring and Detection
The amount of data in IT is overwhelming for most organizations. It’s simply impossible for personnel to provide sufficient monitoring. This means that problems can easily get worse.
AIOps, on the other hand, is built for effectively managing enormous amounts of data. The analysis is also done in real-time. In other words, the improved response time can mean better customer experiences and lower churn.
#3 – Predictions
AI is not static. It instead learns over time. This means that the models will get better at predicting and anticipating problems. Let’s face it, traditional IT is often about taking reactive approaches and engaging in troubleshooting. But this is time-consuming and expensive.
Now it’s true that AI has its faults and there will be mistakes. This is why it is important to still have a human in the loop. But for the most part, the AI will provide a much more proactive approach to IT.
#4 – Automation and Remediation
AIOps is getting to the point where an organization can develop sophisticated automations. For example, suppose the platform detects that a storage system is near capacity. In this situation, the AIOps software can take action and make the needed changes without human intervention.
The automation not only means reducing the need for human intervention but there is the benefit of better managing of resources. By having ongoing monitoring and remediation, systems should have a longer shelf life.
#5 – IT Productivity
IT talent is tough to recruit, especially for non-tech employers. This is why it is a waste to have these people essentially spend their time on repetitive and tedious activities.
Note that one of the biggest pain points is having to deal with the overload of alerts in an IT environment. Often these are mostly noise and not particularly important. So, by using AIOps, the IT personnel can spend time on value-added tasks. It will mean getting even more from an organization’s technology assets.
Conclusion
As with anything with IT, success with AIOps is not just about spinning up some software. Rather, there needs to be a focus on strong planning and change management. This is why it is a good idea to think of AIOps as similar to DevOps. It’s about rethinking core processes.
Moreover, there will be a need to make sure that the data is in the right format. There will likely need to be time to wrangle it, such as by making corrections for gaps and outliers. Data quality is absolutely essential for effective AI.
But making these types of investments will definitely be worth it. The result will be that IT will get more agile and responsive – making it so an organization can improve its top and bottom lines.
