Knowledge is power, and organizations gathered as much data as possible, hoping to use it for a competitive advantage. However, in most places, this strategy did not have the expected results. Many companies are just sitting on a pile of data, stored in different places and formats. Sometimes, a necessary security layer makes it difficult to process it in real-time, get relevant warnings, and seize opportunities.
Companies also gathered different monitoring tools for each of these data streams, which usually work separately from each other, thus missing the opportunity to analyze synergies.
A solution that emerged in the last decade to solve these shortages is using AI for IT operations. In 2017 Gartner gave a name to this new discipline, inspired by DevOps, called it AIOps. The applications have the power to be nothing less than a revolution.
How does AIOps work?
AIOps is, at its core, an application of machine learning which can enhance the operational part of IT. It is mostly used to monitor parameters, perform event analysis, and look for correlations between occurrences to get to the root cause of specific problems that cause bottlenecks in the processes. It also includes all automation to boost productivity without additional human intervention.
The difference between AIOps and other tools is that such a system integrates all data streams in a centralized unit and looks up for patterns across the entire input matrix.
How to adopt AIOps in your organization?
Most CXOs have some reticence when it comes to adopting yet another system, thus incurring more costs and forcing the staff to go through a new learning curve. The benefits of such an initiative have to outweigh the inconvenience of switching to this approach from old systems and the initial downshift in productivity.
Here are a few steps on how to get started with AIOps in your organization.
Start now with AIOps!
Early adopters of any technology pick the low-hanging fruits. Therefore, if you are thinking about making the most out of the AIOps, you should explore your options as soon as possible. Any organization has a lot on its table, but operational excellence should never be a second-tier priority.
As soon as you become interested, you will see that even the initial research phase takes time. It is best to first learn about AI and machine learning before selecting a provider.
Select a Pilot Project
Sometimes, organizations shy away from new technology because they are worried about the investment and deployment scale. Sound advice from experts is to avoid full-scale deployment of any new technology. Instead, try to focus on a relevant case study to learn about using that specific technology in your organization. Select a low-impact case study and use it as a sandbox to allow your teams to learn how to use AIOps in a risk-free environment without becoming worried about their performance reviews.
Have a go-to expert and a dedicated project leader
Tech initiatives fail very often because people become frustrated with adjusting to the new working way, are naturally change-resistant, and need guidance and reassurance. For an AIOps initiative’s success, you need an internal project manager to monitor the development and an external expert. The external expert can support the in-house effort with know-how and insights every time there is an obstacle. An expert can use their previous experience to pin-point potential drawbacks even before the project starts and hedge the risks. The internal project manager needs to make lists of what the team misses at each project step.
Take the platform for a drive test
An AIOps system is a must-have if you want to improve your processes, and there is not only one way to do that. You need to do your research and decide what are the features you look for in your AIOps platform so that it works best for your needs.
The market has countless options and providers, ranging from freemium to cost-prohibitive. Always ask for a demo account to get familiar with the product before buying it. It would help if you made sure the platform is compatible with your organization’s skill levels and ways of working or, at least, that you can accommodate it. Some platforms are more technical, therefore requiring AI know-how; others are more user-friendly.
Decide between custom and off the shelf
After you have thoroughly tested various options, you can take a well-documented decision if your organization is better off with an off the shelf solution, if you need some customization or if you should opt for a tailor-made option. There is no right answer to this without trial and error.
Most likely, you will have a trade-off between upgrading speed and full customization. While mass-market solutions are updated frequently and have a bundle of options added regularly, tailor-made dashboards can reflect your processes in a better way. Most organizations are somewhere in between.
Prepare the infrastructure for AIOps
Before deploying any AI initiative in your company, don’t forget to audit data sources and permissions. Next, draw the workflows and the data flow. Try to redesign these flows if necessary, to accommodate automation as much as possible. Mark any outcomes which could act as red flags to prioritize them on the future dashboard.
Prepare to expand the AIOps initiative
After the initial project is successful and you have gained experience from it, think about expanding the approach to a more sensitive area of your organization, where you could see changes impacting your bottom line.
Is AIOps the future?
Like any other technology, AIOps can never be better than the underlying strategy since it is just a tool. When adopting AIOps it helps to have realistic expectations from this as well as from your team and expect resistance. However, as soon as the AIOps platform is in place and properly used it can have a significant impact on the KPIs, preventing bottlenecks, downtime and allowing staff members to be more productive and more creative since the routine is handled by the algorithm – and here our approach on NOC / SOC alert triage is just an example.
This is a natural evolution of automation in IT and the goal should be to allow high-level workers to focus on creating value instead of firefighting.