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Should your IT operations be human-led or product-led?

It’s only with the massive advancement of AIOps technology that a question like this can even be posed. 

Traditionally, every alarm or problem notification needed to be human-driven. Once an environment was set up, a project would ensue where the company gathered its monitoring experts and the folks who did the building to discuss what scenarios would indicate that something was broken. Much time was spent brainstorming scenarios and creating alarms based on thresholds that would signal a problem. The team would call the project complete; always leaving with a feeling of apprehension because of the obvious risks of unforeseen issues that weren't configured ahead of time.


Is this human-led process still necessary or have AIOps products become smart enough to know when to notify you of a problem without you having to tell a monitoring tool what to look for? Consensus is that AI can contribute considerable value in this area, but there is still skepticism on whether it can truly deliver a product-led IT operations process. Here are the areas that the product would need to deliver to truly pull it off: 


You can’t rely on alerts as your input

Many products in AIOps are focused on consolidation and compression of alerts. There’s value to reducing alerts, and fewer alerts translates directly to operational cost savings. But, if your AIOps tool is dependent on alerts from other systems as its data input, you’re still stuck in a human-led process; relying on the project previously mentioned to define what triggers will cause an alert to fire. This isn’t product-led.



Your product must detect problems from raw data

If you can’t have a product-led IT operations process that relies on alerts, then the process needs to start by detecting problems from the raw data across your full environment. A product-led process simply sends data (logs are ideal) to the AIOps tool and the product defines a problem, alerting you without relying on you to tell it what to look for. This is the only way to seal the gaps and blind spots that exist from your team’s inability to foresee every combination of the endless number of ways things can break. This is product-led.



Your product needs to learn

Another essential aspect of a product-led IT operations process is for your tool to learn from new data and adjust automatically. In machine learning terms, this is called unsupervised learning. The unsupervised learning sees the new data, evaluates it, and adapts to it without needing human configuration or input to adjust properly.  If you need to do additional configuration anytime new data enters your system or find yourself constantly adjusting thresholds to find problems, you still have a human-led IT operations process.



Your product needs to listen

While unsupervised learning is crucial, your product also needs to listen to things you care about so it can successfully bridge the gap between technical issues and business priorities. A product-led process receives technical issues in a way that the user can easily provide feedback to teach the produce what the business cares most about.



Your product needs to recommend a solution  

One of the most obvious gaps with all other AIOps tools on the market is the lack of recommendation to fix the problem presented.  If your team receives an alert and then needs to initiate a human-led search for the resolution, you’re wasting effort and expensive time. A product-led IT operations process not only triggers the workflows in your team within ServiceNow or other ITSM tools, but it should also pull in a recommended resolution to your team can spend time fixing problems rather than searching google for the correct resolution. 


All of the aspects above are available within Sophie, Loom Systems’ AIOps tool. If you’d like to pursue a product-led IT operations process, visit us here and schedule a demo



Loom Systems delivers an AIOps-powered log analytics solution, Sophie, to predict and prevent problems in the digital business. Loom collects logs and metrics from the entire IT stack, continually monitors them, and gives a heads-up when something is likely to deviate from the norm. When it does, Loom sends out an alert and recommended resolution so DevOps and IT managers can proactively attend to the issue before anything goes down.
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