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5 Steps to Operationalize AIOps

Not long ago, Gartner quoted that they expect 50% of large enterprises to implement AIOps by 2020.  Now that 2020 is a few short quarters away, we’ve seen this massive adoption play out quite enthusiastically. 


Tech Support Cheat Sheet

Source: https://xkcd.com/627/ 


While many companies have no difficulty arriving at the conclusion that AIOps will add significant ROI, many are now focused on how to capture that ROI and operationalize AIOps within their IT processes workflows.  For this, we offer 5 clear steps to follow:


Step 1: Define priorities. In the book Measure What Matters by famous investor and OKR pioneer John Doerr, he quotes Andy Grove in saying “A few extremely well-chosen objectives impart a clear message about what we say “yes” to and what we say “no” to.”  Choose the 3 most important objectives you’re focused on accomplishing and the KPIs that will signal without debate whether or not they were reached. 


Three examples of objectives that our customers have pursued with Loom are:

  • A reduction of service tickets of 30% year over year
  • MTTR reduction of 25% in Q4 vs. Q1 as measured by resolution times in ServiceNow
  • Reduction of P1 incidents of 20% year over year


Step 2: Calculate your expected ROI from the tool.  This is an important step that is often skipped.  Inexperienced buyers consider ROI to be a tool used by vendors to convince you to buy their product, but experienced IT buyers know that ROI is most important for their internal justification process to executive budget holders and finance.  When you calculate your expected ROI before implementing AIOps, you have a clear mutual agreement on what will define success.  You win both ways: you can hold the vendor to your expectations that they deliver the value they promised and you have a common language to explain to finance why you need to continue to use the product when it’s up for renewal.  


Step 3: Start small and grow.  We recommend our customers start with 1 or 2 critical applications and perfect the proper workflows from detection to remediation, then move to the next critical applications or logical work structures within IT.  When you start with one group, you also create advocates and experts who can help future newcomers to ramp up faster. 


Step 4: Define key categories of users and expected activities to perform with the product.  Most of our customers have 3 categories of users:

  • The AIOps Manager – owner and administrator of the product, trained in usage and tuning of the product, but not a daily user. Expected to spend roughly 10 hours per month enabling the team to maximize the benefit from the tool.
  • L2/L3 support – subject matter experts and daily users of the product who analyze, resolve, escalate, and provide daily feedback to tune the machine learning. Expected to spend 15-30 minutes per day with the tool.
  • Alert receivers – L1 support and NOC employees who monitor alerts through ServiceNow, Slack, email, etc. and resolve or escalate issues. Depending on size of organization and scope of oversight, these folks can spend 30 minutes to 2 hours per day handling alerts.


Step 5: Know when to say no.  We’ve all been in situations where too many cooks in the kitchen have spoiled the broth.  The flip side of establishing 3 clear objectives in step 1 is knowing what to say no to.  AI is alluring in that its upside is enormous and it’s an exciting project to be a part of.  When people hear you’re engaged in AIOps, you might inevitably be approached by others in the organization from marketing, BI, accounting, distribution and even HR to see if they can use your tool for their analysis too.  If you want to maximize your success in an initial implementation, finish your operationalization of the product in your team before engaging with them. They’ll thank you for giving them a playbook on how to do it successfully when that time comes and you’ll be rewarded for maintaining your clear objectives and achieving them. 



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.
Get Started with AIOps Today!


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