AIOps implementation in the enterprise

New study explores AIOps business value and challenges

Sarah Schlothauer
© Shutterstock / Dmitriy Rezinkov

When it comes to AIOps, IT leaders are still experimenting with its implementation and use cases. A new report from OpsRamp concludes that a majority of IT leaders in the United States are happy with the value that AIOps tools provide. However, teams still face a number of challenges in the enterprise.

OpsRamp conducted a survey of 200 IT managers throughout the United States about their experience with AIOps.

What can we conclude through their data? For one, the report remarks that 87% of respondents “agree that AIOps tools are delivering value through improved hybrid infrastructure resilience, data-driven collaboration, and proactive IT operations”.

Top AIOps use cases

What are the key use cases for businesses?

According to the data, these are the top four reported uses teams find for AIOps tools:

  • Intelligent alerting/alert notifications
  • Root cause analysis/event correlation
  • Anomaly/threat detection
  • Capacity optimization
  • Incident auto-remediation

When it comes to benefits, businesses are still experimenting with how to best use these practices. However, despite its early stages, 87% of respondents are happy with the value that AIOps tools deliver.

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Currently, the top benefit reported is automated of tedious tasks, followed by suppression/de-duplication/correlation of alerts, and then reduction in open incident tickets.


With all of these positives, what is keeping businesses from adopting AIOps? OpsRamp’s report addresses some of the concerns that IT leaders have.

Firstly, businesses face challenges when hiring new machine learning engineers. A majority of respondents report that it takes anywhere from six to twelve months to hire new data science professionals.

Meanwhile, 13% say it takes even longer than a year to fill a position. About these numbers, the survey goes on to suggest that this supports internal retraining and upskilling as an alternative to new hires.

Top AIOps concerns

  • Data accuracy: Currently, machine learning is simply not accurate enough when compared to a human.
  • Skill gaps: Teams face difficulty in learning how to use new tools, especially with the speed that machine learning grows at.
  • Errors/loss of control: Handing over control and adopting autonomous systems can pose issues and concerns.
  • Lengthy implementation cycles: Adopting new solutions or tools throughout a business can take a long time and disrupt workflow. A majority (40%) of respondents said it takes three to six months to implement an AIOps solution. 25% report that it takes greater than six months.
  • Job elimination: With job automation can come department restructures and loss of jobs.

Next step for IT leaders?

Of course, it is worth mentioning that this report may come with a bias. OpsRamp is itself an AIOps platform; The survey itself was given via a third party.

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The report suggests:

“Infrastructure leaders should experiment with AIOps tools to improve the quality of service delivery, unleash the right business outcomes, and gain a solid competitive advantage. It is only a matter of time before AIOps adoption hits the mainstream and IT leaders who rethink organizational practices in line with AIOps rollouts will gain insights for reinventing their incident management workflow.”


However, readers should take the report results with caution before implementing. Be sure that it meets your needs before deployment, as the practice is still fairly new and thus, undergoing research.

Download the report and browse the information.

Sarah Schlothauer

Sarah Schlothauer

All Posts by Sarah Schlothauer

Sarah Schlothauer is the editor for She received her Bachelor's degree from Monmouth University, West Long Branch, New Jersey. She currently lives in Frankfurt, Germany with her husband and cat where she enjoys reading, writing, and medieval reenactment. She is also the editor for Conditio Humana, an online magazine about ethics, AI, and technology.

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