A global survey of 127 managed service providers (MSPs) with at least $25 million in revenue and more than 500 employees finds that the use of artificial intelligence to manage IT operations has significantly increased. Conducted on behalf of OpsRamp, a provider of an AIOps platform, the top three obstacles to achieving steady growth and profitability were identified as the need to improve operational efficiencies (68 percent), followed by increase customer satisfaction and reducing churn (64 percent) and hiring and retaining talented employees (61 percent).
The survey finds MSPs still rely on domain-specific monitoring tools (72 percent) and manual processes to manage IT environments (58 percent), but 40 percent have also adopted AIOps to correlate events. More than two-thirds of MSPs have adopted AIOps are relying on a custom or homegrown platform, the survey finds.
Use of AIOps leads to faster incident resolution for many MSPs
The primary reason cited for adopting AIOps is to improve application availability and service (66 percent), followed by automating operations to increase efficiency and productivity (57 percent) and improving governance (55 percent).
The top three most important capabilities cited by MSPs are built-in monitoring and native instrumentation (56 percent), incident visualization (51 percent), impact visibility and service context (51 percent) and incident management (49 percent). AIOps platforms today are being used for intelligent alerting (66 percent), incident auto-remediation (57 percent), root cause analysis (55 percent) and anomaly/threat detection (49 percent), the survey finds.
The primary operational benefits cited include a reduction in open tickets (67 percent), improved mean time to detection and remediation (57 percent), automation of tedious tasks (53 percent) and suppression, de-duplication, and correlation of events (47 percent).
Finally, a total of 44 percent report they have been able to resolve incidents 50 percent faster or more, with 73 percent taking less than six months to implement an AIOps platform but only 31 percent were able to hire engineers to operate these platforms in less than six months.
Data quality concerns with AI cast a shadow
Despite these advances, there are still concerns over the quality of the data being used to train AI models. A full 70 percent said they are concerned about data accuracy, so much work needs to be done to ensure the quality of the data being collected.
In general, the survey finds the biggest technical challenges MSPs have today is understanding application to infrastructure dependencies during an IT outage (66 percent), followed closely by ensuring rapid mean time to resolution for business services (65 percent). More than half of MSPs (55 percent) are managing 10 or more tools to deal with those issues.
As larger MSPs continue to invest in AI, there is a clear danger that the gap between MSPs may grow, based on their capabilities. Smaller MSPs may not have the same resources needed to invest in AIOps. Arguably, the best way for them to close that gap is to rely on a platform that already exists versus building their own. The challenge then becomes determining which AIOps platform is best suited for the specific needs of an MSP.
At this juncture, there is no doubt MSPs will be relying more on AI in the months ahead. While an AIOps platform is not likely to have a meaningful impact on the cost of IT labor any time soon, the level of scale that MSPs can operate going forward will be substantially greater as more routine tasks are increasingly automated.
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