Top AIOps Hurdles for Companies to Overcome in the Coming Years

Due to the extensive application and integrated nature of AIOps in the whole IT ecosystem, most IT executives are approaching AIOps with high expectations

AI for IT Operations (AIOps) is a burgeoning field of technologies and strategies that inject artificial intelligence into IT operations in an attempt to solve challenges faced by IT operations teams by reducing false positives, using machine learning to spot problems before they happen, automating remediation and seeing a holistic solution from an enterprise perspective. According to a survey conducted in October, 65% of businesses are currently using AIOps and 94% believe it is “essential or very important” for monitoring network and cloud application performance. Even though AIOps are still in their infancy, they are already demonstrating their usefulness. The road to AIOps isn’t always easy. Implementing AIOps is also “tough” or “very challenging,” according to more than half of EMA respondents. Cost, data quality, IT disputes, AI distrust, skills shortages, and integration issues are among the most prevalent challenges mentioned by businesses.  

Before it is Implemented, there is No Defined Approach

Today’s IT businesses are under a lot of strain, and they may feel that they don’t have enough time to prepare properly. “Organizations, in general, are limited on time and resources,” says John Carey, managing director of AArete’s worldwide management consulting firm’s technology group. AI initiatives are frequently started as learning experiences that evolve into possibilities. “You need a plan,” Carey explains. “AIOps should be thorough and well-planned.” Doncha Carroll, a partner in the revenue development group at Axiom Consulting Partners, says that rolling out a tech solution without properly articulating the problems you’re attempting to tackle is an age-old problem for IT.  

Data that is Inaccurate or Incomplete

Data concerns, behind cost, are the second most significant barrier to effective AIOps installations, according to an EMA survey. Training data is the lifeblood of AI and machine learning. Legacy process systems, on the other hand, may not consistently capture performance data. There may also be critical components that are missing or information that is given that is conflicting. According to Gregory Murray, senior research director at Gartner, “the market is still in its first-generation phase.” “We evaluate the data we have because it is the data we have,” says the author.  

Inadequate Coverage

Because a problem in one part of the environment might have repercussions in another, businesses should bring as many systems as possible under their umbrella to reap the full benefits of AIOps. A slow database server might be causing the user experience issue, or the network issue could be a cybersecurity issue.”As more organisations employ digital technology, there are more links between applications,” Machado notes. “If the program is slow, it might cause problems on other systems.” However, there are a few stumbling hurdles in the way. One factor to consider is the cost of such a system. Another problem is integrating all of the relevant data sources so that they can work together.  

Double Payment

Individual teams or divisions with their preferred toolkit and a refusal to give it up might cause internal organisational issues. “Getting rid of alternate monitoring systems may be a political nightmare in a lot of firms,” McKeown White says. Companies commonly make concessions, leaving their existing systems in place while implementing the AIOps platform on top of them. He warns, however, that this might lead to job duplication, integration challenges, and increased expenses. “Organizations are spending too much on these technologies and not getting the results they need.” Some companies are relying on AIOps embedded in domain-specific systems to assist them to solve this problem. For example, artificial intelligence and machine learning are increasingly being utilised to detect problems in application performance monitoring systems.  

The Whole Picture isn’t Clear

Domain-specific systems can automate their operations natively and make AI technologies invisible to users. However, while preserving silos to avoid integration issues, businesses will miss out on AIOps‘ full potential. “You should be able to communicate to the networking system, to the application server, to view all the different regions if you’re trying to perform anything like root cause analysis to increase latency,” Shimin explains. “No one wants to make a Jupyter notebook to look at network logs to discover what went wrong with response time.” Finally, the cloud provider may be able to give a complete set of AIOps capabilities, which may be beneficial for firms that all utilise the same cloud provider.  

Transformation of Culture

Finally, many businesses discover that their staff lack faith in AI systems or are resistant to change. Even at firms with the highest degree of success with AIOps, 22% of respondents claimed “fear or distrust of AI” was the main hurdle to their AIOps projects in the EMA poll, with “talent deficit” coming in fourth place. Sanjay Srivastava, a chief digital officer of Genpact, a global digital transformation company, said, “In the list.” “We’re attempting to explainable AI to break things down, but it works in certain areas and doesn’t in others. AIOps is fast improving to the point that it can make corporate operational choices like traffic redirection, resource reallocation, and instance rotation automatically.