AIOps Trends | eWEEK

Since Gartner coined the term in 2017, the combination of artificial intelligence software and IT operations known as AIOps has grown in importance. AIOps has been successful due to the challenges created by the rapidly growing amount of data in the technology infrastructure. Now that businesses and enterprises generate too much user data for traditional IT teams to monitor, AIOps is helping to streamline their work.

As companies continue to embrace digital transformation across their operations, IT teams and businesses must stay abreast of AIOps trends and how this emerging technology is shaping enterprise IT.

Trends shaping AIOps

Although AIOps is growing, it is still a relatively new space that companies and other businesses are exploring. For a broader understanding of the value of AIOps, it is important to consider some key trends in AIOps.

Business AIOps Growth

There is no doubt that companies are introducing AI technologies with their digital transformation strategies. AIOps will be an integral part of business digital transformation going forward. According to a PWC 2021 Report, more than 50% of companies have fully enabled or started to implement AI in their workflow.

IT leaders are at the forefront of this transformation. IT leaders are developing strategies for using machine learning across multiple facets of your businesses, including sales, marketing, and security. Additionally, business operations as a whole are becoming too challenging and fast-paced for traditional practices to continue to be used. This is the main driver of many digital transformations and will likely drive the rapid growth of AIOps platforms.

AIOps and remote work

What AI priorities Moving from financial analysis and consumer insights to cost optimization and customer experience in the wake of the COVID-19 pandemic, AIOps has found a new foundation.

Remote and hybrid work continually shows the potential of AIOps. According to a Transposition report, more than 90% of IT, DevOps, and Reliability Engineering professionals at the site reported an increase in service incidents. An even higher percentage say that incidents take longer to resolve while working remotely. Expect business leaders to turn to AIOps to address these new issues.

AIOps can transform companies that depend on remote work through a number of practical applications:

  • Visibility. One of the key issues many businesses faced during the work-from-home transition was the loss of visibility across the business. Business leaders did not have the same ability to manage work. AIOps could be leveraged to enhance and even usher in a new era of this visibility.
  • Help desk. AIOps could be a major player in changing the way employees seek IT help for their work. If employees are having trouble logging in or other administrative tasks, AIOps can be leveraged to automate responses and troubleshoot the problem. If the issues are too complex for automation, AIOps platforms can alert your IT team. This ensures that IT operations respond quickly to cases, making the overall business more efficient.
  • Application errors. Because AIOps uses machine learning and artificial intelligence to identify and learn from problems, IT teams do not have to deal with repetitive responses regarding application errors. Instead, problems can be diagnosed, sometimes even ahead of time, to ensure that errors are resolved.

DevOps and AIOps integration

DevOps is a term used to define a type of agile relationship between IT and development operations. These groups used to rely on more practical approaches, but DevOps ushered in a culture of collaboration between the two, improving productivity.

The question of how DevOps and ITOps will work together has been growing. Expect to see AIOps as a key player in this question. AIOps can help streamline the agile stages of DevOps, specifically by helping with monitoring, testing, and security. In fact, using AIOps to bridge DevOps and ITOps will likely be an integral part of any AIOps strategy going forward.

Although DevOps has fostered a more efficient work environment for many companies, teams are beginning to lag behind as technology and design departments scale. Both the speed and growth of businesses are already proving too difficult for DevOps teams to catch up. Integrating AIOps into the DevOps cycle can help with this problem.

Observability

As mentioned above, AIOps can help IT and DevOps teams efficiently monitor and identify issues. Because of this, AIOps can bring a new era of faster and more efficient architectures. This all starts with how AIOps helps with observability.

Observability refers to the ability to view raw data, such as metrics, traces, events, and logs, and perform immediate analysis on that data. It is the method in which IT and DevOps teams identify, evaluate and address problems on a top-down level. AIOps helps with both scale and speed when it comes to observability. This is because it streamlines the way raw data is processed and provides IT and DevOps teams with a better view and understanding of their systems.

As of now, most AIOps tools can only handle individual data types at a time. However, this will accelerate. In fact, AIOps tools could take advantage of machine learning to view raw data and analyze how they relate and interact with each other.

Hyperautomation

As AIOps is accepted by various industry verticals, so is the idea of ​​hyper-automation, the practice of integrating automation into every possible facet of business operations. This is a leading trend in 2021.

In a sense, AIOps is one of the main ways that hyper-automation finds its foundation in ITOps. AIOps solutions centralize data and take advantage of algorithms to aggregate and correlate alerts, as mentioned above. This type of automation is based on the idea that AI is not a replacement, but an assistant for employees and companies.

Hyper-automation and AIOps continue to grow, with leading analysts expecting that by 2024, organizations will leverage these technologies to reduce operating costs by up to 30%. Expect a corresponding increase in low-code solutions on the market as hyper-automation becomes widely adopted. These solutions are based on making automation not only easy to build, but also easy to scale.

AI cybersecurity

Perhaps the fastest growing AIOps implementation has come in the form of cybersecurity. TO IBM 2020 survey of more than 4,000 US, European and Chinese companies demonstrated that cybersecurity was the main use case for AI implementation.

This space is already growing. Companies like CrowdStrike, Cylance, and FireEye take advantage of machine learning and artificial intelligence to detect malware and prevent cyberattacks. Many experts see AIOps as the next frontier in cybersecurity. Major players like Siemens USA are already using these technologies for their cybersecurity needs.

AI cybersecurity solutions can be used to detect malware and potential attacks early. These solutions learn from human behavior and past violations to prevent more from happening. And again, this trend will allow business scalability above all else. Expect to see AIOps used for cybersecurity in the coming years, especially as new technologies like the metaverse and cryptocurrencies, which raise new security concerns, are widely adopted.

Market growth and AIOps solution

Finally, expect the increased capabilities of the AIOps platform and the players themselves to grow in parallel. The next frontier for AIOps solutions will likely be in their ability to analyze and extract multiple types of data at the same time. As of now, most solutions can only handle individual data types at a time.

As these solutions continue to advance technologically, so does the market. Mordor Intelligence reports that the AIOps market is expected to grow from $ 13.51 billion in 2020 to over $ 40 billion in 2026. Companies like Moogsoft, BigPanda, BMC and Splunk are leaders in this market growth. As modern companies and businesses continue to embrace these technologies, the market is expected to follow them accordingly.

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