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Ever 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 found success due to the challenges created by the rapidly growing amount of data in tech infrastructure. Now that companies and enterprises generate too much user data for traditional IT teams to monitor, AIOps is helping streamline their work.
As businesses continue to adopt digital transformation throughout their operations, IT teams and enterprises need to stay on top of AIOps trends and how this emerging tech shapes enterprise IT.
Trends Shaping AIOps
Although AIOps is growing, it’s still a relatively new space that enterprises and other businesses are exploring. For a wider understanding of the value of AIOps, it’s important to note some key AIOps trends.
Enterprise AIOps Growth
There is no question that enterprises are ushering in AI technologies with their digital transformation strategies. AIOps will be integral to enterprise digital transformation moving forward. According to a 2021 PWC report, over 50% of businesses have either fully enabled or begun implementing AI into their workflow.
IT leaders are at the forefront of this transformation. IT leaders are strategizing the use of machine learning in multiple facets of their enterprises, including sales, marketing, and security. Furthermore, enterprise operations as a whole are becoming too challenging and fast-paced for traditional practices to still be used. This is the main driver behind many digital transformations and will likely fuel the rapid growth of AIOps platforms.
AIOps and Remote Work
As AI priorities have shifted from financial analysis and consumer insight to cost optimization and customer experience in the wake of the COVID-19 pandemic, AIOps has found a new footing.
Remote and hybrid working is continually showing the potential of AIOps. According to a Transposit report, over 90% of IT, DevOps, and Site Reliability Engineering professionals reported an increase in service incidents. An even higher percentage claimed incidents take longer to resolve while working remotely. Expect business leaders to look to AIOps to address these newfound issues.
AIOps can transform enterprises that rely on remote work through a number of practical applications:
- Visibility. One of the key issues many enterprises faced during the work-from-home transition was the loss of company-wide visibility. Enterprise leaders did not have the same ability to manage work. AIOps could be leveraged to improve, and even usher in a new era of this visibility.
- Help-desk. AIOps could be a massive player in changing the way employees seek IT help for their work. If employees have issues with logging-in or other clerical tasks, AIOps could be leveraged to automate responses and fix the issue. If issues are too complex for automation, AIOps platforms can alert your IT team. This ensures IT operations quickly respond to cases, making the overall business more efficient.
- Application errors. Because AIOps uses machine learning and AI to identify and learn from problems, IT teams do not have to deal with repetitive responses regarding application errors. Instead, issues can be diagnosed, sometimes even ahead of time, to ensure 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 hands off approaches, yet 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 assisting with monitoring, testing, and security. In fact, the utilization of AIOps to marry DevOps and ITOps will most likely be an integral part of any AIOps strategy moving forward.
Although DevOps has fostered a more efficient work environment for many businesses, teams are starting to lag behind as tech and design departments scale. Both the speed and growth of companies is already proving too difficult for DevOps teams to catch up. The integration of AIOps in the DevOps cycle can assist this issue.
Observability
As previously mentioned, AIOps can help IT and DevOps teams monitor and identify issues efficiently. Because of this, AIOps can bring in a new era of faster, 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 immediately perform analytics on said data. It is the method in which IT and DevOps teams identify, assess, and tackle issues on a top-down level. AIOps helps with both scale and speed when it comes to observability. This is because it streamlines how raw data is processed and provides IT and DevOps teams with a greater view and understanding of their systems.
As of now, most AIOps tools can only handle single data types at a time. This is set to accelerate, however. In fact, AIOps tools could potentially leverage machine learning to view raw data and analyze how they relate and interact with one another.
Hyperautomation
As AIOps is accepted by various industry verticals, so is the idea of hyperautomation, the practice of integrating automation in every possible facet of business operations. This is a leading trend in 2021.
In a sense, AIOps is one of the main ways hyperautomation finds its footing in ITOps. AIOps solutions centralize data and leverage algorithms to aggregate and correlate alerts, as mentioned previously. This sort of automation is predicated on the idea that AI is not a replacement, but an assistant for employees and businesses.
Hyperautomation and AIOps continue to grow, with top analysts expecting that by 2024, organizations will leverage these technologies to reduce operational costs by as much as 30%. Expect the corresponding rise of low-code solutions in the marketplace as hyperautomation is widely adopted. These solutions are built on making automation not only easy to build, but also scale.
AI Cybersecurity
Perhaps the fastest growing implementation of AIOps has come in the form of cybersecurity. A 2020 IBM survey of over 4,000 US, EU, and Chinese businesses showed that cybersecurity was the top use case for AI implementation.
This space is already growing. Companies like CrowdStrike, Cylance, and FireEye leverage machine learning and artificial intelligence to detect malware and prevent cyberattacks. Many experts see AIOps as the next frontier in cybersecurity. Major players such as Siemens USA are already using these technologies for their cybersecurity needs as well.
AI cybersecurity solutions can be used to detect malware and potential attacks ahead of time. These solutions learn from human behavior and previous breaches to prevent any further ones from ever happening. And again, this trend will allow for business scalability above all else. Expect to see AIOps used for cybersecurity in the next few years, especially since new technologies such as the metaverse and crypto currencies – which raise fresh security concerns – are widely adopted.
AIOps Market and Solution Growth
Finally, expect the rise of both AIOps platform capabilities and players themselves to grow in parallel. The next frontier for AIOps solutions will most likely be in their ability to analyze and extract from multiple types of data at once. As of now, most solutions can only handle single 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 businesses and enterprises continue to adopt these technologies, expect the market to follow accordingly.