AI and ML

How Artificial Intelligence (AI) is Empowering DevOps Automation For Enterprises

The COVID-19 pandemic has accelerated technology adoption for enterprises like never before. It has helped several businesses to increase speed and stay competitive as their customer demands evolve. Though the adoption of innovative technology has enhanced business operations across supply chains, customer services, and backend processes, the resources or employees are being pushed to their limit to keep up with the change. This sudden demand for enterprises to adapt to a swiftly changing environment requires dynamic responses, efficient workability and also good infrastructure.

This makes it quite evident that not just the technology or platforms require transformation but also the IT systems sustaining the business processes need to evolve tremendously. With digital solutions and remote working being widely adopted, enterprises are forced to develop applications/software and everything digital at a faster pace. Virtual solutions that previously took months to be developed will now have to shorten the development cycle and be deployed at a faster time to market.

This faster time to market and reduced development phase can only be efficiently achieved by distributed teams collaborating with the adoption of DevOps and Agile methodologies.

The Combined Powers of Artificial Intelligence (AI) and DevOps

Artificial Intelligence (AI) is not a new technology, it has shown infinite possibilities in transforming not only every perspective of business processes but has also significant potential in software development. AI is capable of improving automation across different domains as well as streamlining maintenance methodology by leveraging DevOps.

Artificial Intelligence (AI) and Machine Learning (ML) can efficiently accumulate data produced from multiple software engineering and CI/CD tools, design models, identify patterns to predict malfunctions and implement self-driving and self-healing solutions.

Artificial Intelligence (AI) and DevOps combined together can be integrated into any framework and has the potential to increase the performance of information technology devices. The advantage of Artificial Intelligence (AI) is that it enables DevOps teams to discover better code and deploy their software. Moreover, AI can enhance automation, resolve predicaments efficiently and manage issues seamlessly. Therefore, together they can be considered as business-driven strategies to build applications at a quicker pace by incorporating software development and operations. They can help enterprises by predicting the outcome of an application, hence enabling businesses to make quick and easy modifications.

Enterprise Benefits of Artificial Intelligence (AI) and DevOps

DevOps presents an effortlessly relevant business-oriented approach and combinations of Device Analytics with Artificial Intelligence. While utilizing artificial intelligence-powered software, DevOps teams can leverage hundreds of data points that enable them to streamline test automation services, coding, deploying, and product tracking. Not only does it improve the overall accuracy and performance, but it also reduces the time needed as well as the number of resources required.

There are several innovative tools provided by AI service providers that enable enterprise IT teams to map and integrate data with the evolving business demands. This therefore significantly enhances strategic business resolutions and creates a nice consumer experience.

Some of the benefits of combining Artificial Intelligence (AI) with DevOps are as mentioned below:

Increased access to data

The most significant problem regarding DevOps is the inattentive nature of enterprises that does not provide deployers unfettered access to data. This makes it almost impracticable for business users to leverage data, ultimately making it exceedingly challenging for businesses to partner with them. To overcome this, enterprises can implement AI-powered data processing technologies for the development and operation teams, such that AI will manage data without human intervention.

Timely Notifications and Alerts

An enterprise must ensure that its DevOps teams are provided with reliable warnings and alert methods that let the team identify errors without delay. There are occasions in testing where hundreds of alerts or notifications come in without any priorities on which error is critical and which is not. Such an immense quantity of warnings will have a detrimental influence on the productivity of the team as they will have to spend hours trying to sort out the errors. With the help of AI, the development team can prioritize the warnings based on the information gathered from preceding activities, such as the severity of the warning, and the source of the alerts. As a result, it will significantly help the DevOps team to interpret massive data sets with accuracy.

Detecting anomalies

For every business, proper support of security systems is imperative. With artificial intelligence-DevOps, encrypted data environments can be generated where only those with authority will be able to access the system. The approval to admittance will only be given if the user is verified and has to access it with the help of AI-powered integration tools. Together with the combination of AI and DevOps, enterprises can seamlessly eliminate data breaches and thefts across every domain of their business.


Artificial Intelligence (AI) is already performing a revolutionizing role across multiple domains and enterprises that are enabled by information technology. Its potential to enhance operational processes that comprise DevOps will significantly mimic human behavior, therefore increasing team productivity and eliminating manual errors. It is time for enterprises to unleash this potential of AI and improve adoption, workforce productivity, and eventually client relationships.

Ricky Philip

Ricky Philip is an industry expert and a professional writer working at ThinkPalm Technologies. He works with a focus on understanding the implications of new technologies such as artificial intelligence, big data, SDN/NFV, cloud analytics, and Internet of Things (IoT) services. He is also a contributor to several prominent online publishing platforms such as DZone, HubSpot and Hackernoon.

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