Leveraging AI in the DevOps Process
By Chris White / November 29, 2022
How and why you should leverage AI and enhanced testing and process discovery in your DevOps strategy
To keep up with organizational updates and changes to an application, as well as fine-tuning modernization testing and deployment, businesses are prioritizing DevOps testing and process discovery. Testing and process discovery are not just standard steps in the DevOps process—they’re also keys to driving innovation and speed in an organization’s applications and services.
To further accelerate operations, DevOps teams are leveraging artificial intelligence (AI) to increase productivity and efficiency. AI in the DevOps testing process will equip developer teams with predictive testing capabilities, allowing teams to know what and when to test with minimal user intervention.
Leveraging AI in the DevOps ProcessAs technological innovation is propelled across organizations, executives are finding more ways to apply technology to operational processes for better business outcomes. By doing so, teams can achieve faster time-to-market for various applications, reduce human error and gain more time to focus on other business priorities. Today, organizations are finding it challenging to attract and retain talent, and staffing shortages across industries are affecting employee workloads and productivity. Implementing advanced technology solutions can help automate aspects of the job to boost worker productivity and application development speed.
In the DevOps process, tech leaders are turning to AI to enable predictive testing, allowing teams to know what to test, and when. With AI technology, QA professionals can understand what passed or failed (and why), and receive recommended improvements from AI that they can immediately put to action. This is highly valuable for DevOps teams because it alleviates the workloads of the QA teams, enabling them to focus on more strategic, higher-level work.
For an added layer of intelligence, teams can also deploy machine learning (ML) to the DevOps process. With ML, the testing process will provide development teams the knowledge of what and why something failed, and it will automatically fix the issue and run the test again. Additionally, ML-enhanced testing can learn new and improved ways of testing applications and automatically course-correct—removing the need for QA professionals to intervene.
Preparing for the Future of DevOpsGiven the benefits these technologies provide, AI and ML will play a major role in the future of DevOps. Whether it's managing deliverables, requesting approvals or any of the steps in between, intelligent processes result in lower risk and allow developers to focus on value-added work. As a result, businesses benefit from greater productivity, lower costs and improved sustainability.
To prepare to implement these technologies, organizations must ensure that they have the infrastructure in place to take on these technical implementations. It's also important business leaders prepare for change management. Undertaking company-wide changes will involve experimentation, reworking methodology and collaboration with different stakeholders (e.g., QA pros, developers, management team, etc.).
As technology continues to evolve, the DevOps process needs to be able to keep pace with changing needs. To ensure DevOps processes are modern and capable of keeping up with the complexities of today's environment, organizations are leveraging technology like AI and ML in testing and process discovery to improve worker productivity and enhance application development. Organizations that take advantage of this evolution will ultimately be best equipped to meet customer and business needs.
z/OS / AIX / IBM i / Linux on IBM Z / Linux on POWER / z/VM / z/VSE / Article / Systems management / Artificial intelligence / Data management / Machine learning / DevOps / Artificial intelligence
About the author
Chris White is the principal product manager at Rocket Software.
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