Large-scale, generative AI models are opening up the possibility to do anything—from building applications to designing game-changing UI experiences. And as generative AI continues to make waves, its promise to revolutionize development and testing will usher in a new era of DevOps.
But with generative AI comes uncertainty and doubt, which casts a shadow on the potential of what AI can bring to your company’s development and testing teams. To help better understand AI’s potential value, here are four hidden benefits that generative AI brings to DevOps.
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1. Save time
The traditional method of test creation is generally limited to the human capacity and understanding of test requirements. This often has unintended consequences.
Generative Al tailors specific content by recommending diverse test ideas and generating test scripts. This helps developers and testers alike reduce human errors. In cases where software needs to be tested across multiple platforms, devices, and configurations, auto-generating tests will help developers and testers reduce waste, downtime, and rework. The result will be countless hours of time saved.
2. Reduce costs
Reducing costs in DevOps generally requires making decisions that reduce complexity and mitigate risks. But the lack of insight from end-to-end hinders the decision-making process—all the way from the code level to the CEO level.
Generative AI helps your company make better informed, data-driven decisions by gaining high-value insight and rapid results into areas that enable you to mitigate high-value risks. Generative Al also cuts complexity by auto-generating tests, test scripts, and test cases, making your value stream flow more efficiently.
3. Enhance quality
Escaped defects have a way of finding themselves into releases and into the hands of customers. This often leads to a flood of support tickets from upset customers and multiple patches to fix the release.
With generative Al, developers and testers can find more defects early to ensure each release meets your company’s high standards of quality. Finding and resolving problems early with AI provides you with the potential to go to market faster while simultaneously helping your teams maximize test coverage.
4. Improve productivity
The repetitive manual process of creating tests can unknowingly make tests time-consuming and brittle. Broken syntax, random data, and external dependencies are all culprits that make tests inefficient.
Generative AI improves the breadth and depth of testing with AI models capable of breaking down silos with high-volume, diverse, and realistic tests. It enables DevOps teams to automate repetitive tasks, and empowers them with more time to focus on value generating work that will improve their overall productivity.
Overcome clout about generative AI
The best way to overcome the uncertainty and doubt about generative AI is to focus on the existing things that need to be addressed in your company and consider:
- Would reducing human error, waste, downtime, and rework help you save time?
- Would mitigating high-value risks and cutting complexity help reduce excess spending and costs?
- Would catching defects early, getting to market faster, and maximizing test coverage help enhance your total quality?
- Would automating repetitive tasks and letting developers and testers focus on value generating work help improve productivity?
If you answered yes to any of those questions, it’s time to reimagine work with AI.
At OpenText, we are pioneering this new era of possibilities where generative AI complements human creativity to become tomorrow’s solutions. We recently launched opentext.ai and are using AI to predict delivery times, identify risks and gaps, and generate AI-generated test scripts that deliver high-quality software applications at unprecedented velocity and efficiency.
Backed by next-generation LLM, OpenText gives organizations the ability to harness the power of generative Al without losing control of their sensitive and proprietary data.
Discover next-level DevOps using next-generation AI with OpenText DevOps Aviator.
