As artificial intelligence continues to revolutionize industries, its application in quality assurance is emerging as a game-changer for businesses looking to maintain high performance while accelerating delivery. From streamlining workflows to predicting system failures, AI-powered QA tools are transforming how organizations test and refine their products, ensuring they remain competitive in an increasingly data-driven world.
Pradeesh Ashokan, a Senior QA Engineer at Machinify with over 14 years of experience, has worked on AI systems for healthcare, voice assistants (like Amazon’s Alexa), and smart devices. Having led QA efforts for startups like Viv Labs (now Samsung Bixby) and Riva Health, Ashokan has a deep understanding of how AI can elevate QA practices. “AI isn’t just an efficiency tool; it’s reshaping the entire QA landscape by enabling predictive, real-time, and scalable testing,” he says.
The Role of AI in Modern QA
Traditional QA processes often struggle to keep pace with the complexity and scale of modern applications, especially in sectors like healthcare and consumer technology. AI addresses these challenges by automating repetitive tasks, identifying defects earlier in the development cycle, and enhancing test coverage.
For example, AI-powered tools like Selenium and Appium can automate functional and regression testing, drastically reducing the time required for manual checks. Meanwhile, machine learning models analyze historical data to identify potential problem areas, ensuring a higher degree of accuracy and reliability. “By automating mundane tasks, QA teams can focus on higher-value activities, like improving user experience and addressing strategic challenges,” Ashokan explains.
Ashokan’s work at Riva Health underscores the impact of AI in regulated industries. Leading QA for a wireless blood pressure monitoring system, he implemented FDA-compliant automated testing pipelines, reducing regression testing time by 60% while maintaining stringent quality standards. Such innovations highlight how businesses can use AI not just to enhance efficiency but also to ensure compliance in complex environments.
Advantages of AI-Driven QA
AI-driven QA offers several key advantages that make it an essential tool for businesses:
- Predictive Testing: According to an article from Forbes, Machine learning models can forecast potential failures based on historical trends, allowing businesses to address issues proactively.
- Enhanced Test Coverage: AI enables organizations to test multiple scenarios and edge cases that would be impossible to achieve manually, ensuring robust product performance.
- Faster Time-to-Market: By automating repetitive processes and optimizing workflows, businesses can significantly reduce their product release cycles..
Ashokan adds, “The predictive capabilities of AI, combined with real-time monitoring tools, create a safety net that ensures systems are reliable and resilient under any conditions.”
Building a Future-Ready QA Strategy
For businesses to fully leverage AI in QA, collaboration between teams is essential. Ashokan, who is a published author on Nanotechnology Perceptions, emphasizes integrating QA into development pipelines using CI/CD (Continuous Integration/Continuous Deployment) workflows. “When QA becomes an integral part of the development process, you catch issues earlier and release faster without compromising quality,” he says.
Additionally, fostering a culture of innovation and knowledge-sharing is key to unlocking AI’s potential. As a judge at Hackathon Raptors, Ashokan champions the importance of collaboration in shaping future-ready QA practices.
The Road Ahead
As AI technologies continue to mature, their impact on QA will only grow. From autonomous systems capable of self-healing to tools that dynamically adapt to changes in real time, the possibilities are endless. “The future of QA lies in leveraging AI to not only test products but also to improve them continuously,” says Ashokan.
