ORLANDO, Fla. — Artificial intelligence is changing how companies build, test and improve software.
For businesses that rely on apps, healthcare platforms, smart devices or consumer technology, quality assurance has become more complex. Products are expected to work across more systems, update more often and meet higher expectations from users.
That is putting new pressure on QA teams to find problems earlier and test products more efficiently.
Pradeesh Ashokan, a senior QA engineer at Machinify with more than 14 years of experience, said AI is becoming an important part of that process.
“AI isn’t just an efficiency tool. It’s reshaping the entire QA landscape by enabling predictive, real-time and scalable testing,” Ashokan said.
Traditional quality assurance often depends on manual checks, regression testing and repeated review of product updates. Those steps remain important, but they can become difficult to manage as software grows more complex.
AI-powered testing can help by automating repetitive tasks, identifying patterns in past failures and helping teams decide where to focus attention before a product reaches customers.
Ashokan said AI can help QA teams spend less time on routine checks and more time improving the product experience.
“By automating mundane tasks, QA teams can focus on higher-value activities, like improving user experience and addressing strategic challenges,” Ashokan said.
The use of AI in QA can be especially important in regulated industries, where reliability and documentation matter. In healthcare technology, for example, software problems can affect compliance, operations and user trust.
Ashokan has worked on QA systems involving healthcare technology, voice assistants and smart devices. His work at Riva Health included QA for a wireless blood pressure monitoring system, where automated testing helped reduce regression testing time while maintaining quality standards.
AI can also support predictive testing. Instead of waiting for users or testers to find problems, machine learning models can analyze historical data and flag areas where defects may be more likely.
“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,” Ashokan said.
For companies trying to release products faster, AI-driven QA can also support shorter development cycles. Automated testing can run more frequently, giving teams faster feedback before updates are released.
Ashokan, who is a published author in Nanotechnology Perceptions, said businesses also need to make QA part of the development process instead of treating it as a final step.
“When QA becomes an integral part of the development process, you catch issues earlier and release faster without compromising quality,” Ashokan said.
As a judge at Hackathon Raptors, Ashokan has emphasized the importance of collaboration in building technology that can work under real-world conditions.
For businesses, the message is simple: AI can help companies test faster, but the real value comes when it helps teams build more reliable products.
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