Even after crossing the beta testing phase, QA teams find themselves stuck in long testing cycles, putting in tens of hours of manual effort before the software or a feature is ready for release. At this point, quality assurance becomes more than a checkpoint; it is the deciding factor between a successful launch that entices users and one that is forgotten.
According to a report by Gitnux, Enterprises using AI for testing have reduced their test script maintenance efforts by 65%, resulting in faster releases and more dependable software.
Sanya Khetarpaul
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AI-First QA: Building Smarter Software Testing Workflows
