Sometimes I feel as if I’m the Forrest Gump of quality assurance (QA). Since 1998, I’ve been through the beginning of automated integration testing and service virtualization through being a co-founder of Class I.Q. (now IBM Greenhat). I’ve been through the first phases of an automated testing center of excellence (ACOE). I’ve been there for the start of risk-based testing, and I’ve been a part of the transformation of QA from a somewhat necessary function to something that is now the core and chief concern of any company putting out quality software and apps.
It’s always been our mission to empower our customers to create amazing digital experiences that delight users and drive business success. We introduced our Digital Automation Intelligence approach that disrupts testing as we know it and puts the user back at the center to test the true UX. And we expanded our Digital Automation Intelligence Suite to use artificial intelligence (AI), machine learning, and analytics to predict business and user impacts across different interfaces, platforms, and devices.
We recently co-hosted a webinar with Bloor Research about the Future of Testing, and in it, we conducted an informal poll about artificial intelligence (AI) and testing. When we asked what everyone thought the biggest advantage was to incorporating AI into a test automation strategy, attendees overwhelmingly selected team productivity and efficiency.
The focus on artificial intelligence (AI) in general, in technology, and particularly in testing, is prompting organizations worldwide to take it seriously. It’s hard to ignore AI’s potential benefits, including improved productivity and efficiency, fewer defects, a better UX, and happy customers. And with DevOps and continuous delivery here to stay, staying relevant depends on keeping pace, which is why test automation is so critical.