But considering that an estimated 85% of AI projects fail to deliver on their goals, it's clear that many software development organizations are struggling to understand what skills actually help their teams harness the power of intelligent technologies. Today AI and ML are helping quality teams by ensuring that tests are only run when the application reaches the correct state, making sure that developers and testers can dedicate more time to fixing defects rather than investigating accidental failures. These advanced reporting features help QA teams efficiently identify small changes or errors –and ensure that anomalies are addressed before they lead to more severe issues. But as important as artificial intelligence and machine learning are to the future of software development and quality engineering, most QA professionals are too busy to become AI experts. To maximize their time, effort, and skillset, QA teams are better served by mastering key artificial intelligence and machine learning fundamentals that will enable them to start embracing advanced testing techniques and AI-based solutions as quickly as possible. But considering that an estimated 85% of AI projects fail to deliver on their goals, it's clear that many software development organizations are struggling to understand what skills actually help their teams harness the power of intelligent technologies.

Testing solutions that use artificial intelligence help development teams more easily analyze and understand where and what to fix and gives them the ability to more easily analyze and aggregate terabytes of data generated from automated tests. It is no secret that the pandemic has fueled a permanent shift to customer-centric, digital-first experiences, making it essential to provide flawless applications. As such, the field of QA and software testing has become central to building successful development organizations. Innovations such as artificial intelligence (AI) and machine learning solutions that uplevel and automate a number of testing scenarios are becoming necessary to keep up with the growing demand for continuous testing. This includes helping teams to prioritize testing more effectively and only testing new features or pages that are being widely utilized by customers (to save time!).