The adoption of generative AI in quality engineering (QE) is rapidly expanding among global companies; however, the actual enterprise-wide implementation remains limited. The report indicates that while 89% of respondents utilize AI-based quality engineering at pilot or operational stages, only 15% have achieved successful full-scale adoption. This gap highlights the disparity between the speed of AI integration and its practical application in broader organizational contexts.
OpenText’s VP Tal Levy-Joseph emphasized that AI is redefining quality engineering, necessitating organizations to accept it as essential rather than optional. Although many companies report an average productivity increase of 19% through AI, about one-third have not perceived significant benefits. The findings suggest that beyond applying generative AI, there needs to be a strategic alignment across the entire quality engineering process, with issues like data privacy risks (67%) and integration challenges (64%) posing substantial barriers. A lack of AI and ML specialized skills is noted as a significant gap, affecting 50% of organizations.
👉 Pročitaj original: CIO Magazine
