Over the years, there has been a growing emphasis on enhancing both the quality of software and the processes used to build it. This shift has prompted enterprises to transition from Quality Assurance (QA) to Quality Engineering (QE), linking the outcomes of the quality function with business results. The increased adoption of newer technologies such as generative AI underscores the importance of understanding their implications across processes, people, and technology, as well as the new opportunities they present for the quality function. Generative AI use cases are rising across the Software Testing Life Cycle (STLC) and in quality interventions for generative AI applications/systems. On the supply side, major participants such as Microsoft, Google, and Meta are investing aggressively to dominate the generative AI landscape. Additionally, there have been investments from leading QE-specific technology providers such as Copado, Katalon, QuerySurge, and Tricentis as well. This presents an opportune moment for enterprises to understand how the quality function can be a game changer in their generative AI journey.
In this report, we examine generative AI’s potential in QE and how QE can facilitate smoother adoption of generative AI in the IT landscape.
Scope
All industries and geographies
Market segment: QE services
Sources leveraged: analyst inputs, Everest Group research (covering recruiters, industry experts, and industry associations), ongoing interactions with enterprises and providers, and publicly available secondary data sources
Contents
In this report, we:
Analyze QE market size (split across geographies and industry verticals)
Examine QE buyer trends across various geographies, industry verticals, and revenue sizes
Identify key trends that are shaping the QE market
