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World Quality Report 2025: AI adoption surges in Quality Engineering, but enterprise-level scaling remains elusive

Source Decision Insights Record Published November 13, 2025 Companies 2
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2 companies named across 4 categories, one of 51 articles referencing Capgemini. Previous coverage: Capgemini report shows enterprise GenAI adoption surged fivefold in two years (Nov 2025).

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OpenText, in collaboration with Capgemini and Sogeti, released the 17th edition of the World Quality Report 2025: Adapting to Emerging Worlds. The report indicated that while 89% of organizations are pursuing Generative AI (GenAI) in their quality engineering (QE), only 15% achieved enterprise-level deployment.

The report outlined a gap between organizations' interest in GenAI and their readiness to implement it. It emphasized the complexities involved in progressing from initial experimentation to full-scale application, highlighting the need for operational innovation alongside strategic oversight.

Tal Levi-Joseph, Senior Vice President of Application Delivery Management at OpenText, stated, “Quality engineering is being redefined by AI. Standing still is no longer an option – organizations must embrace AI-driven transformation to stay competitive and deliver faster with higher confidence.” Mark Buenen, Global Leader for Quality Engineering & Testing at Capgemini, noted that organizations are grappling with aligning GenAI with their business objectives, despite recognizing advancements in technology.

Key findings from the report include:

  • Widespread adoption, with 89% of organizations piloting or deploying GenAI workflows, 37% at production stage, and 52% in pilot phases.
  • An increase in non-adopters to 11%, compared to the previous year's 4%, indicating a shift towards more deliberate strategies.
  • Limited scalability, as only 15% reported enterprise-wide implementation, while 43% remain in experimental stages.
  • An emerging focus on shaping inputs in GenAI, moving towards test case design and requirement refinement.
  • Reported productivity gains averaging 19%, although one-third experienced minimal improvements.
  • Identified top challenges include integration complexity (64%), data privacy risks (67%), and reliability concerns (60%).
  • Persistent skills gap, with 50% of organizations lacking AI/ML expertise.
  • Strategic misalignment, with GenAI perceived more as a tactical enhancement rather than a strategic enabler, leading to fragmented initiatives.

Levi-Joseph emphasized the necessity for organizations to enhance their fundamental quality engineering capabilities alongside Artificial Intelligence (AI) deployment. Additionally, Homomorphic Encryption (HE) noted the significance of collaborative intelligence, merging human expertise and AI to achieve quality outcomes. The report showcased a transition towards a shift-right approach in quality engineering, complementing the existing shift-left methodologies.

Press release, provided by Cision on behalf of OpenText. Read the original.

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