World Quality & Testing Report 2026

The World Quality Report (17th Edition) explores the evolving landscape of Quality Engineering and Testing (QE&T) through a global survey of 2,000 executives across 23 countries. Published in association with OpenText and authored by leaders from Capgemini and Sogeti, this year’s report focuses on the theme “Adapting to emerging worlds,” highlighting how organizations are navigating the transformative potential and practical uncertainties of generative AI (Gen AI), automation, test data management, agile integration and enterprise systems.

Key Insights 

• Gen AI Adoption in QE&T: Only 15% of organizations have successfully scaled Gen AI across the enterprise, while 30% have deployed it operationally, 43% remain in the experimentation phase and 11% are non-adopters.
• Productivity Improvements: Organizations report an average testing productivity improvement of 19% from Gen AI, though one-third experience very little improvement (1% to 9%).
• Gen AI Top Challenges: The leading challenges in adopting Gen AI for QE&T are the difficulty of integrating tools into existing workflows (64%), data limitations and privacy risks (63%) and concerns about hallucinations and reliability (60%).
• Data Privacy Concerns: Data privacy and security risks regarding the potential exposure of sensitive data when prompting or training AI tools are cited as a concern by 67% of respondents.
• Test Automation Progress: Nearly half of organizations (47%) are transitioning toward an enterprise strategy for test automation, 14% are in the planning phase, and only 8% have a fully established, KPI-driven strategy.
• Test Automation Coverage: The average percentage of test cases currently automated stands at 33%.
• Open-Source vs. COTS Tooling: 49% of organizations use a balanced mix of open-source and commercial tools, 23% use a limited number of open-source tools sparingly, 19% rely primarily on open-source components, 5% use them exclusively and 4% do not use them at all.
• AI-Generated Test Scripts: An average of 25% of new automated test scripts were generated using Gen AI-driven tools over the past 12 months.
• Test Data Management (TDM) Gen AI Adoption: 10% of organizations are fully integrated with Gen AI platforms for TDM, 24% use them partially, 31% are piloting, 31% are exploring and 5% have no adoption.
• Synthetic Data Generation: On average, 25% of test data is generated from synthetic data, with 35% of organizations generating more than a quarter of their test data synthetically.
• TDM Ownership: Primary responsibility for test data is held by a test team or test engineers (38%), a dedicated test data team (34%), everyone in the team (12%), the environment team (8%), a third party (6%), or has no specific owner (1%).
• TDM Pain Points: The top challenges in test data include lack of data quality (51%), difficulty creating large data sets (49%), lack of accuracy (48%), compliance rules (47%), and high costs associated with creation, maintenance, and storage (45%).
• Skills Importance: For Quality Engineers, the most important skills cited are Gen AI (63%), core Quality Engineering skills (60%), development/coding skills (58%), and domain knowledge (56%).
• Agile QE Structures: Current QE structures supporting agile projects include a centralized QE organization supporting multiple agile teams (35%), a centralized testing team responsible for all testing (32%), dedicated quality and testing teams independent of agile teams (19%), quality engineers embedded within agile teams (10%), and no dedicated quality engineers or teams (3%).
• Embedding Projections: Organizations average 25% of quality engineers embedded in agile teams today, a figure projected to grow to an average of 35% in two years.
• Business Outcome Alignment: Only 25% of organizations view QE as a strategic partner with metrics directly tied to business outcomes, 38% include some strategic measures while viewing QE mainly as a delivery support function, and 37% view QE primarily as a basic testing service focused on project-level indicators.
• Enterprise Digital Solutions (ERP) Gen AI Use: In enterprise digital solutions testing, 6% are actively using Gen AI, 20% have completed pilots and plan to scale, 44% are conducting pilot projects, 24% believe the technology is not yet mature enough, and 6% are not considering it.

Conclusion

The World Quality Report 2025-26 highlights an industry undergoing a profound transition, balancing immense enthusiasm for generative AI and automation with the pragmatic realities of governance, skill gaps, and integration complexity. While technologies like synthetic data and AI copilots are reshaping workflows, true scalability remains limited to a small group of frontrunners who invest in strategic alignment, robust data privacy protocols, and cross-functional collaboration. Ultimately, the future of Quality Engineering depends on combining foundational discipline with intelligent augmentation, evolving QE from a traditional gatekeeping function into a strategic accelerator of business value and innovation.

You can check out the full report here.