GenAI adoption in quality engineering grows but scaling lags
Organisations across sectors are advancing their use of artificial intelligence in quality engineering, but only a small proportion have so far managed implementation at an enterprise-wide scale, according to new industry research.
Adoption patterns
The World Quality Report 2025, based on a survey of more than 2,000 senior executives worldwide, found that almost 90% of organisations are piloting or deploying generative AI (GenAI) in their quality engineering processes. However, just 15% of those organisations have reached company-wide rollout. Most remain at the experimental or limited-use stage, with 43% experimenting and 30% using GenAI in selected areas.
The data point to a recalibration in GenAI strategies. The proportion of organisations not adopting GenAI has increased to 11% from 4% last year, though this figure still falls well short of the 31% reported in 2023. This suggests an initial surge in interest has led to more considered approaches focused on readiness and value.
Operational focus
Respondents reported a range of benefits from GenAI adoption in quality engineering, including an average increase in productivity of 19%. Yet, one in three organisations said they had seen little tangible improvement, emphasising challenges with integration and operational alignment.
GenAI applications are evolving. While early usage centred on analysing outputs like defect detection and reporting, current adoption now places greater emphasis on shaping inputs, such as designing test cases and refining requirements.
Key barriers
Companies highlighted several factors hindering their ability to scale GenAI beyond pilots. The complexity of integrating GenAI with existing systems (cited by 64% of respondents), concerns over data privacy (67%), and the problem of machine hallucination and reliability (60%) emerged as the top challenges over the past year.
These have replaced obstacles previously seen as more strategic in nature, such as lack of validation strategy or insufficient AI skills. Skill shortages remain significant, with half of organisations indicating they lack adequate expertise in AI and machine learning, unchanged from the previous year.
Strategic misalignment
The report notes a disconnect between operational enthusiasm and broader strategic integration. Many organisations view GenAI primarily as a tactical enhancement rather than a core strategic enabler. This approach often results in fragmented efforts and projects that struggle to obtain sufficient funding or executive oversight.
Collaborative approaches
"Quality engineering is being redefined by AI. Standing still is no longer an option - organisations must embrace AI-driven transformation to stay competitive and deliver faster with higher confidence," said Tal Levi-Joseph, Senior Vice President, Application Delivery Management, OpenText.
The report also highlighted the trend towards collaborative intelligence, where human expertise and AI tools are combined to achieve quality outcomes. There is a shift in the quality engineering field, with continued dominance of the 'shift left' approach but growing adoption of the 'shift right' methodology, covering more aspects of the software delivery lifecycle.
"For organisations to unlock GenAI's full potential in quality engineering, they must invest in skills, governance, data and outcome alignment. AI amplifies capability, but it cannot substitute for it," Levi-Joseph said.
"Comparing year on year data from the World Quality Reports, Generative AI in Quality Engineering has shifted from early experimentation to strategic integration. While technical progress is clear, many organisations still struggle to align GenAI enabled quality engineering with business goals. In 2025, we're seeing more focus on governance, ROI, and cross-functional impact. The challenge ahead is closing the Gen AI divide turning investment into measurable value," said Mark Buenen, Global Leader, Quality Engineering & Testing, Capgemini.
