OpenText, Capgemini, and Sogeti Tackle Generative AI Gap in Quality Engineering Report

OpenText Partners with Capgemini and Sogeti to Address Generative AI Gap in Quality Engineering

OpenText collaborates with Capgemini and Sogeti to unveil the 17th edition of the World Quality Report 2025, titled "Adapting to Emerging Worlds." This report reveals a troubling disparity between the growing interest in generative AI (Gen AI) and its actual implementation in quality engineering (QE). Despite nearly 90% of organizations venturing into Gen AI initiatives, only 15% manage to achieve enterprise-scale deployment. This stark contrast highlights a critical challenge within the industry, as organizations grapple with various barriers that hinder effective scaling of Gen AI technologies.

The report identifies significant obstacles that organizations face when attempting to scale Gen AI in their quality engineering processes. Notably, integration complexity affects 64% of respondents, while concerns about data privacy risks emerge for 67%. Additionally, a skill gap exists for 50% of organizations, indicating that the workforce may not be adequately prepared to harness the potential of Gen AI. While the majority of organizations are actively piloting Gen AI-augmented workflows—37% have reached production levels and 52% remain in pilot phases—there is a concerning rise in non-adopters, which increased to 11% from just 4% in 2024. This trend signals that, while many are experimenting with Gen AI, a significant portion of the industry still lags in implementation.

As organizations navigate through these challenges, the report emphasizes the importance of refining strategies for Gen AI deployment. Although an average productivity boost of 19% is reported, one-third of organizations see little to no gains from their Gen AI initiatives. This situation underscores the necessity for a paradigm shift in how businesses utilize Gen AI, moving from merely analyzing outputs to actively influencing inputs, particularly in test case design and requirements refinement. Tal Levi-Joseph from OpenText highlights the imperative for businesses to embed AI-driven transformations within their operations to maintain competitiveness, while Mark Buenen from Capgemini points out the ongoing struggle to align Gen AI-enabled quality engineering with overarching business objectives.

In addition to these findings, the report sheds light on the evolving landscape of quality engineering, indicating that organizations are still in the experimental stages of Gen AI integration. With 43% of companies yet to fully implement Gen AI solutions and 30% operating within limited use cases, the urgency for strategic alignment becomes more pronounced. To bridge the gap between investment in Gen AI and the realization of tangible value, organizations must adopt innovative approaches that facilitate effective integration and deployment.

Ultimately, the World Quality Report 2025 serves as a clarion call for organizations to proactively address the challenges of generative AI in quality engineering. By recognizing and overcoming the barriers to effective deployment, businesses can leverage Gen AI technologies to enhance their operational efficiency and drive significant value in an increasingly competitive landscape.