Skepticism toward AI software tools is fading as more organizations adopt them

The evolving landscape of software quality assurance: Generative AI is emerging as a significant tool in managing software quality, with organizations increasingly integrating it into their quality engineering processes.

• A survey by Capgemini and Sogeti reveals that 68% of organizations are now using generative AI to assist their quality efforts, marking a significant shift in attitudes over the past year.
• 29% of organizations have fully integrated generative AI into their test automation processes, while 42% are actively exploring its potential.
• The evolution of large language models and AI tools, particularly Copilot, has enabled seamless integration into existing software development lifecycles, enhancing efficiency and innovation in quality engineering automation.

Shift in industry perception: There has been a notable change in how organizations view the value of AI in quality engineering, moving from skepticism to active implementation.

• In the previous year’s survey, many organizations were skeptical about the value of AI in quality engineering despite increased investments in AI solutions.
• The latest survey indicates a significant shift, with many organizations now moving from experimentation to real-scale implementation of generative AI in quality engineering activities.
• Experts believe this trend will continue, with further advancements expected in the application of AI to software quality assurance.

Challenges and concerns: Despite the growing adoption, organizations face several challenges in implementing AI-based quality assurance tools.

• 61% of respondents express concerns about potential data breaches associated with generative AI solutions.
• 57% of organizations cite a lack of comprehensive test automation strategies as a barrier to advancing automation efforts.
• 64% identify reliance on legacy systems as a key obstacle in implementing AI-based quality assurance tools.

Recommendations for implementation: The OpenText/Sogeti team offers several recommendations for organizations looking to leverage AI and automation in their software quality efforts.

• Take an enterprise-wide view by clearly outlining objectives and desired outcomes of quality engineering automation.
• Start experimenting with multiple AI approaches immediately to identify the most beneficial solutions without committing to a single platform or use case.
• Leverage the full range of generative AI capabilities, going beyond automated test script generation to realize self-adaptive test automation systems.
• Tie quality engineering automation to key business performance indicators, focusing on outcomes such as increased customer satisfaction and reduced operational costs.
• Rationalize quality engineering automation tools to ensure compatibility with emerging technologies like generative AI.
• Enhance quality engineering talent by incorporating more full-stack quality and software development engineers in testing roles.
• Understand that generative AI will enhance, not replace, quality engineers, but allow sufficient time for benefits to become apparent.

The future of quality engineering: Software quality engineering is undergoing rapid evolution, adapting to new challenges and opportunities presented by AI-generated code.

• The definition of quality engineering has expanded from testing human-written software to include AI-generated code.
• There is an increased volume of code and test scripts that need to be generated and managed.
• Quality engineering now requires testing software chains from end to end, encompassing a broader scope of responsibilities.

Broader implications: As generative AI continues to reshape software quality assurance, organizations must adapt their strategies and skillsets to remain competitive.

• The integration of AI in quality engineering represents a significant shift in how software is developed, tested, and maintained.
• While AI offers numerous benefits, organizations must balance these advantages with potential risks, such as data security concerns.
• As the field evolves, there will likely be a growing demand for professionals who can effectively leverage AI tools in quality assurance processes, potentially reshaping job roles and required skills in the tech industry.