Survey: Adoption of AI Software Testing Slowed by Trust Issues

DevOps.com

by James Maguire

February 20, 2026

AI-Generated Deep Dive Summary

A new survey from Leapwork reveals a significant tension in software development: while AI is increasingly seen as essential for the future of testing, many teams are hesitant to fully trust it for critical workflows. Despite high enthusiasm for AI-enabled testing, concerns about reliability, transparency, and control are holding adoption back. The survey, which gathered insights from over 300 engineers and IT decision-makers, highlights that while AI offers promising benefits, such as faster test execution and reduced manual effort, teams remain cautious due to a lack of confidence in its accuracy and decision-making processes. The research underscores that AI testing tools are still perceived as "black boxes," making it difficult for teams to understand how decisions are made or to pinpoint errors when things go wrong. This lack of visibility into AI's operations is particularly concerning for mission-critical tasks, where even minor mistakes can have serious consequences. Additionally, many respondents expressed worries about over-reliance on AI, fearing that it could lead to complacency and a failure to catch edge cases that require human judgment. For those in the DevOps space, this matters because AI testing has the potential to streamline workflows, reduce costs, and accelerate delivery cycles—key priorities for modern development teams. However, overcoming trust issues is crucial to realizing these benefits at scale. Teams must find a balance between leveraging AI's efficiency and maintaining human oversight to ensure reliability and adaptability in their testing processes. Ultimately, the survey suggests that while AI is here to stay in software testing, its widespread adoption will depend on addressing transparency, control, and accountability concerns. As tools become more interpretable and teams gain experience with AI-driven solutions, confidence in these technologies is likely to grow, paving the way for broader implementation in critical workflows.

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Originally published on DevOps.com on 2/20/2026