Study: AI a Priority for Testing Teams Even as Doubt Remains

Software testing teams are prioritizing AI initiatives, but their willingness to scale adoption depends heavily on accuracy, reliability, and the ability to maintain stable automation as systems evolve, according to new research from test automation vendor Leapwork.

The Copenhagen-based company surveyed more than 300 software engineers, QA leaders, and IT decision-makers at large and midsize organizations worldwide to assess how teams are approaching AI in testing and what factors influence confidence in its use.

AI in software testing seen as strategic priority

The study points to strong momentum behind AI-driven testing. 

Eighty-eight percent of respondents said AI is a priority for their organization’s future testing strategy, with 46% describing it as a critical or high priority. Meanwhile, 80% expect AI to have a positive impact on testing over the next two years.

Adoption is underway, but still uneven. Sixty-five percent said they are currently using or exploring AI across one or more testing activities. 

However, only 12.6% reported deploying AI across key test workflows today, suggesting many organizations remain in pilot or limited-use phases.

The findings reflect a broader enterprise trend: experimentation with AI is widespread, but production-grade implementation requires operational maturity and measurable results that many organizations have not yet found.

Accuracy and reliability remain barriers to broader AI adoption

Despite optimism, concerns around quality and stability continue to temper AI expansion in testing environments. Fifty-four percent of respondents cited accuracy and quality concerns as barriers to broader AI use.

Testing, particularly in business-critical systems, carries elevated risk. Broken or unreliable tests can disrupt release cycles and erode stakeholder trust. 

According to the study, tests that break too frequently, difficulty automating workflows across systems, and the time required to update tests are among the top challenges limiting greater automation.

Nearly half (45%) said it takes three days or more to update tests after a change in a critical system. That lag can significantly impact DevOps velocity and continuous delivery initiatives.

“It is no longer a question of whether testing teams will leverage agentic capabilities in their work,” said Kenneth Ziegler, CEO of Leapwork. “The question is how confidently and predictably they can rely on it.”

Manual effort continues to constrain QA teams

The research also underscores how much manual effort still shapes modern QA operations. On average, only 41% of testing is automated across respondent organizations.

Test creation was identified as the primary bottleneck by 71% of respondents, followed by test maintenance at 56%. More than half (54%) cited lack of time as a barrier to adopting or improving test automation.

Leapwork argues that pairing AI capabilities with stable, mature automation frameworks is key to scaling testing without sacrificing trust. 

Organizations that integrate AI alongside reliable automation, rather than treating it as a standalone solution, may be better positioned to expand coverage and accelerate release cycles while maintaining confidence in outcomes.

For channel partners and enterprise IT leaders supporting digital transformation initiatives, the findings highlight both opportunity and caution: AI in testing is advancing quickly, but operational resilience remains the deciding factor in long-term adoption.

Learn more about the trends shaping the AI industry in our latest episode of Channel Insider: Partner POV with Corey Noles and Grant Harvey, the cohosts of The Neuron.