While software development teams have accelerated delivery through the wider adoption of AI over the past year, many organizations are struggling to maintain confidence in software quality as increasing scale and complexity introduce new risks into the software development lifecycle (SDLC), according to Tricentis’ Quality Transformation Report.
“Accelerating business transformation initiatives is one of the top priorities for today’s C-suite and AI has the potential to help software development teams move faster than ever before,” says Kevin Thompson, CEO of Tricentis. “However, with increased speed comes increased risk. When software quality processes fail to keep pace with development speed, organizations often respond by taking shortcuts that materially degrade or reduce confidence. Our research highlights the growing pressure teams are facing to balance speed, quality and control as software development accelerates. As risks like financial performance and customer trust become more visible and measurable, software quality can no longer be treated as just an engineering concern. It must become a boardroom imperative.”
Key takeaways:
- Despite significant AI advancements and increased adoption of AI tools, six in 10 organizations still report deploying untested code, remaining consistent with 63% in 2025. The difference is in 2025, organizations largely attributed this to accidental quality slips (40%). Now, organizations admit that they are knowingly deploying untested code: largely driven by leadership pressure to prioritize speed over quality (32%), and the sheer volume of AI-generated code becoming too overwhelming for teams to test fully (30%).
- More than half of organizations across every major industry surveyed reported deploying untested code to production, with financial services (64%), retailers (63%), and energy and utilities (58%) operating under the greatest strain.
- Nearly half of organizations (48%) have fully implemented AI internally, but of those organizations, more than 50% report that their AI tools and processes regularly change. One-third of teams (33%) cite this tool complexity and sprawl as a key barrier to achieving continuous software quality at scale. Other top barriers include skills gaps (33%), code volume increasing faster than they can manage (28%), and a lack of clear quality and trust metrics (26%).
- What’s considered AI progress in the boardroom may feel more like operational friction to software teams. More than four in five CEOs (81%) report high confidence in AI-driven systems and tools, compared to just 56% of QA and DevOps professionals. Similarly, 44% of C-level executives believe their business is very prepared to operationalize, govern, and scale AI agents across the SDLC, compared to just 23% of QA and DevOps professionals.
- While 83% of organizations trust agentic AI to make release decisions and 82% say they are prepared to operationalize and govern AI agents at scale, many continue to struggle with untested code (60%), tool sprawl (33%), security concerns (27%), skills gaps (24%), and data quality issues (24%).
- One in five organizations (20%) report losing more than $1 million annually due to poor software quality, driven primarily by security and compliance failures (30%) and technical debt and rework (28%). Nearly half (45%) estimate losses between $500,000 and $1 million.
“Many organizations are still relying on quality processes that weren’t designed for software development in the AI era,” adds Thompson. “As development accelerates, leaders need clearer visibility into software quality risk and stronger alignment between engineering, QA and the broader business. The organizations that succeed will be the ones that can scale speed and control together.”
