Enterprises are shipping huge volumes of untested AI-generated code – experts warn it will cause major security issues and have huge financial repercussions

The accelerating use of AI in software development has led 60% of global organizations to ship untested code, prompting warnings over potential security risks.

According to Tricentis' 2026 Quality Transformation Report, the proportion has dropped only slightly since 2025, when it was 63%.

Crucially, the reasons behind the volume of untested code have changed. Last year, the company attributed much of this to accidental quality control slips, cited by 40%.

Now, though, 32% of organizations admit that they are doing it knowingly, mainly because of leadership pressure to prioritize speed over quality. Three-in-ten blamed the sheer volume of AI-generated code, which is overwhelming quality control and testing processes.

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“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,” said 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."

Poor code quality is now a global problem

The problem was found right across the board, with more than half of organizations across every major industry surveyed reporting deploying untested code to production.

The trend was most prevalent in financial services (64%), retailers (63%), and energy and utilities (58%).

Governance is also suffering, the study noted. While 48% of organizations have fully implemented AI internally, more than half of those report that their AI tools and processes regularly change.

Similarly, a third of teams cite tool complexity and sprawl as a key barrier to achieving continuous software quality at scale.

Other top barriers include skills gaps, cited by a third, code volume increasing faster than they can manage (28%), and a lack of clear quality and trust metrics (26%).

Conflicting priorities put enterprises at risk

Notably, researchers said what’s considered AI progress in the boardroom may feel more like operational friction to software teams, highlighting a growing divide between the C-suite and frontline workers.

Just over four-in-five CEOs reported high confidence in AI-driven systems and tools, compared with 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 with 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%).

Poor quality hurts the bottom line

Naturally, there's a financial cost associated with this poor software quality. Around one-in-five organizations report losing more than $1 million annually as a result, mainly because of security and compliance failures (30%) and technical debt and rework (28%).

"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," said 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.”

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