In the rush to capitalize on Generative AI, software development and delivery has shifted into overdrive. Teams are moving faster, delivering more code, and automating everything from testing to deployment. In many ways, it’s a golden age for software productivity. But beneath the surface, a growing problem threatens to undo those gains: software quality isn’t keeping up.
Call it the ‘AI speed trap’. The more we trust AI to ship code autonomously without rigorous due diligence, the wider the quality gap becomes. And the consequences are already visible. Outages, security breaches, mounting technical debt, and, in some cases, millions in annual losses as the result of business disruption.
In fact, recent research shows that two-thirds of global organizations are significantly at risk of a software outage within the next year, and almost half believe poor software quality costs them $1m or more annually. There is an emerging tension in AI-driven software development: speed vs. stability.
Head of UK & Ireland at Tricentis.
Faster doesn’t always mean better
Modern DevOps and Continuous Integration/Continuous Delivery (CI/CD) pipelines were built to prioritize velocity; GenAI has turbocharged this further, creating more code than ever before.
But AI doesn’t ensure quality; it ensures output. We all know that AI can get it wrong. Without proper guardrails, AI-powered development becomes like a high-speed factory churning out code without accountability. So why are so many teams pushing code live without fully testing it? Because the pressure to deliver quickly outweighs the mandate for due diligence.
That’s not just anecdotal. The 2025 Quality Transformation Report found that nearly two-thirds of organizations admit to releasing untested code to meet deadlines. It’s a staggering statistic, and a stark warning.
The new definition of ‘quality’
Traditional metrics like test coverage, defect rates, or system stability used to define quality. Today, speed is starting to stand in for quality, but it’s a dangerous substitution. Shipping faster doesn’t mean shipping better.
If quality becomes synonymous with velocity, teams risk ignoring deeper indicators, including resilience, maintainability, and customer experience. And when those things fail, the fallout can be major: lost revenue, compliance failures, or service outages that damage trust.
Software quality must be redefined for the AI-first world. It’s not just about finding bugs, it’s about ensuring long-term performance, user satisfaction, and business continuity. In this landscape, quality is less about the absence of errors and more about the presence of confidence.
When confidence is missing
Here’s the paradox: even as organizations accelerate releases, many teams hesitate internally. Over 8 in 10 (83%) of EMEA IT teams (as well 73% in the US) say they delay launches because they aren’t confident in their test coverage. The disconnect between external pressure to move fast and internal uncertainty about product stability is a symptom of broken feedback loops and incomplete visibility.
Worse still, misalignment between leadership and delivery teams creates confusion about what quality even means. While C-suite leaders push for speed and innovation, engineering teams struggle to maintain test rigor under shrinking timelines and budgets.
This breakdown isn’t just a technical issue; it’s a cultural one. To fix it, organizations need stronger alignment around goals, clearer quality metrics, and smarter automation that doesn’t just accelerate work but elevates it.
AI needs to be accountable
Trust in AI is growing, and for good reason. Used well, it can offload repetitive tasks, help developers ship faster, and even make autonomous release decisions, with nine in 10 tech leaders backing its judgment. But handing over the reins to AI doesn’t mean humans should abdicate oversight.
Autonomous AI agents making release decisions may boost productivity, but without transparency, explainability, and traceability, they can also introduce risk at scale. Responsible AI use in development means embedding governance into automation. It means having a way to audit what AI did, and why.
This starts with AI-literate teams. Developers and testers need to understand the logic behind AI-generated outputs, not just blindly accept them. Ethical awareness, systems thinking, and contextual judgment must be part of every team’s toolkit if AI is going to serve as a true partner in quality.
Closing the quality gap
If software engineers want sustainable gains from AI, leaders need to clearly define what quality means for their teams, what level of risk is acceptable within their business and build that into testing strategies from day one.
The quality gap won’t close with more speed, but with smarter systems. This means investing in autonomous software testing and quality intelligence not as an afterthought, but as a strategic function.
By leveraging AI-driven insights and real-time automation, it’s possible to proactively identify risks, eliminate bottlenecks, and embed quality throughout the software development lifecycle. This enables teams to deliver at speed without compromising reliability.
It also requires a return to fundamentals: clear requirements, continuous feedback, and cross-functional accountability. These aren’t outdated concepts, they’re the foundation for any resilient development practice. In short: if AI is the engine, quality must be the brakes and the steering.
A smarter, more balanced future
AI has given us the ability to build and deploy software at unprecedented speed. But if we don’t pair that speed with intelligent quality engineering, the risks will outpace the rewards. The future belongs to organizations that move fast and stay resilient.
That means building AI-augmented testing into every stage of the software lifecycle. It means defining quality not by how fast you ship, but how confident you are that your software can perform in the wild.
It means treating AI as a tool, not a shortcut. Because in the race to deliver, the real winners won’t just be the first to cross the finish line. They’ll be the ones who don’t crash on the way.
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