2025 saw generative AI race into software teams at extraordinary speed, yet most organisations are now realising that turning early experimentation into tangible value is far more difficult than the hype initially suggested.
Capgemini’s World Quality Report 2025 found that almost 90 percent of organisations are now piloting or deploying generative AI in their quality engineering processes, yet only 15 percent have reached company-wide rollout. The rest remain in the early stages, feeling their way through proofs of concept, limited deployments or experiments that never quite scale.
This gap between excitement and deployment points to a simple truth: speed and novelty alone are not enough to deliver quality software. With AI changing the way teams think about testing, organisations need to intentionally build the foundations that will make AI-supported quality engineering scalable in 2026.
Speed does not equal quality
Many teams are drawn to AI because of its ability to generate tests and code with remarkable speed. For instance, I have seen people feed a Swagger document into an AI model to generate an API test suite within minutes. However, upon reviewing the tests, we could see just how many of those results were flawed or over-engineered.
When teams leave this level of quality review until the very end, they often discover too late that the speed gained upfront is offset by the time spent reworking what the AI produced. And unsurprisingly, this pattern is becoming common because AI can accelerate generation, but it cannot ensure that what it produces is meaningful.
It may hallucinate conditions, overlook domain context or even misinterpret edge cases. And without strong oversight at every stage, teams end up deploying code that has passed large volumes of tests but not necessarily the right tests.
In 2026, this will push organisations to prioritise quality review frameworks built specifically for AI-generated artefacts, shifting testing from volume-driven to value-driven practices. This is where the idea of continuous quality will become increasingly essential.
Continuous quality
Quality engineering as a term can sometimes give the impression that quality is something delivered by tools or by a distinct engineering function considered at the very end. Continuous quality takes a broader and more realistic view; it is the idea that quality begins long before a line of code is written and continues long after a release goes live.
Instead of treating testing as a final gate, deploying quality testing at every stage integrates quality-focused conversations into design, planning and architectural discussions. This continuous process in turn sets expectations around data, risk and outcomes early, so that by the time AI tools produce tests or analyses, teams are already aligned on what good looks like.
This approach mirrors the familiar infinity loop used in DevOps. Testing, validation and improvement never sit in isolation. They flow through the delivery lifecycle, consistently strengthening the resilience of systems; when organisations adopt this mindset, AI becomes a contributor to quality rather than a barrier.
As AI becomes more deeply embedded in pipelines, continuous quality will be the model that determines whether AI becomes an enabler of better software in 2026 or a source of unpredictable failures.
Aligning AI adoption to real quality goals
Once quality becomes a continuous activity, the next challenge is understanding how AI amplifies the complexity already present in enterprise systems. Introducing AI-generated tests or AI-written code into large, interdependent codebases increases the importance of knowing how even small changes can affect behaviour elsewhere. Quality teams must be able to trace how AI-driven outputs interact with systems that have evolved over many years.
Senior leaders are placing pressure on teams to adopt AI quickly, often without clear alignment on the problems AI should solve. This mirrors the early days of test automation, when teams were told to automate without understanding what they hoped to achieve. The result is often wasted investment and bloated test suites that are expensive to maintain.
The most important question organisations will be compelled to ask in 2026 is why they want to use AI, particularly deciding the specific outcomes they want to improve, the types of risk they want to reduce, and the part of the delivery process which stands to gain the most from AI support. When teams begin with these considerations instead of treating them as after-thoughts, the adoption of AI will become purposeful rather than reactive.
The evolving role of the tester in an AI-enabled pipeline
This shift toward more deliberate AI adoption naturally changes what quality professionals spend their time on. As AI becomes embedded in development pipelines, testers are no longer simply executing or maintaining test cases. They increasingly act as the evaluators who determine whether AI-generated artefacts actually strengthen quality or introduce new risk.
As AI systems start generating tests and analysing large volumes of results, testers move from hands-on executors to strategic decision-makers who shape how AI is used. Their focus shifts from writing individual test cases to guiding AI-generated output, determining whether it reflects real business risk and ensuring gaps are not overlooked.
This expansion of responsibility now includes validating AI and machine learning models themselves. Testers must examine these systems for bias, challenge their decision-making patterns and confirm that behaviour remains predictable under changing conditions. It is less about checking fixed rules and more about understanding how learning systems behave at their edges.
Data quality becomes a cornerstone of this work. Since poor data leads directly to poor AI performance, testers assess the pipelines that feed AI models, verifying accuracy, completeness and consistency. Understanding the connection between flawed data and flawed decisions allows teams to prevent issues long before they reach production.
While AI will certainly not replace testers in 2026, it will continue to reshape their role into one that is more analytical, interpretative and context driven. The expertise required to guide AI responsibly is precisely what prevents organisations from tipping into risk as adoption accelerates – and what will ultimately determine whether AI strengthens or undermines the pursuit of continuous quality.
Preparing for 2026
As these responsibilities expand, organisations must approach the coming year with clarity about what will enable AI to deliver long-term value. The businesses that succeed will be the ones that treat quality as a continuous discipline that blends people, process and technology, rather than something that can be automated away.
AI will continue to reshape the testing landscape, but its success depends on how well organisations balance automation with human judgment. Those that embed continuous quality into the heart of their delivery cycles will be best positioned to move from experimentation to genuine, sustainable value in 2026.
