AI is changing what we mean by quality
Artificial intelligence is transforming software development across the entire lifecycle. In quality assurance, this evolution is helping teams improve productivity, accelerate testing, prioritize critical scenarios and increase the consistency of digital products.
But it is also changing the nature of risk.
For decades, testing relied on relatively predictable systems: a given input produced an expected output. Quality could be measured through accuracy, consistency and the ability to reproduce an error in order to fix it.
AI-based systems work differently. They learn from data, evolve over time and can generate different responses to seemingly similar situations. As a result, it is no longer enough to verify whether a feature meets a technical requirement. Organizations must also understand the risks the system introduces, how those risks can be detected and what mechanisms are in place to manage them.
From functional validation to risk management
In response to this new reality, QA is evolving into a discipline that is increasingly aligned with risk architecture.
Quality teams no longer participate only at the end of the development cycle, nor are they limited to executing tests. Their role now extends to identifying, assessing and monitoring risks associated with AI model behavior, both during development and once systems are in production.
This requires the introduction of new quality criteria, including reliability, explainability, stability, traceability, security, regulatory compliance, continuous monitoring and the ability to respond to unexpected behavior.
Quality is no longer a one-time activity. It has become an ongoing discipline. In critical applications, this shift is particularly important: QA helps bridge the gap between delivery speed and the level of trust required to operate at scale.
When AI helps validate AI
AI itself is also transforming the work of QA.
AI-powered tools can help generate test cases, identify patterns, prioritize critical scenarios, detect anomalies and analyze large volumes of data in a fraction of the time. When used effectively, these capabilities allow teams to focus on higher-value decisions: what to validate, which risks to accept, what thresholds to define and when intervention is needed.
However, using AI to validate AI-based systems can also amplify errors if appropriate controls are not in place.
An incorrect response delivered with a high degree of confidence, a silent degradation in performance or unexpected behavior under specific conditions can go unnoticed if oversight relies exclusively on automated processes.
That is why automation must be complemented by robust control frameworks, expert review and full traceability.
Human oversight: The judgment behind trust
Human oversight has become a cornerstone of AI governance.
In QA, this means that professionals continue to play a central role in interpreting results, assessing exceptions, validating risks, defining acceptance criteria and making decisions in ambiguous situations.
AI brings speed, analytical power and scalability. People bring context, experience and judgment.
Trust emerges from the combination of both. The goal is not to replace expert judgment, but to enhance it with tools that help teams identify issues earlier, make better decisions and respond more effectively.
Governance as the foundation of quality
Governance plays a central role in this new era.
Organizations need to define who approves a model, what data is used, how performance is monitored, which metrics are used to assess risk and what procedures should be activated when unexpected behavior occurs.
They must also establish clear accountability across business, technology, data, security, compliance and QA teams. In AI-driven environments, quality cannot rest on a single function. It requires a shared responsibility model, with controls embedded from the design stage and maintained throughout the system’s lifecycle.
This approach positions QA in a more strategic role: helping ensure that AI not only works, but works in a way that is trustworthy, secure and aligned with business objectives.
Standards and regulation for trustworthy AI
As AI adoption grows, regulatory frameworks and standards aimed at strengthening governance and risk management are becoming more established.
The European Union’s AI Act introduces a risk-based legal framework and defines requirements and obligations for certain AI systems, particularly those classified as high risk.
ISO/IEC 42001 provides a framework for establishing, implementing, maintaining and improving an AI management system, with a focus on governance, accountability, trust and risk management. ISO 31000, meanwhile, offers general principles and guidelines for managing risk across the organization.
Similarly, the NIST AI Risk Management Framework reinforces the need to manage AI risks through functions such as govern, map, measure and manage, taking a view that extends well beyond the initial deployment phase.
All of these frameworks point in the same direction: the quality of AI-based systems increasingly depends on an organization’s ability to oversee, manage and continuously improve them.
QA as an enabler of trust in the AI era
QA is undergoing a fundamental shift.
Beyond verifying that an application functions correctly, QA now helps ensure that systems operate reliably in real-world environments, that risks are identified early and that effective mechanisms for oversight, control and response are in place.
In the AI era, the success of QA is no longer measured solely by the absence of defects. It is measured by the ability to anticipate risk, manage uncertainty and build trust in systems that are becoming increasingly intelligent and dynamic.
The future of QA is not just about testing faster. It is about helping organizations operate AI responsibly, at scale and with confidence.