Frequently Asked Questions - FAQ
AI in QA: What decision‑makers really ask
AI in quality assurance raises important questions – especially in regulated industries where accountability, traceability, and compliance are non‑negotiable.
This FAQ addresses the most common and critical questions we hear from leaders responsible for quality, risk, and delivery.
It clarifies what AI can and cannot do in QA, how it is governed, and how it can be introduced safely without increasing risk.
Our viewpoint is simple:
AI should strengthen human decision‑making – not replace it.
Yes – when AI is used as decision support, not an uncontrolled decision maker.
In regulated industries, AI in QA is reliable when:
AI suggestions are traceable
outputs are reviewed and approved by humans
all decisions are auditable
AI is typically used to:
- help analyze test coverage
- help identify risk areas
- suggest optimization on what to test and when
—not to make final release decisions.
This aligns with regulatory expectations in industries such as automotive, medical devices, defence, and finance, where accountability and transparency are mandatory.
AI does not:
- define business requirements
- judge legal or regulatory compliance
- replace domain experts
- remove the need for test strategy
AI is very good at identifying patterns and anomalies, but it does not understand:
- business impact
- safety consequences
- regulatory interpretation
QA still requires humans to define what is acceptable, what is risky, and what must never fail.
By introducing AI incrementally, with explicit control points.
A low‑risk approach includes:
- starting with analysis and prioritisation, not execution
- applying AI to existing test assets
- keeping humans in the decision loop
- logging and documenting AI outputs
AI should first support testers, not replace them. When introduced correctly, AI reduces risk by exposing blind spots earlier in the lifecycle.
Through the same principles used for any critical tool in regulated environments.
Effective governance means:
- documenting how AI is used
- defining what decisions AI can and cannot influence
- validating outputs against known baselines
- ensuring traceability from AI insight to human decision
AI models are treated as:
- support tools, not authorities.
This makes AI use compatible with quality management systems and compliance frameworks.
AI makes test automation smarter – not obsolete.
Traditional test automation:
- executes predefined tests
- ensures repeatable verification
AI can add:
- intelligent test selection
- test authoring
- risk‑based prioritisation
- detection of unexpected behavior
- insights across large data sets
Together, they form a hybrid approach:
- rule‑based automation for control, AI‑based analysis for adaptability.
By applying AI to one concrete question – not to the entire QA process.
A typical starting point:
- analyze an existing codebase or test suite
- identify areas with highest technical or quality risk
- use AI insights to guide manual or automated testing
No organisational change is required initially. No process overhaul is needed. The goal is learning and insight – not immediate scale.
Small, well‑governed pilots create confidence and momentum without disruption.