Will Artificial Intelligence Reinvent QA?
QA in the Face of Software Complexity
Automating tests has never been more critical—or more challenging. As digital products evolve, QA teams grapple with complex environments, tight deadlines, and rising quality demands.
Traditional methods fall short:
Test scenarios are time-consuming to write/maintain.
Automation requires deep technical expertise.
Functional coverage often remains incomplete.
- Maintaining traceability between Specifications and Tests, especially with the use of tools specific to task management (such as Jira) on the one hand and CI/CD for automatic tests (Regression / Unit / Integration, etc.) on the other.
AI as a Catalyst for Future Quality Assurance
This is where artificial intelligence comes into play. Thanks to advances in NLP (natural language processing) and machine learning, and even more so to the scale of Generative AI (GenAI) with Multimodal LLMs (which interact with text, sound, images and video) that can easily grasp and understand any application interface, AI now makes it possible to:
Generate intelligent test cases from specifications or bug histories,
Optimize coverage by identifying under-tested areas,
Dynamically adapt test suites to code/requirement changes,
Analyze test results to prioritize fixes and detect failure patterns.
AI doesn’t replace QA —it augments it.
The Evolving Role of QA Engineers
In this new landscape, the role of the tester is evolving. It’s no longer just about designing or executing test cases, but rather about becoming a conductor between human and artificial intelligence. New responsibilities include:
Orchestrate collaboration between technical teams and artificial intelligence,
Oversee AI-generated test relevance,
Guide quality priorities with data-driven insights,
- Contribution to product strategy with a data-driven approach.
AI frees up time and unlocks new possibilities : a more predictive, more strategic QA, more integrated at the heart of delivery.
Quality Assurance for Artificial Intelligence Products
- The quality of the AI models used in the application, ensuring their performance, robustness, and consistency in different usage contexts,
- Data quality throughout the process, from acquisition to model training using these data,
- Tracking through relevant metrics, allowing the measurement of the evolution of AI model and data performance,
- Explainability of the model used, to ensure trust, regulatory compliance, and the ability to diagnose errors,
- Implementation of user feedback loops during production.
These points are just the tip of the iceberg compared to the complexity of QA tasks now intrinsically linked to the evolution of AI applications.
A New Era for Software Quality
Artificial intelligence in QA is no longer science fiction.
It’s a quiet revolution, already at work in many tech teams.
And like any revolution, it raises questions, it disrupts established norms…
But above all, it opens up unprecedented opportunities for those who know how to embrace it with discernment, strategy, and curiosity.
