Artificial Intelligence (AI) is radically transforming the way software is developed and how its quality is ensured. This technological revolution raises a critical question: how will the role of Quality Assurance (QA) evolve in an AI-driven world? Industry leaders and specialized reports agree on one point: QA will not disappear; on the contrary, its role will become more strategic and critical than ever.
AI Accelerates Development and Redefines the Importance of QA
The adoption of generative AI and code assistants (for example, coding agents such as Copilot or Cursor) is significantly accelerating software development. This advancement transforms the speed at which deliverables are produced, but it also introduces a new challenge: quality assurance does not automatically scale at the same pace, since its primary goal remains protecting the reliability and value of the product.
In this context, automated testing becomes increasingly strategic. While AI agents can produce large volumes of code in a short time, they can also introduce subtle or unexpected behaviors that are not always evident at first glance. AI accelerates development, but it does not eliminate the need for rigorous validation; on the contrary, it increases the importance of detecting issues as early as possible.
For this reason, the role of QA is not to “keep up” with development speed in a linear way, but to evolve: adopting new practices, tools, and approaches that allow quality to be sustained in an environment where software is produced faster than ever.
In this scenario, software quality becomes even more critical, but also more complex. The traditional QA approach—primarily focused on validating functionality after the code has already been written—is insufficient to address the speed, volume, and changing nature of AI-generated code. Each line produced by these systems can introduce non-obvious behaviors that require new validation strategies.
As a response to this new context, testing shifts to becoming deeply integrated into the development process. Validation practices emerge where testing happens in parallel with code generation.
Within this framework, agentic testing begins to gain relevance: the use of AI to automatically generate test cases, execute validations, and evaluate code behavior across multiple scenarios. In this model, AI not only accelerates software production but also assists developers and QA teams in early defect detection, acting as a first layer of quality control.
This approach does not replace the QA role; instead, it complements and extends it into earlier stages of development, involving developers as well. Many organizations are already observing that automation— increasingly enhanced by AI—reduces manual testing effort, lowers the number of production defects, and consistently improves the end-user experience.
In summary: as AI increases development speed, the role of QA becomes more important than ever to ensure that speed does not come at the expense of quality.
From Tester to Quality Strategist: The Evolution of the QA Role
Far from disappearing, the role of the QA professional is evolving with a stronger strategic focus. There is growing consensus across the industry: AI will not eliminate the need for testers; instead, it will elevate their role. Human intervention remains essential to ensure that the outputs of AI tools are correct, relevant, and of high quality.
A hybrid approach is emerging, where QA expertise is combined with automation. QA professionals review and refine AI-generated test cases, creating a feedback loop that ensures a balance between efficiency, accuracy, and quality control.
The QA Engineer of the future increasingly resembles a test strategist or AI-assisted quality analyst, with responsibilities that include designing effective test plans, guiding and training intelligent automation, and analyzing trends revealed by AI-driven tools.
QA will spend less time on manual execution or script maintenance, and more time on activities such as:
Complex exploratory testing
Leveraging the tester’s curiosity, creativity, and business knowledge to investigate edge cases and unforeseen scenarios—areas where AI may overlook critical details.
Early collaboration in the development lifecycle (Shift-Left)
Working closely with developers and product owners from the very beginning, defining quality criteria and acceptance conditions before a single line of code is written. This ensures quality is built proactively rather than reactively.
Risk management and test prioritization
Focusing on identifying which tests are most critical for each release. For example, using AI to prioritize test cases based on areas of the code with the most changes or a history of failures—an AI-enhanced risk-based testing approach. This improves efficiency by ensuring that the most severe issues are detected as early as possible.
Intelligent Testing Tools: The New Generation of QA Automation
As part of the evolution of the QA role, we are adopting a strategy based on intelligent testing tools that combine AI capabilities with modern automation frameworks. The goal is not just to automate faster, but to expand the scope of quality assurance in an environment where software is generated and evolves at higher speed.
A concrete example includes MCPs and tools such as Cursor, which act as key allies in automating end-to-end (E2E) scenarios.
AI-assisted test case generation
We leverage the reasoning capabilities of advanced models to translate acceptance criteria and user stories directly into structured test cases. This significantly reduces manual test design time and ensures broader initial coverage.
E2E automation with modern frameworks
Generated scenarios are integrated with automation tools such as Playwright, a flexible and robust frontend testing framework. Through the use of MCPs and tools like Cursor, test cases can be automatically converted into executable scripts, ready to be integrated into CI/CD pipelines. This allows testing to keep pace with development without relying exclusively on manual intervention.
Intelligent test maintenance
One of the main challenges of traditional automation is the high cost of maintenance. Here, AI plays a key role: through MCPs, tests can adapt to minor UI changes—such as selector updates, label changes, or layout adjustments—reducing false negatives and preventing small changes from breaking entire test suites.
Key Skills for the QA of the Future
To thrive in an AI-driven landscape, QA professionals must acquire new competencies and strengthen existing ones:
AI and Machine Learning literacy
Testers are not expected to become data science experts, but they do need a solid understanding of how AI and machine learning systems work. This includes basic concepts such as training, inference, and model tuning, as well as recognizing that AI-driven products are non-deterministic and may produce different outputs for similar inputs. Validation strategies must therefore focus on consistency, robustness, and fairness rather than exact repeatability.
Analytical and creative thinking
Human skills will become more valuable than ever. In QA, this translates into the ability to analyze large volumes of test data to identify quality patterns or problem areas, while also designing innovative tests beyond obvious paths. Creativity helps anticipate rare failure scenarios, while analytical thinking turns data into actionable insights.
Product understanding
- Understanding the product’s purpose and value proposition
- Knowing critical user flows and key interaction points
- Continuously evaluating functional, visual, and usability consistency
- Actively participating in product definition and refinement from early stages
Business understanding
- Knowing the client’s strategic objectives
- Prioritizing testing efforts based on real business impact
- Analyzing commercial risks associated with defects or regressions
- Collaborating with Product Managers and stakeholders to align quality with business strategy
Programming knowledge (SDET mindset)
As testing becomes more automated, solid software development foundations are a strong advantage. This includes understanding the code under test, writing supporting code (testing utilities, CI/CD scripts), and communicating effectively with developers. The SDET (Software Development Engineer in Test) profile enables QA and development to speak the same language.
DevOps and CI/CD proficiency
Future QA professionals must be comfortable working in continuous integration environments, understanding tools such as Docker, cloud platforms, and pipelines like Jenkins or GitHub Actions.
Communication and quality leadership
As the role becomes more strategic, soft skills gain importance. QA professionals must clearly articulate risks and quality criteria to both technical teams and upper management, promote best practices, and foster a culture where “quality is everyone’s responsibility.”
In short, QA professionals will need a blended skill set—combining testing expertise with technical, product, and business knowledge, along with strong communication skills—to lead quality assurance in the digital era.
Strategic Recommendations
Adopt AI as an ally—gradually but without delay
Start with pilot projects in high-impact areas where AI can deliver tangible benefits, such as automating repetitive regression tests or complex UI tests. Experiment, measure results, and scale based on learnings.
Critically evaluate available AI solutions
The market is crowded with tools claiming to offer “AI-powered testing.” Validate them through proof-of-concept exercises, assessing features such as self-healing, intelligent test generation, multi-platform support, and scalability. Involve the QA team in selection decisions.
Upskill the QA team
Transitioning to AI-augmented QA requires new skills. Invest in training programs covering basic data science/ML concepts, advanced automation, and AI-specific tools. Hiring talent with SDET or data analysis experience can also complement existing skills.
Foster a culture of quality and early collaboration
Embrace the idea that quality is a shared responsibility from the start. Enable real Shift-Left practices by involving QA early in design and planning, and encouraging close collaboration across roles.
Keep the human element in the quality loop
No matter how advanced automation and AI become, human oversight remains critical. Combine AI speed with human intuition, and maintain exploratory manual testing for critical or novel features.
Defend and communicate the value of QA in the AI era
QA must be prepared to demonstrate its strategic value. Beyond defect detection, QA ensures reliability, user experience, and product integrity. Translating testing results into business language—cost reduction, customer satisfaction, and faster time-to-market—is essential.
Sources:
momentic.ai
qavalley.com
blogs.opentext.com
buzzsprout.com
deeplearning.ai
