AI in testing: the dilemma between speed and quality is behind us

Generative Artificial Intelligence has already entered the software development cycle, and with it, a common question has arisen in companies: is it possible to gain speed without compromising quality? In practice, this is no longer the right question. According to the Capgemini Research Institute, more than 55% of organizations already explore the use of this technology in development and testing, focusing on efficiency and time reduction. The advance is clear. The point is now another: how to transform this speed into quality consistently.

AI-assisted automation already allows significantly reducing operational effort on repetitive tasks.In recent projects, we have recorded reductions of up to 80% in regression cycles, freeing up teams for more strategic activities such as risk analysis and business validation.

This move also accelerates the adoption of more mature practices, such as anticipating testing throughout development. With the support of technology, it is possible to identify vulnerabilities earlier, structure test scenarios even before coding and increase the consistency of validations.

AI can still generate inconsistencies, omit context or require human reviews. Without a clear quality strategy, speed gain can amplify risks & directly impact the business.Therefore, the role of testing is changing World Quality Report, quality teams are moving to higher-value roles, focusing on risk analysis, impact validation and oversight of AI-generated outcomes.

In practice, the tester ceases to be only an executor and starts to act as a decision agent. someone who interprets, prioritizes and ensures that what reaches production is aligned with the business. At the same time, we are not only using AI to test, but increasingly testing solutions built with it. This requires expanding the quality criteria, considering consistency, dynamic behavior and response to non-linear scenarios.

The value of this technology in testing is not in replacing human labor, but in enhancing it. Organizations that can balance automation with technical criteria and business vision tend to capture real gains, not only in productivity, but in quality and competitiveness. Because, in the end, going faster only makes sense when it also means getting better.

*Paola Aguirre is a Testing Leader at Ecosystems Global, where he works with quality assurance strategies, automation and evolution of QA practices for digital transformation environments.