While many presentations get lost in complex architectures and models with billions of parameters, most testing professionals are still left without answers to essential questions: what kinds of artificial intelligence exist, what actually works in a testing context, and—above all—where to start without falling into hype-driven exaggeration.
Hingo Santos is a results-driven Head of Quality Assurance with more than 18 years of experience in software testing, quality strategy, automation, and DevOps enablement. He has led global QA teams across Europe, India, and Latin America, building strong testing communities, mentoring professionals, and designing scalable frameworks that enhance delivery quality, speed, and reliability.
He recently completed his postgraduate degree in Artificial Intelligence, further strengthening his commitment to bridging theoretical knowledge with practical application in modern software testing.
Currently at BNP Paribas in Lisbon, Hingo defines the QA strategy for mission-critical applications and leads multiple AI-driven initiatives aimed at elevating team performance through practical, high-impact solutions integrated into daily testing workflows.
While many presentations get lost in complex architectures and models with billions of parameters, most testing professionals are still left without answers to essential questions: what kinds of artificial intelligence exist, what actually works in a testing context, and—above all—where to start without falling into hype-driven exaggeration.
This session slows things down—not to oversimplify, but to make the topic accessible and applicable to those who are on the ground every day ensuring quality. It presents the main types of AI (supervised and unsupervised learning, NLP, deep learning, and GenAI), with examples inspired by real-world cases widely used across the industry.
With a practical and straightforward approach, it explores ways to apply AI to activities such as defect classification, log analysis, scenario extraction, and visual validation of interfaces. The session includes simple exercises, ready-to-use prompts, and a maturity checklist to help each team understand where they are and how they can progress.
More than a technical talk, it is a call to action: you don’t need to be a data scientist to use AI in testing — you just need context, clarity, and curiosity.
