How You’ll Test AI Apps for Trust and Scale in 2026
Traditional testing breaks down for AI: models learn from data, make decisions based on patterns, and can surprise you with unexpected errors. Because of this, a single, universal playbook for AI QA simply doesn’t exist yet.
What You’ll Learn
This session will be valuable for QA Professionals, ML Engineers, Engineering Managers, and Product Owners committed to responsible AI adoption.
- Understand why a high accuracy score is insufficient for real-world deployment of AI products
- Learn the five critical challenges of AI testing
- Learn why tools like SHAP and LIME are critical for interpreting model decisions
- Explore the essential role of Explainable AI (XAI)
- Review the key areas of modern AI validation, including Data Validation, Model Verification, and End-User Testing (UAT)
- Discuss the vital importance of AI security
Our Expert Speakers
Michael Tomara
QA Lead, TestFort
With over a decade in QA, Michael brings hands-on experience in shifting testing practices left. He specializes in process optimization, ensuring early defect avoidance, and improving the release stability for complex, data-driven applications.
Twinkle Joshi
Senior Software Engineer | Lead QA Automation & Testing Expert
Twinkle is a leading advocate for Scalable, Quality-First Software, specializing in the intersection of traditional QA and modern machine learning. Her focus is on leveraging Agentic AI principles for autonomous testing and building systems that are both highly scalable and ethically sound.
Bruce Mason
Delivery Director, TestFort
With 25+ years in QA management, Bruce focuses on practical, business-driven outcomes. He guides organizations in implementing smoother delivery flows and sustainable quality improvements, ensuring testing strategies are directly tied to compliance and business value.
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QA Services to Support Your Progress
Testing Consulting
Get expert guidance to define the right Responsible AI Testing strategy and align your QA process with global compliance standards.
AI App Testing
Thorough validation of user workflows and business logic to ensure your software works perfectly for real users.
Quality Engineering
Build quality, transparency, and human oversight into every stage of AI development through specialized automation and continuous model improvement.