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.

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    What You’ll Learn

    This session will be valuable for QA Professionals, ML Engineers, Engineering Managers, and Product Owners committed to responsible AI adoption.

    1. Learn the five critical challenges of AI testing
    2. Learn why tools like SHAP and LIME are critical for interpreting model decisions
    3. Explore the essential role of Explainable AI (XAI)
    4. Review the key areas of modern AI validation, including Data Validation, Model Verification, and End-User Testing (UAT)
    5. Discuss the vital importance of AI security

    Catch Up with Previous Issues

    • QA Audit Webinar

      QA Audit Webinar

      Discover where even mature QA processes hide costly gaps and learn how to transform audit findings into real improvements.

    • QA for Startups Webinar

      QA for Startups Webinar

      How to ensure product quality when you don’t have a QA team or when your single QA is overworked? Our testing and delivery experts share proven techniques and tips.

    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.

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