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An overview of how organizations are using artificial intelligence to move from reactive testing to proactive quality engineering—reducing costs, accelerating releases, and improving reliability. Topics include automating and optimizing test design for higher coverage, predicting defects and focusing testing where it matters, enabling intelligent test automation with self-healing scripts, continuously monitoring quality across CI/CD pipelines, and generating realistic, privacy‑compliant test data. Expected outcomes discussed include reducing total cost of quality by up to 25% and accelerating release cycles by 20%.

The Cost of Quality (CoQ) has long been a challenge for QA teams; where rework, production incidents, and defect leakage silently erode profitability. In traditional environments, up to 70% of CoQ comes from failure-related activities. But with AI-driven testing, that balance is shifting. This webinar explores how organizations are using artificial intelligence to move from reactive testing to proactive quality engineering—reducing costs, accelerating releases, and improving reliability. Join us to uncover how AI can:

• Automate and optimize test case design for higher coverage and reduced effort.
• Predict defects and focus testing where it matters most.
• Enable intelligent test automation with self-healing scripts.
• Continuously monitor quality across CI/CD pipelines.
• Generate realistic, privacy-compliant test data with minimal manual effort.

We’ll also discuss measurable business outcomes—like reducing total CoQ by up to 25% and accelerating release cycles by 20%.

Learn how to turn testing from a cost center into a strategic value driver by engineering quality from the start.