Applying AI Across the Quality Engineering Lifecycle

Speaker: Garima Sarin

Topic: Applying AI Across the Quality Engineering Lifecycle

Summary
Problem Statement
Traditional QA models focused on test execution and coverage no longer provide sufficient risk visibility or justify investment at scale.
AI Across the QA Lifecycle
AI is applied end-to-end—from requirements analysis (risk and ambiguity detection), to test design and prioritization (risk-based scope optimization), to execution (flaky test detection and intelligent reruns), and defect triage (pattern recognition and root-cause correlation).
Business Value & ROI
AI reduces QA waste, improves automation signal quality, lowers cost of quality, and enables data-driven release decisions. Metrics shift from activity-based reporting to confidence- and risk-based indicators.
Leadership Strategy
A pragmatic adoption approach covering data readiness, CI/CD integration, governance, and human-in-the-loop controls to scale AI responsibly.
Outcome
Quality Engineering evolves from test execution to risk intelligence and strategic business enablement.

Agenda:
🕛 4:00–4:05 — Welcome
🕛 4:05–4:55 — Shreyansh Sharma Applying AI Across the Quality Engineering Lifecycle
🕛 4:55–5:00 — Closing

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