AI Quality Engineering: From QA Gate to Growth Engine

In this episode, host Michael Bernzweig interviews Khurram Mir, CMO and founder of Kualitatem and Kualitee, about how quality engineering is changing in the AI era. Khurram explains why traditional QA—built as a late‑stage gate for waterfall releases—can’t handle today’s rapid deployment cycles and AI‑enabled systems. He shows how to shift testing left, embed quality across the SDLC, and use generative AI for test case generation, synthetic data, self‑healing automation, and smarter defect analysis so quality becomes a growth enabler instead of a release bottleneck.Key topics:The limitations of traditional QA in rapid release cycles and how to adaptPractical AI tools for test case generation, synthetic data, and self-healing automationThe importance of embedding quality across the entire SDLC, not just in QARed flags indicating waterfall thinking still dominates an organization’s QA approachRoadmap for AI adoption: pilot, integrate, and mature with predictive capabilitiesUpskilling testers into business-savvy quality engineers through critical thinkingThe shift from reactive testing to proactive, risk-driven quality managementUsing AI for defect triage, test data creation, automation maintenance, and integration mappingAligning teams and KPIs to foster shared ownership of qualityData pipeline best practices for AI reliability and real-time transformationTimestamps:00:00 - Introduction to AI-driven quality engineering revolution00:29 - Khurram Mir’s personal journey from software tester to QA innovator01:13 - Key elements for executives to start with quality engineering03:00 - Red flags signaling waterfall QA in modern organizations04:36 - Measurable outcomes of investing in quality early05:21 - Impact of maturity levels on quality transformations09:44 - Evolution of QA from waterfall to continuous deployment models11:00 - Why traditional QA models break in AI-enabled fast release cycles13:22 - Role of QA as a system, embedded from requirements to deployment15:52 - The impact of generative AI on test case creation and defect prediction17:27 - How AI addresses the "blank page" problem in test design19:40 - Synthetic data generation for healthcare and regulated industries21:52 - Self-healing automation and intelligent defect analysis23:02 - Roadmap to AI adoption: start small, scale responsibly24:29 - Quality as a growth enabler, not a cost center25:05 - Live Q&A session highlights34:36 - Deep dive into data pipeline best practices for AI reliability Ready to action this strategy?Connect with this expert now for a complimentary strategy consult or exclusive offer.https://summit.softwareoasis.com/activate-offers/(Claim Your Offer / Schedule Consultation)Apply to Present at our Live Events as an experthttps://experts.softwareoasis.com/expert-introductionsGet Your Networking Passhttps://experts.softwareoasis.com/executive-networking-passClaim your complimentary event pass and attend live:https://bootcamps.softwareoasis.com/https://summit.softwareoasis.com/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.