Legacy vs. Autonomous QA Arena - Surviving the AI-Driven Quality Evolution
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Watch on YouTubeYour test suite is failing more from flaky locators than actual bugs — and it's costing you 30–40% of your QA budget. This is not a lecture about AI hype. It's a live arena.
Software testing is undergoing its biggest architectural shift in a decade. Enterprise AI adoption in QA is projected to jump from 15% to 80% by 2027, Playwright adoption surged 235% year-over-year, and yet 89% of teams piloting GenAI in QA still can't get it reliably into production. Something doesn't add up — and this session is built to figure out why, together, in real time.
In 75 fast-moving minutes, we put legacy script-driven automation head-to-head against self-healing, AI-augmented execution — live. You'll vote on real failure logs before we reveal the root cause. You'll watch an actual test suite break on a live deploy, then watch an AI observation layer catch and heal it in seconds, with the exact resolution strategy shown on screen — no smoke, no vendor slideware. We'll deconstruct what "self-healing" actually means mechanically (spoiler: it's three measurable techniques, not magic), and confront the uncomfortable stat at the center of the AI-in-QA conversation: why almost everyone is piloting this, and almost no one has it in production.
This isn't a tooling sales pitch, and it isn't survivor bias from one team's lucky migration. It's a structural argument, backed by market data and a live technical demonstration, for why the QA professional's job is changing — not disappearing. The session closes by mapping the exact skill shift underway: from writing and maintaining scripts, to owning risk-based test strategy, GenAI evaluation, and AI governance. Attendees leave with a framework for that shift, not just a demo they watched.
Key Takeaways:
Diagnose the real cost of test flakiness — understand why locator and timing failures consume up to 40% of QA budgets, and how to make that case to non-technical stakeholders.
See self-healing test automation demystified live — not a vendor claim, but a working demonstration of the actual resolution mechanics (selector, timing, and runtime-error healing).
Understand why Selenium isn't dying, just being re-scoped — a clear framework for deciding what stays on legacy tooling vs. what moves to AI-native platforms.
Close the pilot-to-production gap — learn why 89% of GenAI-in-QA pilots stall before production, and what governance pattern (human-in-the-loop) actually gets teams past that wall.
Walk away with a personal skill roadmap — the four capabilities defining the shift from "script author" to "quality owner," and how to start building them now.
About the speaker
Athresh Guruprakash:
Athresh is a technology leader with over 18 years of experience across software engineering and applied AI/ML systems, currently leading engineering teams at Equifax within financial services risk technology. Over his career he has built and led diverse, cross-functional engineering organizations — owning delivery, technical direction, and team growth — with a strong personal focus on mentoring engineers at every career stage, from early-career developers to senior individual contributors. His technical interests sit at the intersection of large language model evaluation, AI-augmented engineering practices, and responsible AI deployment in regulated industries. Beyond his day-to-day role, Athresh authors published research spanning LLM reliability, applied ML, and large-scale systems architecture, and contributes to Tech with AG, a learning forum that makes modern AI concepts accessible to students and early-career technologists.
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