Legacy vs. Autonomous QA Arena - Surviving the AI-Driven Quality Evolution
Your 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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TestMu Conf
Testμ (TestMu) is the world’s largest virtual conference on agentic engineering and quality, built by the community, for the community. As AI reshapes how we build, test, and ship software, Testμ Conf is where you connect, grow, and lead: agentic workflows, autonomous quality, battle-tested AI playbooks, hands-on workshops, and the engineering culture driving it all.
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