You’ve Been Writing the Same Login Test for 3 Years

Every sprint: 25 test cases documented. 4 automated. 21 added to backlog.

Large Language Models like ChatGPT, Gemini, and Claude are transforming how QA teams write, execute, and maintain test cases. But most teams are still stuck using generic LLMs that generate code requiring hours of editing.

Specialized AI agents aren’t just faster—they’re a paradigm shift in test automation.

This practical guide shows you how 500+ QA teams have achieved 70%+ automation coverage in 90 days—clearing backlogs that would have taken 18 months using traditional methods.

What’s Inside:

• The 12x Speed Gap: Why Your Backlog Keeps Growing

Understand why you’re documenting test cases 12x faster than you’re automating them—and why traditional solutions (hiring SDETs, low-code tools, ChatGPT) have failed to close the gap. Includes real cost analysis: $65,325/year for a team with 272 unautomated tests.

• The Release Velocity Tax: Real Data from 500+ QA Teams

See how automation coverage directly impacts release speed and production defects. <30% coverage = 12-14 day cycles + 8.4 bugs per release. 70%+ coverage = 4-6 day cycles + 1.9 bugs per release. Case studies with specific outcomes.

• From Generic LLMs to Specialized AI Agents

Learn the fundamental difference between ChatGPT (knows “some Selenium”) and specialized AI agents (know 15+ frameworks, your tech stack, your architecture patterns). Includes survey data showing why 89% of QA engineers report having to heavily edit ChatGPT-generated code.

• The Complete Workflow: Generate → Execute → Repeat

Step-by-step breakdown of how AI agents generate production-ready test automation. From plain English test cases to framework-specific code in 90 seconds. Includes execution on 5,000+ real devices and scaling strategies.

• The 90-Day Transformation Roadmap

Month-by-month implementation guide showing how teams go from 15% to 70%+ automation coverage. Months 1-2: Clear the backlog. Months 3-4: Shift left. Months 5-6: Expand scope (API, visual, accessibility, performance testing).