Own Automation.
Don't Just
Prompt It.

Yes — AI can generate a Playwright script from plain English. That's real. But here's what it cannot do: decide what actually matters to test, evaluate whether the output is correct, debug a failure in your CI pipeline, understand your specific application's architecture, or answer for itself in a job interview. The people being displaced are not the ones who understand code — they're the ones who don't, because they cannot catch what AI gets wrong. An AI-generated test suite can look complete and be dangerously incomplete. Someone has to read it. Someone has to question it. Someone has to own it. That person needs to understand automation.

The program is structured around your working week with weekend sessions only. Practice between sessions focuses on specific actions that genuinely advance your progress rather than busy work. Proper AI tool usage also reduces implementation time. You won't need to abandon your current employment during the four months.

That depends on what you're trying to do. If you're moving to a new employer, the stack here covers what appears on virtually every Australian automation job description right now. If you're looking to transition into automation at your current company, the more important question is whether you'll be able to pick up their specific tools once you have the fundamentals — and the answer is yes. Automation concepts transfer across frameworks and languages: the architecture, the patterns, the CI/CD thinking, the API testing approach. Once you can write real automation code and genuinely understand what you're doing, your company's specific tool becomes a weeks-long learning task, not a months-long one. The P.A.C.E.R™ method is built exactly for that — a repeatable system for picking up any new technology fast.

C++ is for systems and embedded programming, not test automation roles. For Java and C#: the market has moved toward Python and TypeScript for new automation positions. However, programming fundamentals transfer across languages. Once you understand programming in Python, Java and C# become a syntax problem, not a learning problem. The P.A.C.E.R™ method gives you a repeatable system for picking up any new technology rapidly.

Passive watching doesn't build lasting skills. This program emphasises real code work with live expert review addressing your specific challenges. Month one is designed to demonstrate visible progress fast — a personal roadmap, genuine code, professional feedback, and measurable project advancement. Within weeks, you'll know whether this approach is working.

Success here is measured by capability, not certificates. By completion, you'll have a functioning automation suite reviewable by hiring managers on GitHub, code you personally built and can explain, and a professionally positioned resume. That's the standard I'm holding myself to — not a seat count. And if you complete the program and haven't reached it, I keep working with you at no extra cost until you have.

The program front-loads support, roadmap development, feedback, and project materials before requiring full commitment. Most programs collect money first and deliver later. I deliver first, prove the value, and let the result make the decision obvious. The readiness benchmark exists because accountability is measured by what graduates can actually do.

You don't need a programming background. Month one deliberately lowers the learning curve: instruction starts from QA reasoning, introduces syntax contextually, leverages AI appropriately, and provides feedback before confusion becomes discouragement. Your existing testing instincts are the foundation.

Sessions are recorded — missing one doesn't mean falling behind. For significant circumstances, deferral to the next cohort preserves your progress without extra expense. Progress doesn't reset; you continue from where you left off. First-month deposits are fully refundable if major changes occur.