Why Your Automation Strategy May Be Falling Behind
If your QA team is still spending hours writing page objects, test locators, and data factories by hand, you’re already behind.
Generative AI is reshaping test automation at a staggering pace, slashing coding tasks from hours to minutes, and enabling engineers to produce 3–5x more high-quality code per sprint.
But while the hype around AI in QA is everywhere, few leaders know how to separate shiny “magic solutions” from practical business value.
That’s where Ben Fellows comes in.
Meet Ben Fellows
Ben Fellows is the founder of a QA services company and a leading voice on LinkedIn in the AI-powered QA movement.
With years of experience helping QA teams implement Playwright and AI-driven automation, Ben has trained industry leaders like Jim Hazen and Butch Mayhew through his hands-on workshops.
His focus?
Helping QA leaders cut through noise and apply AI where it directly accelerates delivery, reduces costs, and boosts team productivity.
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1. Use AI as a Productivity Booster, Not a Silver Bullet
According to Ben, too many vendors are selling “AI agents that do all your testing for you.”
While flashy, these solutions are slow, expensive, and not production-ready.
Instead, the real value today is augmented coding—using AI to generate the same high-quality code your engineers would normally write, only faster.
- Example: Writing a Playwright page object model that used to take 3–4 hours now takes 20 minutes or less with AI.
- Business outcome: Teams can ship features faster, reduce backlog pressure, and keep pace with accelerated development cycles.
2. Rethink QA Roles in the Age of AI
As AI tools speed up code generation, the bottleneck has shifted. QA leaders are no longer struggling to produce enough code—they’re struggling to review code at scale.
Ben notes that some companies are rebalancing their org charts:
- Fewer engineers focused on raw coding
- More emphasis on reviewers, architects, and test strategists
This shift requires QA managers to rethink job descriptions, performance metrics, and team structures.
3. Focus on High-Value, Tedious Tasks First
Want to get started?
Don’t aim for moonshots. Ben recommends applying AI to repetitive, pattern-based tasks that drain engineering hours:
- Page Objects: Automatically generate hundreds of locators and methods with accuracy rates above 80%
- Data Factories: Feed AI your schema and let it produce test data factories in minutes.
- API Insights: Point AI at an endpoint and get the object shape, dependencies, and even better Swagger documentation.
By targeting these tedious tasks first, QA leaders can quickly demonstrate ROI and gain buy-in from skeptical stakeholders.
4. Invest in Premium Models and Guardrails
Not all AI is created equal. Ben warns that results vary dramatically depending on the model. Teams using cheap or outdated models often dismiss AI prematurely because outputs are poor.
Best practices:
- Budget $200–$250 per engineer/month for premium models like Claude or GPT-5.
- Use Cursor rules/templates to enforce coding standards across your team.
- Always review and debug AI-generated code—the goal is acceleration, not blind trust.
5. Prepare for the Next Wave: Image-Based Testing
Looking ahead to 2026, Ben predicts a shift away from DOM-based automation toward image-based or natural-language testing.
Imagine instructing an AI: “Log in, navigate to the dashboard, and validate formatting matches the design.” The AI evaluates the page visually—just like a real user—removing the need for brittle locators and assertions.
While this is still expensive and slow today, the technology is improving quickly. QA leaders should start experimenting now to avoid being blindsided.
Actionable Takeaways for QA Leaders
Here’s how to start applying these insights in your team:
- Run a POC with premium AI models (Claude, GPT-5) using Cursor or Copilot
- Target tedious tasks first—page objects, locators, and data factories.
- Shift your team mix toward reviewers and strategists, not just coders.
- Set guardrails with templates and coding rules to ensure consistency.
- Explore future trends like Playwright MCP and image-based testing, but don’t bet the farm yet.
Final Thoughts
AI won’t replace great testers—it will amplify the best ones. By adopting augmented coding today, you can free your team from repetitive drudgery, accelerate delivery, and prepare for the next wave of AI-driven automation.
To dive deeper, check out the full episode of the TestGuild Automation Podcast with Ben Fellows—including live demos of AI writing 500+ lines of production-ready Playwright code in minutes.