How QA Can Lead in the Age of AI: Embracing Automation and Innovation

Gen-AI is reshaping how we build and ship software — enabling faster demos, real-time iteration, and automation at scale. But in this fast-moving future, QA isn’t just keeping up — it has the chance to lead.

 

At QonfX, I had the chance to hear Ben Hofferber speak on AI's role in scaling QA, and his session was a standout for me. He shared how QA professionals can drive the adoption of AI in ways that enhance our testing — not replace it.

Ben shared how teams are using Gen-AI tools to generate high-fidelity demo apps that look great at first glance. But when testers start to dig in — things break down.

That’s the reality: AI has helped us move faster on the development side, but QA hasn’t caught up yet. These demo apps often look finished but are missing resilience, accessibility, or thoughtful edge-case handling.

That’s where we come in — and where strong testing fundamentals matter more than ever.

Acommon theme among the speakers is that AI testing is powerful - but not plug and play. Ben introduced the idea of using AI agents and multi-agent control protocols (MCPs) to handle repetitive testing tasks like regression or smoke tests. These tools can help:

  • Expand coverage

  • Reduce human error

  • Save time on routine validations

But as with all automation, oversight is critical. He emphasized that AI in QA is not set-and-forget — we still need skilled testers to monitor, adapt, and guide these tools.

I’ll be honest — AI agents and MCPs are still a bit of a foreign language to me. But Ben made it approachable and helped me see how we can take the first steps into these workflows.

He outlined several approaches that are likely to become common strategies when working with AI tools. Here’s a quick breakdown:

  • Exact Text – Helpful in classification tasks, but not robust for general validation.

  • Similarity Scoring – Uses embedding models to detect if AI output is “close enough” to expected.

  • LLM Validators – Can verify output but aren’t reliable without supporting checks.

  • Hill-Climb – Measures whether output is better than before — useful for iterative improvements.

  • Manual Reference Checks – Still necessary to verify high-risk scenarios or unknown domains.

It was a great reminder: AI can enhance our work, but only if we know how to evaluate it properly. We should explore ways to ground our AI tools in real product data or internal services to keep the results relevant and accurate.

So how do we bring AI into our QA workflows without losing control or quality?

Here’s what I’m excited to try with my team:

  • 🧾 Shared Prompt Templates: Use AI to generate consistent test cases from Jira tickets.

  • ⚙️ Automated Regression with Agents: Identify stable flows that can be delegated to AI.

  • 🔍 Early Detection of Missing Requirements: Let AI flag gaps in ticket details and flow logic.

  • 🧪 Experiment with Hill-Climb or Similarity Tests: For areas like copy changes or UI states.

  • 🤝 Collaborate with Product and Dev: Ensure AI-generated tests reflect real-world use cases.

If we can reduce the time spent writing repetitive test cases, we can free up energy for exploratory testing, usability feedback, and early product insights.

All the speakers at QonfX echoed the same theme: AI isn’t replacing testers — it’s reshaping how we test. And it’s up to us to decide how we lead that change.

Let’s not wait to be handed tools — let’s experiment with them. Let’s push for better coverage, faster feedback loops, and higher quality across the board.

QA has always been about more than finding bugs — it’s about safeguarding quality as we build the future.

Have you started using AI in your testing workflows yet? What’s worked — or surprised you?

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