Helpful for speed, hopeless for accuracy: where we’re at with AI and QA
It would be pretty dreamy if AI could just test everything for us, wouldn’t it? We’ve thought about it, and we’ve heard it from our clients. Usually, the sentiment comes from teams under pressure to ship faster, with tighter budgets. When things are tight, bringing in a high-tech tool to sort it all out sounds pretty appealing. On the surface, AI-powered QA tools tick all the boxes: speed, coverage, efficiency.
But in practice, especially when used with brownfield or cross-platform apps, they rarely deliver what matters. AI can check what’s expected, but with the unexpected? It flops.
The missed bug nobody saw coming
Maybe you’ve been here: all your automated tests pass, your build goes out, everything seems fine. Then, your users hit a critical bug. They’re using a real device out there in the real world with different context - something the AI tool never accounted for.
Now you’re dealing with complaints, a drop in App Store ratings, and frustrated teams scrambling to patch something that should’ve been caught earlier. Trust takes a hit, retention dips, revenue follows. Not good!
The tool said it was fine, your users say otherwise.
False confidence is costing teams more than they realise
This is the real risk of leaning too hard on AI. Not just the bugs it misses, but the belief it gives you that testing’s done, when it isn’t. That sense of ‘we’ve got this covered’, when key gaps are still wide open.
It’s especially dangerous in tangled or legacy codebases, where even small regressions can have big consequences.
Where we’re at with AI and QA
We’ll hold our hands up, we’ve looked at using AI in our QA process - but only where it fits. Right now, it’s not an embedded part of our practice, but we’re open to it. We’ve found that it’s helpful for early coverage, repetitive tests, and spotting low-hanging issues. But, it just can’t handle nuance. It doesn’t understand wider context, UX flow, or edge-case behaviours across devices, the areas where QA is needed most.
We build QA strategies that focus on real experience, with opportunities to blend automation (when we think it’s ready!). We know what AI can handle and what it can’t. By keeping it real, we’ve been able to help clients avoid bad launches, broken features, and the brand damage that follows.
How confident are you in your current QA coverage?
If your AI tools are telling you everything’s fine, but you’re still firefighting bugs in production, it’s time for a second opinion. Chat to us about an audit, we’ll take a look at your current setup, uncover any blind spots, and give you a roadmap to stronger coverage - without the false promises.