You Don’t “Build Trust” in GenAI You Manage Risk

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Let’s stop pretending you can trust generative AI.

I just read another article claiming that with the right QA methodology, you can “ensure the delivery of high-quality AI systems.”

That’s not just optimistic, it’s misleading.

 Here’s the problem:

Generative AI doesn’t produce consistent output; it’s not deterministic, it’s not explainable and no framework in the world changes that.

Sure, you can evaluate GenAI, measure performance against benchmarks, and even tune prompts with layers of human review.

But don’t confuse that with “trust.”

Trust implies predictability. AI is probabilistic by nature.

You can’t regression-test your way out of hallucinations. You can’t automate your way to ethics. And you sure as hell can’t claim “quality assurance” when the model behaves differently every time you run it.

Here’s what’s real:

  • GenAI needs risk management, not false confidence.
  • QA teams must shift from pass/fail to risk analysis.
  • Business leaders need to stop asking, “Can we trust the model?” and start asking, “What’s the cost when it fails?”

Trust is earned through consistency. GenAI, by design, is anything but consistent.

So let’s stop selling magic. And start building systems that account for failure.