Related question
What quality problems should we expect from AI-generated software and solutions?
Short answer
Expect discipline to be missing. After reviewing a lot of AI-generated builds, the enthusiasm is there and the testing is not. Inconsistent labels, no attention to timing or refresh behavior, key functions buried, and sample data treated as adequate for testing.
We are repeating mistakes the software industry learned to avoid decades ago. Developers are excited about what AI can do and rush to create builds and add features. That is the good news. The discipline is not baked in. Testing, quality control, and simple usability appear to be afterthoughts or skipped entirely.
This is a governance question as much as a technical one, because the person who has to live with a poor-quality system is rarely the person who approved it. The warning signs are consistent. Different labels for the same thing in different places. No consideration of flow timing or page refreshes. Priority elements buried three levels down. Delivering high volume with a low match to the actual criteria. Assuming AI-generated sample data is adequate for testing. One-size-fits-all assumptions that ignore how different the users actually are.
The result is solutions that technically function and miss the mark. The minimum bar should not be "gets a reasonable answer." It should be "delivers real value to the people who have to use it."
Quality control is not glamorous. It is what turns a clever technical solution into something a business can rely on.