SmartBear vice president of AI and architecture Fitz Nowlan explains why maintaining software integrity in the age of artificial intelligence requires organizations to double down on testing and quality assurance. He discusses how AI-driven development increases risk and why stronger QA practices are essential for reliable software delivery.

Nowlan argues that AI-driven development increases risk in a few ways. More code gets produced, more quickly, which means more surface area for defects and more opportunity for issues to slip into production. He also notes that relying on traditional, manually authored testing approaches can’t keep up with the churn created by AI-generated code. As a result, organizations need to “double down” on testing and quality assurance, and they need to modernize how QA is done.

A central theme of the conversation is the rise of AI-powered QA. Nowlan explains why the same wave of automation transforming software creation must also transform test management, test execution, and even test authoring. He points to functional specifications—requirements and definitions of correctness—as the source of truth that can guide both what gets built and what gets tested, especially as roles blend across development, QA, DevOps, and product management.

The discussion also tackles a common concern: can you use AI to test AI-written code without “letting the fox guard the henhouse”? Nowlan says the key isn’t necessarily a different model, but a different structure—separating the frameworks and prompts used for QA from those used for building features, and expanding validation beyond just code to include operational signals like latency and system behavior.

Finally, Nowlan looks ahead to “vibe coding” and citizen development, where prompts and specs matter as much as source code. His message: speed is great, but reliability still wins—and in an AI-driven world, stronger QA practices are the only way to maintain software integrity.