AI Detects Accessibility Bugs Well, But Fixing Them Still Requires Human Expertise

AI tools are increasingly capable of identifying web accessibility failures, explaining them, and even generating code fixes, but a developer's real-world experience reveals a critical gap between detection and sound remediation. While working on an accessible predictive search component for a Shopify storefront, the AI correctly flagged genuine bugs such as broken keyboard navigation and misconfigured ARIA live regions. However, it also proposed fixes that were technically plausible yet fundamentally wrong, including routing focus through Tab in a combobox widget, which directly contradicts established interaction patterns for screen-reader users. A key concern is that incorrect fixes were delivered with the same confident, fluent tone as correct ones, giving no indication of which recommendations might fail in practice. The author concludes that AI is a useful accelerant for accessibility work, but deciding what the right fix should be still requires human expertise, assistive technology testing, and an understanding of how disabled users actually experience an interface.