Column: Structuring Success

Card sorting has been a cornerstone of information architecture for decades. This method works well because it lets real people show you what they think. You can hand participants a set of labeled cards representing pieces of content, ask them to group the cards in whatever way makes sense to them, then study the patterns that emerge. The result: a window into users’ mental models that no amount of internal brainstorming could replicate.

However, the conversation around card sorting is changing. Artificial intelligence (AI) tools can now generate category structures in seconds. Large language models (LLMs) such as ChatGPT and Claude can sort a list of 40 content items into plausible groupings without your needing to recruit a single participant. Some teams have started asking whether traditional card sorting is still worth the effort. Others have gone even further and entirely replaced participant research and information architecture with AI outputs.

Both reactions miss the mark. AI does not make card sorting obsolete, but it does change how smart teams should use it. In this first installment of my new column Structuring Success, I’ll share what AI can and cannot do for card sorting, illustrate the differences that AI can make with real product examples, and offer a practical framework for combining human insights with AI’s speed. Read More