The rapid rise of artificial intelligence is fundamentally reshaping the technical landscape for intellectual property professionals. Moreover, it has forever changed the way humans interact with information. In just a few years, AI-powered tools have gone from experimental to essential, transforming how companies and law firms approach complex tasks like patent research. This shift isn’t just about adopting new technology—it’s about reimagining how IP teams operate, collaborate, and deliver value.

In the world of patent research, the debate between Boolean and semantic search methods is more alive than ever. It’s fascinating how much time and energy professionals invest in crafting complex Boolean syntax—hours spent perfecting every AND, OR, and NOT—while often neglecting to give semantic search the same attention and rigor. Semantic search, which leverages machine learning to understand the context and meaning behind queries, is sometimes dismissed quickly if it doesn’t instantly replicate the results of a painstaking Boolean search. But does that comparison even make sense?