Stefan Brehm

For decades, patent data has primarily been treated as something to search: prior art search, FTO search, monitoring, invalidity search, landscape search. These tasks are important — but they represent only a fraction of what patent information can actually do for a company.

Patent data is one of the richest technical and competitive knowledge sources available. It can teach R&D teams how technologies work, reveal design alternatives, expose competitor intent, support stronger product decisions, identify white spaces, guide portfolio strategy, and help companies actively shape or block competitive moves. In that sense, patent information is not merely a legal resource. It can become a system for competitive excellence.

The current bottleneck is not a lack of data. It is access, interpretation, and activation. Much of the knowledge remains locked inside expert workflows, specialist tools, and time-consuming analysis processes. As a result, companies often use patent data defensively and selectively, rather than continuously and strategically.

AI changes the equation — but not in the superficial sense of faster searches or automatic summaries. The deeper opportunity is to make patent knowledge usable across IP, R&D, product, strategy, and management. If done right, AI can help turn patent information from a search task into a business capability: supporting better inventions, better product design, better competitive positioning, and ultimately a stronger contribution from IP to company value.

This talk argues that the AI transformation of IP has not truly started yet. The first wave made existing workflows faster. The next wave will redefine what companies expect from patent data.