Conventional patent creation approaches fall short when the pace of business change accelerates. They were developed to capture inventions from stable, ‘steady state’ businesses, not to shape the patent portfolio an evolving business requires.
Progressive IP organizations close this gap by adding IP Catalyst capabilities. The Catalyst – a capability either embodied in a dedicated role or made part of the responsibilities of existing IP roles – works proactively, building close relationships with business and technology stakeholders and inventor communities, and taking responsibility for engineering portfolios with clear business intent rather than waiting to capture whatever happens to be invented.
The IP Catalyst capability is difficult to scale. It requires a rare combination of business acumen, technical depth across multiple domains, and working IP knowledge. These multi-skilled profiles are hard to find and even harder to retain.
LLMs and AI can bridge the scaling gap. AI-enabled portfolio engineering amplifies the Catalyst capability by carrying the information load and broadening the reach of each individual. It gives IP Catalysts the decision support needed to be both business-driven and effective at scale.
Not just any AI will do. Both generic LLMs and purpose-built but disconnected AI tools tend to create noise, false confidence, and disconnected outputs. What is needed is purpose-built AI that embeds in the company's workflows and goes beyond language processing: AI that connects business strategy, technology landscapes, and patent positions into a single, coherent picture.
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Previously published May 2026.