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Discovery Is Becoming an AI-Native Workflow
Discovery in scholarly publishing is no longer a search box sitting on top of a content repository. It is becoming a workflow layer that moves users from question to understanding to evidence with less friction. That shift is the starting point of a new essay on the KGL blog, written by Hong Zhou, VP of Product Management, and Adrian Stanley, Chief Business Development Officer, on why AI-native discovery is now table stakes and what it will take for publishers to get there without losing trust.
The piece makes a case we think every publisher should be reading. Platforms and publishers should stay focused on what only they can provide: a trusted single source of truth, with the content quality, metadata integrity, rights clarity, and authoritative version of record that AI systems depend on.
The technical and commercial rails that make that content usable in AI environments are better handled by a specialist partner whose job is to keep pace with the technology. That division of responsibility is exactly what the KGL and Cashmere collaboration is built to deliver, and it matters most for the small and mid-sized publishers who cannot build bespoke AI infrastructure on their own.
Hong and Adrian also dig into what "local LLM" discovery actually means, why the market is becoming dual-channel rather than either-or, and how discovery, rights, and monetization are collapsing into a single infrastructure layer. If you are thinking about how your content will show up in AI-mediated discovery, and whether you can do it in a way that preserves control, attribution, and trust, this one is worth your time.
Read the full essay on the KGL Blog.
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