P2P

73 I L T A N E T . O R G typically fall short of the end-user expectations. This is mainly caused by one thing in particular: content is not yet ready. This lack of data readiness stems from an overall – and often historic - lack of data governance. Data is typically over shared, under tagged, lacking in consistent version control, and abundantly duplicated and outdated, to name some problems. Another practical issue is that the content is not available in a system or form that the AI can work with. For instance, Microsoft 365 Copilot will require high- quality data to be available in SharePoint Online so it can be indexed in the Microsoft Graph to be made available via the Semantic Index. For those that have worked on enterprise search solutions, you will notice some similarities in these challenges and indeed, many of the same hygiene factors that an efficient and trustworthy search solution requires will need to be in place for AI. In this context, we talk about an AI Sweet Spot, which is "good content" that can be used for anchoring AI and providing better "grounding" during the prompt process. At one end of the scale, content is just a binary object (a file) with a little bit of metadata (such as a filename, date and author) which provides little context outside of the actual document content. At the other end of the scale, a file might be tagged with related sectors, legal subject areas, and legal jurisdiction, turning it into content that can be used as knowledge in the proper context. From that, content can now be connected to create expertise. This end of the scale, where content exists as knowledge and can be linked together as expertise, is where AI can thrive. This is the AI sweet spot.

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