In many discussions, I find that context is often being referred to as a synonym for more data, but more data doesn't necessarily mean more context. Organizational theory is a good way to think about context, which breaks enterprise knowledge into four categories: know-what, know-how, know-why, and know-who. Take a sales deal. 𝗞𝗻𝗼𝘄-𝘄𝗵𝗮𝘁 is the account plan, pricing, security questionnaires, and redlines. 𝗞𝗻𝗼𝘄-𝗵𝗼𝘄 is how to move the deal forward, like when to involve security and how to sequence the work.  𝗞𝗻𝗼𝘄-𝘄𝗵𝘆 is rationale, like which objections indicate real risk and which approvals are routine.  𝗞𝗻𝗼𝘄-𝘄𝗵𝗼 is the social knowledge, like who decides, who has handled a similar issue in the past, and who needs to be involved. Most of what AI can retrieve today falls in the know-what category. The rest is trapped in scattered conversations, unwritten routines, and informal human networks. We've spent the last 7 years at @Glean building toward this gap. Our context layer to connect know-what, know-how, and know-who, while inferring know-why from how work actually happens. An AI tool with access only to know-what can find the account plan but will fall short in telling you who to involve, which objection deserves attention, or what to do next. It’s the difference between knowing the account, and knowing how to move it forward.