AI Architecture

Most companies don’t fail at AI because of bad models. They fail because they never had a strategy. That might sound blunt, but it’s what shows up again and again in enterprise settings. Teams jump into pilots, experiment with tools, hire a few data scientists—and then stall. Not because the tech didn’t work, but because the system around it didn’t exist. If you’re leading AI inside an organisation, you don’t need more tools. You need structure. ...

Your enterprise already has many channels. Humans use some. Machines use some. And now AI is asking for its own. In the last post, I explained why APIs are not enough and how MCP creates a new AI channel. If you missed that, read it here first: From APIs to AI Channels: The Next Evolution This post is the next step. We zoom out and see where this MCP channel actually fits. ...

Your system already has APIs. Documentation. Schemas. And yet an AI agent still can’t use it properly. That’s not a tooling problem. It’s a channel problem. For decades, every meaningful leap in software came from improving how we communicate with systems. It started simply. You’d walk over to someone’s desk. Ask how things worked. Knowledge lived in people’s heads. Fast if you knew who to ask. Painful if you didn’t. ...