Somewhere in the last two years, "the AI flagged it" quietly became a stand-in for "we checked." It shows up in incident reports, vendor risk reviews, hiring decisions, and customer refund logs. It sounds like due diligence. In an actual audit, it's closer to a shrug, and regulators have started treating it that way. The Phrase That Doesn't Hold Up "AI approved it" answers a different question than the one auditors, regulators, and courts actually ask. They don't want to know what the system did. They want to know who was accountable for letting it do that, and whether a qualified person reviewed the outcome before it affected a customer, an employee, or a filing. A model output is not a control. A person checking that output, on a defined cadence, with the authority to override it, is a control. Those two things get conflated constantly, and the gap between them is where compliance programs are quietly failing. The Federal Trade Commissi...
Ask five different engineers what's slowing down their AI rollout, and at least three will say the same thing: integration. Not the model. Not the prompts. The plumbing. Getting an AI assistant to actually talk to your CRM, your ticketing system, or your internal database has, until recently, meant custom code for every single connection. Multiply that across a dozen tools and a handful of AI platforms, and you've got a maintenance nightmare nobody signed up for. That's the problem Model Context Protocol was built to solve. What MCP Actually Is Strip away the jargon and MCP is refreshingly simple: it's a shared language that lets AI models talk to outside tools and data sources without a custom translator built for each one. Anthropic released it as an open standard in late 2024, and the nickname that's stuck to it says a lot — people call it the USB-C port for AI. Just like you don't need a different cable for every device anymore, MCP means you don't...