Why Connecting AI to Real Systems Is Still Hard

Part 1 of 6 — MCP Article Series The models themselves work well. For anything self-contained — writing, summarising, generating code — they are genuinely capable. But the moment you connect an AI model to your actual systems — your order database, your payment gateway, your CRM — something changes. The model is capable. The integration is not. Every connection has to be built by hand. Every system has different authentication, different error formats, different versioning rules. And when something breaks — which it does every time an API updates — a developer has to fix it. This is the problem sitting quietly underneath most AI projects. It is not about the model. It is about everything the model needs to reach before it can do real work. Five AI applications. Three system integrations each. That is 15 integrations total. At a reasonable estimate of around forty hours per integration, that is roughly six hundred hours of engineering effort. Not to build new features. Not to ship a pro