An integration is a working connection between tools, so data moves and actions happen without a person copying and pasting by hand. In AI work, an integration might let an agent read email, pull a transcript, create a task, or update QuickBooks, through an API, an MCP server, or a connector. The connection itself is only one part of the system.
Picture the plumbing in a building. A sink is useful only if water can get in and waste can get out. The pipe stays hidden, and it’s the reason the sink works. A pipe can also connect two places and still carry the wrong water, at the wrong time, with no shutoff valve.
Why you care
AI integration is really data engineering, process mapping, and change management. You have to know where the data lives, what the work should do, and who can take which action; if those are fuzzy, the integration just moves confusion faster. Bad integrations create silent risk: data in the wrong place, actions with too much permission, a team that thinks something is handled when nobody checked. The first question is always what work should flow through this connection, and what should never.