Integrations

How AI connects with your other systems

An integration is what lets AI act on the systems where your work actually lives, rather than waiting for you to copy information back and forth by hand.

Most early AI use involves a lot of pasting: pulling information from one system, putting it into a chat window, getting an answer, and copying it somewhere else. Integrations remove those manual steps. They give AI a direct connection to the tools and data it needs so it can read, write, or trigger actions without a human as the middleman.

Integrations vary widely in complexity. At the simple end, a shared folder or a live document is an integration: AI can read it directly. At the more complex end, an API connection lets an agent pull from a database, update a CRM record, or send a message in a tool you already use. The value is the same in both cases: the AI acts on real, current information instead of stale pastes.

The practical question before building integrations is what work the integration is for. A connection that gives AI access to data it never needs is complexity without benefit. Map the task first, then build only the connections the task requires.

Make it visibleIdentify one AI task where you regularly copy information from a tool (a spreadsheet, a CRM, a folder) into a chat. Find out whether that tool has an API or export format that could feed AI directly, and look up what access it would require.

Why AI needs this

Each part of your work matters to AI in a specific way. Some of it is context a tool needs before it can help, some of it is work a tool can take on, and some of it is judgment that should stay with you.

Access to live data

An AI working from a paste you made yesterday is working from the past. A direct integration means it always acts on what is current.

Fewer manual steps

Every time a human copies information from one system to another to enable AI, that is a step that breaks, gets skipped, or introduces errors. Integrations eliminate that fragility.

Actions that stick

An AI that can only produce text in a chat window cannot update your CRM, send a message, or move a file. Integrations are what let AI change the systems where the work actually happens.

Security perimeter

An integration needs defined access: what the AI can read, what it can write, and what is off-limits. AI needs those boundaries built into the connection, not assumed.

Questions to explore

Use these on your own or in a group. There are no right answers, only better conversations.

Which AI tasks in your work require the most copy-pasting between systems to enable?
If AI could read directly from one system you use today, which would produce the most value?
What data does AI regularly need that you have to fetch and paste manually every time?
Which systems hold the most important data AI does not currently have access to?
What access controls would you need in place before you connected AI to a sensitive system?

Readiness traps

Connecting AI to everything before you know what it needs. Over-connected systems are harder to audit, harder to secure, and create more surface area for things to go wrong.
Assuming the integration will stay working. APIs change, permissions expire, and systems get updated. An integration with no monitoring is one breaking change away from silent failure.
Building integrations before the underlying process is stable. Connecting a tool to a workflow that is still changing means rebuilding the integration every time the workflow evolves.