How to connect AI to the systems your company already uses

Start with a specific process

Choose the task AI should support: transcribe a call, find information or prepare a suggestion for review. Identify the input data, responsible person and record that receives the result.

Map the integration available in the current system: an API, event, file or database procedure. That contract guides the new capability and its access limits.

Separate reading, suggestions and execution

Reading information, suggesting a response and changing a record have different effects. The integration must authorise each action and require the fields needed to execute it.

The model can interpret intent. The service that writes to the system must check identity, permission, scope and process rules.

Define what confirms an action

If a connection drops after submission, the system may not know whether the action occurred. An immediate retry can duplicate a record or message.

Use source identifiers and a way to check state. In integrations I implemented, reconciliation recovers pending requests and the cursor advances after publication. An uncertain external result receives separate handling.

Preserve the service during AI failures

A call, service interaction or record can continue while transcription waits for capacity. The interface must report the wait, and the queue must preserve work that can still be processed.

In an audio layer I implemented, compressed segments are held in a durable journal. Recovery uses confirmed files after restart. This separates capture from processing speed.

Deploy in verifiable stages

• Confirm the current system's contract and authorisation for each action.
• Assess the flow with representative data and failure scenarios.
• Define the change window, reconciliation and rollback before expanding use.
• Measure waiting time, errors and the full process duration to guide operations.