When using an AI model inside a business automation workflow, I’ve found it safer not to send the model’s response directly into the next action.
A simple pattern is:
User Input → AI Analysis → Structured Output → Validation → Business Logic → Action
For example, an AI model might extract:
- Customer name
- Service required
- Budget
- Location
- Urgency
Before creating a CRM record or triggering another action, I validate the required fields and make sure the output follows the expected structure.
This also makes it easier to handle unexpected AI responses. If validation fails, the workflow can retry, ask for clarification, or send the case to a human instead of performing the wrong action.
I’ve found that separating the AI step from the business logic makes automation workflows much easier to debug and maintain.
How are you validating structured AI output before using it in production workflows?