Implementation is an operations project
AI implementation is less like installing a plugin and more like changing an operating procedure. The model is only one component. The system also needs data access, permissions, prompts, evaluation, logging, review rules, and exception handling.
Most failed deployments do not fail because the model is useless. They fail because the model is placed in the wrong workflow or given poor context.
Map the workflow before choosing the model
Start by mapping the current workflow from trigger to outcome. What starts the work? Which systems are opened? Which documents are consulted? Which decisions are made? Which outputs matter?
Only after that should you choose the model, integration pattern, and interface. A workflow map makes the technical decisions sharper.
Integration turns AI into a product
AI integration services connect the model to the business context: CRM records, files, inboxes, calendars, product data, standard operating procedures, databases, and internal tools.
Context should be selective. More data is not automatically better. The system should retrieve the right slices of information for the job.
Minimum launch controls
- Read-only access before write access where possible.
- Separate credentials and least-privilege permissions.
- Logs for inputs, outputs, actions, errors, and human overrides.
- Test cases from real messy historical examples.
- Clear thresholds for automatic action versus review.
- Rollback paths for anything that changes records or contacts customers.