Key takeaways
- Feasibility depends on APIs, webhooks, and permissions
- Give the AI layer the least access it needs
- Budget for maintenance when upstream APIs change
Start with the interfaces
For each system involved, find out how data gets in and out. A documented API or webhook support is the best case. A scheduled export is workable. A tool with neither may be a poor candidate until that changes.
Do not assume that because a vendor has an API, the thing you want is exposed through it. Check the specific actions and fields.
Decide what the AI may touch
Treat the AI layer like a new employee: it gets only the access its job requires. Read-only where possible. Write access for specific actions, with logs. Anything involving money or customer communication should have an approval step until you trust the behaviour.
Map the data
Fields rarely line up neatly between systems. Decide which system is the source of truth for each piece of data, how records are matched, and what happens on conflicts or duplicates. This unglamorous mapping is where most integration time goes.
Plan for failure and for change
Calls will time out, rate limits will bite, and vendors will change their APIs. Build retries and alerts, and decide who looks when something breaks. A support arrangement that covers integration updates is not a luxury here.