Start with the problem
The question should not be “where can we add AI?”. It should be “which task consumes time, which information is hard to find and which decision needs better data?”. Only then can we evaluate whether AI is the right tool.
Uses with practical value
Searching large datasets, summarisation, categorisation, content preparation, enrichment and assistants inside ERP or CRM systems are examples where AI can save real time. Outputs should still sit inside a controlled workflow and should never be treated as automatically infallible.
When a rule or API is better
If a process has a clear rule, such as “when stock drops below X, send an alert”, there is no reason to use AI. A deterministic rule is cheaper, predictable and easier to test.
Good architecture combines the right tools instead of trying to solve every problem with the same technology.
Data and control
Before connecting an AI tool to business data, access, personal data, logging and what may be sent to a third-party service all need consideration. Convenience should not override security and data governance.