AI is powerful, but it is not the default answer to every repetitive task. Many of the best efficiency gains come from deterministic automation: move this data, create that record, send this notification, enforce this rule. It is cheaper, easier to test and often more reliable.
Start with the boring work
Look for copying data between systems, status chasing, recurring reports, standard notifications, file movement, approvals and predictable routing. These are often automation problems, not reasoning problems.
Use AI where judgement or language matters
AI earns its place when the task involves interpreting unstructured text, extracting meaning, drafting, summarising, classifying ambiguous information or helping a person explore complex knowledge.
Combine them deliberately
A strong workflow might use automation to collect the right information, AI to interpret one part of it, a human to approve the result, then automation to update systems and notify the next person.
Why this matters commercially
Using conventional automation where it fits reduces model cost, variability and governance overhead. More importantly, it keeps the architecture understandable.
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Automate the predictable. Use AI for the genuinely cognitive part.