AI assistant or automation: which one does the job?
Many business problems described as an AI opportunity are actually workflow problems. Choosing the simpler tool first usually produces a system that is easier to understand, test and maintain.
Use deterministic automation when the rules are known. Consider AI when the job genuinely needs interpretation, extraction, drafting or flexible language — and keep the boundary visible.
Ask whether the work is rules or judgement
If a task can be expressed as stable conditions — when this happens, check these fields, then do that — ordinary automation is often the cleaner option. It behaves predictably and can be tested against exact outcomes.
AI becomes more useful when the input is less structured and the job needs interpretation: summarizing a document, classifying a nuanced enquiry, drafting from approved context or retrieving relevant knowledge from a larger source set.
The best system may combine both
An AI step can sit inside a deterministic workflow. The workflow decides when the model is called, what context it receives, what shape the output must take and what happens when confidence or required information is missing.
- Rules control triggers, permissions, retries and state changes.
- AI handles the narrow step that benefits from flexible interpretation or language.
- Validation checks the output before anything important happens next.
- A person handles sensitive, ambiguous or unsupported cases.
Choose the least complex system that solves the problem
Do not add a model because AI sounds more advanced. Add it only when it improves a defined task enough to justify the extra uncertainty, cost and review it introduces.
The strongest architecture is often boring in the right places: rules where rules are enough, AI where it earns its role and clear human control around both.