There is a lot of pressure right now to put an AI model into every workflow. Most of the time, you shouldn’t. Not because models aren’t useful — they are — but because a good system uses the simplest mechanism that reliably does the job.
Here is the test we apply before reaching for a model.
Use a rule when the decision can be written down
If you can describe the decision as a set of conditions — if the order is over £500 and the customer is in the EU, route to the priority queue — use a rule. It is faster, cheaper, deterministic, and you can explain exactly why it did what it did. Rules are boring, and boring is a feature in operations.
Use a model when the input is messy and the judgment is fuzzy
Reach for a model when the input is unstructured and the decision needs judgment a rule can’t express: classifying the intent of a free-text support message, extracting fields from a PDF that never has the same layout twice, drafting a first-pass reply. These are the tasks where a model genuinely outperforms a brittle pile of conditions.
Whatever you use, wrap it in something you can trust
When we do use a model, it doesn’t get to act unsupervised. It sits inside:
- Guardrails — hard limits on what it can touch.
- A human checkpoint — for anything consequential or irreversible.
- Evaluation — we measure whether it is still making good decisions.
- Logging — every call is recorded, so you can see what happened and why.
The goal isn’t to use AI. The goal is a system that does the work correctly and that you can hand over with confidence. AI is one tool in that toolbox — used where it earns its place, and left out where it doesn’t.