The Cases Where AI Is the Expensive Wrong Answer
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The test
Ask the person who does the job to describe how they decide. If they produce a set of conditions, that is a rule engine. If they say “you just know from reading it”, that is a model.
Most business processes are more rule-shaped than people expect, and rules are cheaper on every axis that matters.
What rules do better
| Property | Rules | Model |
|---|---|---|
| Cost per decision | Effectively nil | Real and recurring |
| Speed | Instant | Hundreds of milliseconds up |
| Consistency | Perfect | High, not perfect |
| Auditability | Trivially explained | Requires effort |
| Testing | Exhaustive | Statistical |
| Changing it | Edit a condition | Adjust and re-evaluate |
Where models genuinely earn it
- Free text where meaning matters — emails, notes, reviews
- Documents with varied layouts, where position cannot be assumed
- Classification with fuzzy boundaries and many categories
- Summarising or drafting for a human reader
- Matching things that are the same but written differently
The hybrid is usually right
Model for the messy part, rules for the decision. Extract the fields with a model; decide what to do with them using logic you can read, test and defend.
That keeps a probabilistic component out of the audit trail while still handling input that rules cannot parse.
An honest example
A client wanted AI to decide which orders needed manual review. Their expert described six conditions in four minutes. We built the six conditions: two days, no running cost, perfectly auditable.
The model then handled the part they could not specify — reading the free-text delivery notes for special instructions. That is the right division of labour.
Frequently asked questions
What if the rules get complicated?
Can we start with rules and add AI later?
Do you lose the project by saying this?
How do we know if our problem is rule-shaped?
Been told you need AI?
Describe the decision to us and we will tell you whether a rule would do it better and cheaper.
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