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AI versus deterministic automation

The most common design mistake is asking a language model to do a job that a database and a rule could do perfectly. The reverse — forcing rigid rules onto genuinely fuzzy input — is just as costly.

A simple dividing line​

Use AI for turning messy, human input into structure:

  • understanding free text, speech and images;
  • extraction, classification, translation, summarisation;
  • matching and ranking when the inputs are noisy;
  • proposing a draft for a human to approve.

Use deterministic logic for anything that must be correct, auditable or legal:

  • prices, quantities, tax and totals;
  • availability, permissions and quotas;
  • deadlines, compatibility and safety rules;
  • state transitions and approval policy;
  • the system of record itself.

The pattern: AI proposes, rules dispose​

A robust workflow almost always looks like this:

  1. AI converts the input into a structured proposal with a confidence signal.
  2. Deterministic rules validate that proposal against authoritative data.
  3. A human approves anything uncertain or high-stakes.
  4. Only validated, approved data enters the system of record.

This keeps the parts that must never be wrong out of the model's hands, while still using AI where it genuinely shines.

Why it matters commercially​

Deterministic cores are your defensibility and your safety story. "The AI drafts; your prices, rules and approvals are enforced by code you control" is a sentence that wins trust in exactly the regulated, money-handling contexts where the work is most valuable.

Next​

For the approval half of the pattern, see human approval and accountability. For where AI should not appear at all, see when not to use AI.