AI & decision-making · JNAGA perspective
When is a rules-based workflow better than an AI model?
Using AI is not the objective. The objective is a dependable way to complete the work.
Published 25 September 2026
Imagine incoming requests must be routed by a clearly recorded service type and a fixed approval limit. The conditions are explicit and changes are controlled.
A rules-based workflow may be easier to test and explain. An AI model could still help people interpret unstructured descriptions, but the final routing rule should remain visible and auditable.
Look for stable, explicit decisions
If the inputs are well defined and the decision can be written as a small set of rules, deterministic software gives the business a clear account of why an action occurred. It can be tested against known cases and changed through a controlled process.
A model may be helpful when the work involves varied language, ambiguous documents or drafting. That does not mean the model should also make every subsequent decision. Separate interpretation from the rule that authorises an action.
Compare the full operating burden
A model-based approach needs representative tests, monitoring, error handling and review of changing outputs. A rules engine needs maintained definitions and exception handling. Neither is maintenance-free. Compare them against the variability and importance of the work, not the novelty of the tool.
A hybrid can be sensible: a model proposes a category, a person resolves uncertainty, and explicit rules govern what happens next. The handoffs must be designed, not assumed.
Prefer the simplest dependable path
If a conventional rule solves the problem well, using a model adds complexity without necessarily adding value. If rules collapse under genuine variation, test assistance in a bounded way. The right choice is the one the organisation can understand, correct and responsibly own.