Applied AI
When should you not use AI?
The short answer
Do not use AI simply because the capability exists. Prefer deterministic software when the correct behaviour can be specified explicitly, and avoid AI where its uncertainty, latency, cost or operational burden does not improve the outcome.
When the rule can be written down
Eligibility checks, permissions, calculations, policy thresholds and many routing decisions are usually better implemented as deterministic logic. A model adds variability without adding useful intelligence.
When the failure is hard to detect
AI is more difficult to justify when a wrong output is consequential and neither the system nor the user can reliably recognise the error. In those settings the product needs stronger constraints, verification or a different architecture.
When there is no evaluation plan
If the team cannot define representative tasks, failure modes and acceptable thresholds, it cannot know whether the AI system is improving or merely changing.
When a simpler interface solves the problem
Many “AI problems” are information architecture, workflow or integration problems. Better defaults, search, forms, rules or data access can produce a more reliable result at lower cost.
When AI is still useful
AI is compelling where language, interpretation, synthesis, pattern recognition or uncertain reasoning are intrinsic to the task. The strongest products place those capabilities inside deterministic boundaries rather than asking the model to own the entire workflow.