When to use AI—and when deterministic software is the better choice

## Start with the decision, not the model Teams often ask which model to use before they have defined the decision the system must support. A more useful first question is: **how much ambiguity can this step tolerate?** Deterministic software is the right default when inputs, rules, and expected outputs can be stated precisely. AI becomes useful when the work involves language, images, incomplete context, or patterns that are difficult to encode as fixed rules. ## Choose deterministic logic when correctness is explicit Use rules, validation, and conventional software for steps such as: - checking required fields and accepted formats; - enforcing permissions, limits, prices, or eligibility criteria; - moving a record through a known state machine; - calculating totals or reconciling identifiers; - producing an auditable result from stable inputs. These steps benefit from repeatability. The same valid input should produce the same output, and failures should be explainable without interpreting a probabilistic response. ## Choose AI when the input is ambiguous AI can help when a person would otherwise need to interpret unstructured information. Examples include classifying an inbound request, extracting candidate fields from a document, summarizing a long conversation, or drafting alternatives for a reviewer. That does not make the AI output a final decision. It makes it a proposal inside a controlled workflow. NIST’s AI Risk Management Framework emphasizes governing, mapping, measuring, and managing risk across the system rather than treating the model as an isolated component. ## Combine both for production workflows A practical pattern for AI-assisted workflows is hybrid: 1. deterministic code validates access, schemas, and required data; 2. AI handles the ambiguous transformation; 3. deterministic checks reject malformed or disallowed outputs; 4. a person reviews cases whose impact or uncertainty is high; 5. logs retain the input, output, version, and final decision. This separation makes failures easier to locate. If a record is rejected, the team can distinguish a missing field from a low-confidence interpretation or a human override. ## A five-question decision test Before adding AI to a step, ask: 1. Can the desired result be expressed as stable rules? 2. What happens when the result is wrong? 3. Can the output be checked automatically? 4. Is a human available for exceptions? 5. What evidence must be retained for audit and improvement? If the first answer is yes and the others are unclear, deterministic software is usually the safer choice. If ambiguity is unavoidable and validation is possible, an AI-assisted step may be justified. ## Puna Tech’s operating perspective We treat AI as one component of an operational system. The implementation should expose inputs, validation, overrides, and failure states to the people responsible for the process. The goal is not maximum automation; it is a workflow the team can understand, operate, and improve.

When you need that hybrid pattern on a live process—not a demo—see how we approach process automation and AI-assisted workflows.

A useful pilot starts with one bounded decision, a representative test set, explicit acceptance criteria, and a manual fallback. Expand only after the team has observed real error patterns and agreed on ownership.