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Choose the method one workflow step at a time. Use deterministic logic when inputs, rules, and acceptable outputs can be specified clearly; consider AI when a step must interpret ambiguous or open-ended material. Then test the AI step, validate what it returns, and match human oversight to the consequences of an error.
Start with the step, not the whole workflow
A workflow does not have to be entirely AI-driven or entirely conventional software. A process can use explicit code for stable transformations, AI for interpretation, and code or human review to check what happens next. The useful question is not “Should this workflow use AI?” but “Which method can handle this particular step, and how will we know it worked?”
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For each step, write down its purpose, inputs, expected output, and the cost of an incorrect result. That small specification makes it easier to compare methods and decide where checks or escalation belong.
When deterministic logic is the better starting point
If a decision can be expressed as rules, calculations, or predetermined steps, begin with deterministic logic: ordinary code or a fixed validation rule. NASA’s Software Engineering Handbook states, “If rules, computations, or predetermined steps can be explicitly programmed, it is not necessary to use AI/ML” (NASA Software Engineering Handbook, section 3.1).
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This is a design heuristic, not a claim that deterministic software is always cheaper or superior. It is a useful fit when the inputs and permitted results are sufficiently clear to specify and test.
- Calculating a total from known numeric fields.
- Checking that required fields are present and have the expected types.
- Applying fixed thresholds, permissions, or allowed-value lists.
- Converting data into a defined format or routing a request according to explicit criteria.
When AI may be useful
Consider AI when the step needs to interpret meaning or context that is difficult to enumerate in fixed rules—for example, extracting the intent of an open-ended message or classifying material whose wording varies. The case for AI is stronger when a person can define what an acceptable result looks like and the system can be tested against representative examples.
AI output is not proof of correctness. A result that looks plausible can still be wrong, and a check based only on format or permitted values may not catch a mistaken interpretation. The Singapore Government Responsible AI Playbook notes that evaluation methods “are not mutually exclusive” (Singapore Government Responsible AI Playbook, “Evaluation methods”). Combine methods where that improves confidence: for example, evaluate interpretation quality and separately enforce required fields and allowed actions in code.
Use a decision sequence for each step
- Specify the task. Record its purpose, inputs, expected output, and the consequence of an incorrect result.
- Ask whether rules are explicit. If the decision is a fixed transformation or can be described in clear rules, implement deterministic logic first.
- Identify interpretation needs. If the step depends on open-ended meaning or context that rules cannot reliably capture, consider AI and define the scope of what it may decide.
- Choose evaluation criteria. Test against examples representative of expected use, including difficult or varied inputs. A successful demonstration on a few cases does not establish performance under changed conditions.
- Add checks and failure handling. Where feasible, validate outputs for required fields, types, ranges, evidence, permissions, and allowed actions. Specify whether a failed check stops the workflow, triggers a defined retry, or sends the case for review.
- Set oversight and monitoring. Decide who reviews, who can intervene, and how performance and failures will be monitored after deployment.
Match the method and oversight to the risk
Compare candidate designs across the factors that matter for the particular process. A step that is easy to reverse and has a readily checkable output presents a different oversight problem from one that can trigger a consequential, hard-to-reverse action.
| Question | What to consider |
|---|---|
| How variable are the inputs? | Stable, structured inputs may suit fixed rules; varied language or context may require interpretation. |
| Can correctness be checked? | Consider whether expected outputs, constraints, or required evidence can be tested. |
| What is the cost of error? | Assess potential harm, reversibility, and the amount of human review warranted. |
| Can the result be audited? | Consider whether the decision and relevant inputs can be recorded and examined later. |
| Can performance be evaluated over time? | Plan for representative testing and monitoring, especially as inputs or operating conditions change. |
NIST’s AI Risk Management Framework (AI RMF 1.0) says that “Validity and reliability for deployed AI systems are often assessed by ongoing testing or monitoring that confirms a system is performing as intended” (NIST AI RMF overview). NIST guidance is voluntary, not itself a legal requirement; the overview says the framework is under revision. Applicable obligations depend on jurisdiction, sector, the action being automated, and the system’s behavior.
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One workable design is deterministic preprocessing and permission checks, followed by AI interpretation only where needed, then deterministic validation and policy gates. If a check fails—or the outcome is consequential—stop or send the case to a person who can make an informed decision. Only then should the workflow take a logged, controlled action.
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This is an illustrative design pattern, not a universal template. Human review should be meaningful: reviewers need enough context and authority to question or override the output. NIST’s Generative AI Profile identifies automation bias as a risk, in which people may over-rely on or overestimate AI output (NIST AI 600-1, Generative AI Profile). An approval click that does not involve informed scrutiny is not an effective safeguard.
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What to document before deployment
- Which steps use deterministic rules, AI, or human judgment—and why.
- Each step’s inputs, expected outputs, scope, and known limits.
- Evaluation criteria and the representative conditions used to test performance.
- Validation rules and what happens when a result fails them.
- Who reviews exceptions, who can intervene, and what actions the system may take.
- How outcomes and failures will be logged and monitored after deployment.
These choices should reflect the actual system and its context, not a borrowed accuracy figure or a general claim that one method is always safer. The UK Government’s Data and AI Ethics Framework provides further guidance for considering data and AI use in context (UK Government Data and AI Ethics Framework).
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