Use conventional code for clear, stable rules that must behave predictably and be tested repeatedly. Consider AI when a task depends on interpreting variable or unstructured input, but only if it performs well on representative cases. In either design, code should validate inputs and outputs, enforce permissions and business rules, and route uncertain or high-impact decisions for review. There is no universal cutoff: the right boundary depends on the task and the consequences of failure.
Start with the task, not the technology
Before choosing AI or conventional code, define what the system receives, what it must produce, what counts as an error, how repeatable its answers need to be, and what a wrong result could affect. The National Institute of Standards and Technology (NIST) says whether AI is appropriate or necessary should be decided for the particular context and purpose. Its AI Risk Management Framework (AI RMF) 1.0, released January 26, 2023, is voluntary guidance—not a universal formula for dividing work between models and software. NIST AI Risk Management Framework
Use conventional code for explicit, stable rules
When requirements can be expressed as conditions and checked against known examples, conventional code is usually the practical default. It is a good fit for operations such as verifying required fields, checking a value against an allowed range, applying a permission rule, or calculating a result from fixed inputs. These rules are easier to encode as repeatable tests and to enforce consistently.
This is an engineering recommendation, not a claim that ordinary code is error-free or always more reliable. Software can still contain bugs, and the choice should be judged against the requirements and operating conditions of the specific task.
#1 Best Overall
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Consider AI for interpretation, then test the case
AI may be useful when the task involves interpreting natural language, images, or other inputs whose possible forms are difficult to enumerate in advance. That makes AI a candidate to evaluate, not an automatic reason to deploy a model. Test it on examples representative of the real use, including incomplete, unusual, and potentially misleading inputs. Define what level of performance is acceptable before relying on it.
AI also introduces risks that may be data-dependent: training data may not match the deployment context, behavior may be difficult to predict, and data or concepts may drift over time. Those risks can create maintenance needs even when the system initially performs acceptably. NIST AI RMF Playbook
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Put deterministic safeguards around consequential actions
Judge the deployed system as a whole, not just the model. AI can produce an interpretation or recommendation while conventional code applies the checks that determine whether an action is allowed. For consequential actions, make the system verify permissions, required fields, acceptable ranges, and business constraints before proceeding.
Match human oversight to the possible harm. If the system cannot detect or correct errors reliably, provide a route for human intervention. Require confirmation or review when the potential impact warrants it; serious safety risks call for especially urgent and thorough management. Make clear who owns review, escalation, override, and correction.
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Compare the options against the same criteria
Set priorities and acceptable thresholds for the specific use case. NIST cautions that trustworthiness characteristics can involve trade-offs and may not apply equally in every setting. It states: “Human judgment should be employed when deciding on the specific metrics related to AI trustworthiness characteristics and the precise threshold values for those metrics.” NIST AI RMF trustworthiness guidance
| Criterion | Questions to ask |
|---|---|
| Correctness and reliability | Does the system meet the requirements under expected conditions? What errors occur on representative cases? |
| Robustness | How does it handle unusual, incomplete, adversarial, or out-of-distribution inputs? |
| Failure impact and safety | Who or what could an error affect? How severe and reversible would the consequences be? |
| Testability | Can behavior be covered with clear, repeatable tests? Which parts are difficult to evaluate? |
| Explainability and auditability | Can a reviewer understand, document, and reconstruct why the system acted? |
| Privacy and security | What sensitive information is collected, exposed, retained, or acted upon? |
| Maintenance | Could rules, data, models, or conditions change? How will drift be noticed? |
| Human oversight | Who reviews, escalates, overrides, and corrects uncertain or wrong decisions? |
Revisit the boundary when conditions change
A decision that fits one version of a task may not fit after the data, model, users, environment, or intended use changes. NIST’s guidance treats trustworthiness across design, development, deployment, use, and evaluation; testing and monitoring should check whether a deployed system continues to perform as intended. Plan who will notice changed conditions and what corrective action follows. The framework’s resource pages describe revision work, so check NIST’s current framework status before using it as guidance for a regulated or otherwise high-stakes setting.
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In practice, keep a responsibility in deterministic code—or with a person—if the AI option cannot meet a defined quality bar, cannot be monitored in its actual context, or has no safe escalation path. NIST does not establish a numeric break-even point at which AI becomes preferable to code; thresholds have to be set for the particular application.
Quick Recap
Best Value
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