The 30-Second Version
The most valuable AI literacy skill is knowing when AI is appropriate and when it is not. A good recommendation is conditional: it names the use case, risks, controls, and evidence required before deployment.
The AI Decision Matrix
AI Use Case Decision Matrix
flowchart TD
A[Classify the use case] --> B{High stakes?}
B -- No --> C{Standardized task?}
B -- Yes --> D{Standardized task?}
C -- Yes --> E[Use freely with normal review]
C -- No --> F[Experimental pilot only]
D -- Yes --> G[Design carefully with HITL, audit, bias tests]
D -- No --> H[Avoid or research carefully]
flowchart TD
A[Classify the use case] --> B{High stakes?}
B -- No --> C{Standardized task?}
B -- Yes --> D{Standardized task?}
C -- Yes --> E[Use freely with normal review]
C -- No --> F[Experimental pilot only]
D -- Yes --> G[Design carefully with HITL, audit, bias tests]
D -- No --> H[Avoid or research carefully]
High stakes + high standardization: design carefully. Examples: fraud flags, credit decision inputs, regulated customer treatment. Require human-in-the-loop, audit logs, bias testing, and explainability.
Low stakes + high standardization: use freely with normal review. Examples: meeting summaries, internal drafts, ticket classification.
High stakes + low standardization: avoid or research carefully. Examples: novel legal interpretation, rare medical diagnosis, one-off employment decisions.
Low stakes + low standardization: experimental. Examples: brainstorming, early research, ideation.
Five Questions Before Deployment
1. Reversibility
If the AI output is wrong, can we fix it without lasting harm?
2. Auditability
Can we explain what happened later: input, model, version, retrieved context, decision, reviewer, and action?
3. Failure Cost
How often will the system fail, and what is the cost of one failure?
4. Regulatory Exposure
Does this use case touch credit, employment, healthcare, insurance, biometrics, children, regulated advice, or other high-risk domains?
5. Data Risk
What data is sent to the model, where is it processed, who can see it, and what happens if it leaks?
Quick Reference
| Use case | Risk | Required controls |
|---|---|---|
| Drafting internal documents | Low | Standard review |
| Internal document summarization | Low | Spot checks |
| Customer-facing chatbot | Medium | Output scanning, escalation, monitoring |
| Fraud detection flag | High | Human review, audit log, bias testing |
| Credit decision input | High | Compliance review, bias testing, human final decision |
| HR screening | High | Bias testing, human review, legal review |
| Regulatory interpretation | High | Expert verification |
| Security-critical code generation | High | Security review and tests |
The AI-Literate Recommendation Format
We can use AI for this use case if:
1. A human reviews high-impact outputs before action is taken.
2. We log model version, inputs, retrieved context, and reviewer action.
3. We test for bias and prompt injection before launch.
4. We monitor drift and failure rate after launch.
5. We document regulatory obligations and owners.
Without those controls, I would not recommend deployment.
The goal is making a decision that survives scrutiny from engineering, legal, risk, customers, and leadership.
Turn the five questions into requirements. Each “yes, if” condition should become an acceptance criterion or release dependency.
Use the matrix during intake. It prevents low-risk ideas from getting buried and high-risk ideas from sneaking through as normal features.
Ask for conditional recommendations. “Yes, with these controls” and “no, because the failure is irreversible” are both AI-literate answers.
Path Summary
01 How AI Fails -> know the six failure modes and fixes
02 Model Limits -> design around constraints
03 Privacy Risks -> know what data moves where
04 Bias Risk -> test fairness before deployment
05 Prompt Injection -> defend the AI attack surface
06 2026 Expectations -> know literacy expectations by role
07 Serious Training -> build a program that changes behavior
08 Decision Framework-> decide when AI belongs