SUPER AI GUY / WORKFLOW DELEGATION LAB
AI vs. Human Task Audit
Explore the trade-offs in delegating a task. Adjust two assumptions and inspect the formula, review plan, and limits.
← Back to AI LabPreset ratings and weights are examples, not validated risk estimates. This tool cannot assess a model, determine legal obligations, or authorize autonomous deployment. See how the ratings and weights work. Related guides: check AI answers and evaluate models fairly.
INTERACTIVE AUDIT MATRIX
Workflow Delegation Audit Matrix
Set the task assumptions, then inspect the example allocation and review requirements.
01 / SET THE CONTEXT
Task profile
CURRENT TASK PROFILE
Example: sort incoming messages into an internal queue with an easy correction path. Sending replies, handling sensitive records, or changing accounts requires a separate assessment.
Both controls use an illustrative 1–5 scale. A higher rating changes the planning result; it does not measure the chance of failure or the percentage of a task that can be automated.
Inspect the formula and limitsCalculated in your browser. No task data is sent or saved.
02 / INSPECT THE RESULT
Delegation planning score
A weighted example score, not a probability of failure.
Lower planning band
Commodity layer · bounded pilot
Consider AI assistance for a clearly defined, reversible task. A low example score does not establish that autonomous execution is safe.
At liability 4 or 5, require a qualified human to approve consequential actions in this planning framework, regardless of the average score. This is a tool rule, not a determination of legal requirements.
Conceptual shares from the formula, not measured staffing, time, or recommended resource allocations. A high-consequence gate still applies even when the AI share is larger.
03 / PLAN THE CHECKS
Operational review plan
- Test on representative examples and edge cases; verify outputs against known answers.
- Limit permissions and data access. Keep validation, monitoring, and a stop or rollback path.
- Assign a human owner, sample completed work, and escalate uncertain or consequential cases.
04 / ASSUMPTIONS & LIMITS
Every number is inspectable.
The calculation
Inputs are integer ratings from 1 to 5. The example score is (complexity × 0.4) + (liability × 0.6), shown to one decimal place.
Human review share is round(((score − 1) / 4) × 90) + 10. AI assistance share is 100 − human share. The gauge therefore runs from 90% AI / 10% human at score 1.0 to 0% AI / 100% human at score 5.0.
Planning bands: score ≤ 2.0, score > 2.0 and ≤ 3.8, and score > 3.8. Liability 4–5 adds an approval gate in every band.
What the model leaves out
The supplied presets, 40/60 weights, boundaries, and gauge are illustrative choices. They have not been calibrated against real outcomes and are not an industry standard.
The model does not measure model accuracy, failure likelihood, privacy sensitivity, reversibility, reviewer competence, or deployment conditions. Two inputs cannot establish safe autonomy, compliance, or the appropriate staffing split.
Retrieval, validation, and human review each have limits. Check performance on the actual workflow before increasing permissions or using consequential output.
Evidence behind the review approach
NIST’s AI Risk Management Framework treats risk as a combination of likelihood and consequences, emphasizes context, and calls for risk management appropriate to potential impacts. It also notes that simplified metrics can miss important distinctions. Those principles inform the review guidance here; NIST does not validate this tool’s formula. Read NIST AI RMF 1.0, sections 1.1–1.2.3 .
Source checked October 5, 2026 · Framework published January 2023