Quick Answer
An AI decision authority matrix clarifies use cases, risk levels, AI roles, human reviewers, decision owners, permitted and prohibited actions, escalation, override authority, final authorization, retention, and review frequency.
Complete it before AI-assisted outputs move work. It is a planning template — not a substitute for legal, policy, or contracting review.
Assign responsibility before AI-assisted action
If authority is undefined, the model’s most confident answer becomes the default decision. The matrix exists to prevent that default. It forces a named human path for review, rejection, escalation, and authorization.
Pair it with the human-in-the-loop governance checklist. The checklist asks whether controls exist; the matrix names who holds them for a specific use case.
Build an authority matrix for your use case
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Disclaimer: This matrix is a planning template and does not replace legal, policy, or audit review. It does not issue a compliance certification.
Illustrative governance example—not an approved policy for any specific organization.
| Use case | Risk level | AI role | Human reviewer | Decision owner | Required evidence | Permitted action | Prohibited action | Escalation condition | Override authority | Final authorization | Record-retention requirement | Review frequency |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Operational briefing package | Medium | Draft synthesis across selected models | Named domain reviewer | Accountable program owner | Source list, model IDs, reviewer notes | Prepare review package | Authorize external action without human approval | Material disagreement or missing provenance | Program owner | Program owner after review | Per organizational retention schedule | Quarterly control review |
How teams typically apply the matrix
Start with one high-accountability use case. Define who may prepare a package, who may review it, who may reject or escalate it, and who may authorize action. Then connect those names to an operational workflow that can retain the corresponding records.
Reuse columns; do not reuse authority blindly. A medium-risk internal briefing and a high-risk external filing should not share the same authorizer by convenience.
What each column is for
Use case and risk level set the intensity of review. AI role should describe what the model is allowed to do (draft, compare, retrieve) rather than implying it decides. Human reviewer examines quality and sources. Decision owner is accountable for the outcome. Required evidence lists what must exist before authorization.
Permitted and prohibited actions prevent silent expansion of scope. Escalation conditions and override authority keep exceptions from becoming unofficial policy. Final authorization is the control that turns a package into an institutional act. Retention and review frequency keep the matrix from rotting after the first workshop.
What the matrix does not do
A completed table is not runtime enforcement. If the workflow still allows an operator to paste a model answer into a customer or agency package without the named authorizer, the matrix is documentation of intent only.
Operationalize it through human authorization workflows and retain the result using Decision Ledger record design. The human override study design describes how review and override events should be measured when ledger data are available. Latency and cycle-time measurement design is covered in governed AI workflow performance research. SmartSolo can record reviewer and authorizer actions in a governed execution path; it cannot assign your organizational authorities for you.
Frequently asked questions
What is an AI decision authority matrix?
It is a planning table that names, for a given use case and risk level, who may prepare, review, reject, escalate, and authorize an AI-assisted outcome — and what must be retained.
How is this different from a RACI chart?
A RACI chart assigns general responsibility. An authority matrix is decision-specific: it includes permitted and prohibited actions, escalation conditions, override authority, and record-retention requirements.
Who should be the final authorizer?
The person accountable for the operational outcome, with enough authority to accept the residual risk. That is often a process or program owner, not the model operator.
Should every AI use case have a matrix?
Every consequential or externally visible use case should. Low-risk internal drafting may use a lighter pattern, but the organization should still know who may publish or act.
References
Authoritative sources cited for nearby factual claims. Links open official publisher pages.
Apply these ideas in an operational workflow
Educational resources explain governance concepts. SmartSolo helps teams operationalize review, authorization, and decision records.
See governed AI execution in a live workflow
Review how SmartSolo coordinates multiple AI models, routes human authorization, and preserves the decision record.