Direct answer
Review source, rights, claims, likeness, provenance, accessibility, disclosure, and market fit before release.
1. Inputs
Apply this stage to AI for CMOs by naming the executive owner, affected workflow, current evidence, unresolved questions, and the artifact that must exist before the review advances.
Decision test: Audience and market insight
AI can synthesize approved research, feedback, search, social, and CRM evidence into themes and questions. The CMO still needs to know the population, provenance, representation, and difference between observed behavior and generated interpretation.
- Which people and channels are represented?
- Can each insight be traced to evidence?
- What sensitive inferences are prohibited?
Failure modes to test: stereotyping; sampling bias; invented consumer needs.
2. Output review
Apply this stage to AI for CMOs by naming the executive owner, affected workflow, current evidence, unresolved questions, and the artifact that must exist before the review advances.
Decision test: Creative development and production
Generative tools can expand concepts and produce variants, but brand teams must preserve rights, provenance, approvals, accessibility, and the distinction between an exploration and a publishable asset.
- What training, input, and output rights apply?
- Which review gates cover claims and brand expression?
- How is AI involvement disclosed or recorded?
Failure modes to test: rights disputes; brand drift; unreviewed factual claims.
3. Claims and rights
Apply this stage to AI for CMOs by naming the executive owner, affected workflow, current evidence, unresolved questions, and the artifact that must exist before the review advances.
Decision test: Content supply-chain operations
AI can brief, draft, adapt, tag, route, and localize content within a governed workflow. The useful architecture connects source claims, approved language, asset rights, market rules, version history, and distribution status.
- Which repository owns approved content?
- How are market and channel variations controlled?
- Can a team identify every live derivative when a claim changes?
Failure modes to test: orphaned variants; outdated claims; loss of review evidence.
4. Disclosure
Apply this stage to AI for CMOs by naming the executive owner, affected workflow, current evidence, unresolved questions, and the artifact that must exist before the review advances.
Decision test: Media planning and activation
AI can support audience, budget, bid, and placement decisions inside advertising platforms. CMOs should ask which objective is optimized, what data enters the model, what controls remain available, and how incrementality will be evaluated.
- What is the optimization target?
- Which placements and audiences can be excluded?
- How will causal lift be separated from platform attribution?
Failure modes to test: opaque allocation; brand-unsafe placements; self-reported performance bias.
5. Distribution record
Apply this stage to AI for CMOs by naming the executive owner, affected workflow, current evidence, unresolved questions, and the artifact that must exist before the review advances.
Decision test: Customer journeys and personalization
AI can select or generate a next message when consent, identity, eligibility, channel rules, and frequency are enforced outside the model. The experience should include an explanation and fallback for high-impact or sensitive contexts.
- What customer data and lawful basis support the decision?
- Which offers or messages are prohibited?
- How can a customer opt out or correct information?
Failure modes to test: discriminatory targeting; preference violations; creepy or harmful inference.
Evidence packet to retain
Apply this guide as a record of judgment, not as a disposable checklist. Keep the scope, current baseline, representative scenario, participating people, source materials, decision rights, observed exceptions, outcome measures, unresolved claims, and the date on which the conclusion must be reviewed again.
- Audience and market insight: AI can synthesize approved research, feedback, search, social, and CRM evidence into themes and questions. The CMO still needs to know the population, provenance, representation, and difference between observed behavior and generated interpretation.
- Creative development and production: Generative tools can expand concepts and produce variants, but brand teams must preserve rights, provenance, approvals, accessibility, and the distinction between an exploration and a publishable asset.
- Content supply-chain operations: AI can brief, draft, adapt, tag, route, and localize content within a governed workflow. The useful architecture connects source claims, approved language, asset rights, market rules, version history, and distribution status.
- Media planning and activation: AI can support audience, budget, bid, and placement decisions inside advertising platforms. CMOs should ask which objective is optimized, what data enters the model, what controls remain available, and how incrementality will be evaluated.
The final packet should distinguish what an official source establishes, what was observed during evaluation, what a provider or participant reported, what the reviewing team inferred, and what remains unknown. That separation is essential when the result will influence an executive, employee, customer, investor, or regulated decision.
Evaluation worksheet
| Question | Required record | Approval condition |
|---|---|---|
| What changes? | Current and proposed workflow | Boundary and owner are explicit |
| What supports the output? | Source, rights, lineage, quality, and version | Material inputs are traceable |
| Who decides? | Review, approval, exception, and escalation rights | A real person has time and authority |
| What would prove value? | Baseline, population, period, measure, and exclusions | Activity is not substituted for outcome |
| When do we stop? | Thresholds, incidents, change triggers, and fallback | Exit is practical and controlled |
Final approval gate
Approve only when the role-specific decision is clear, the evidence supports the conclusion at the claimed level, material unknowns remain visible, ownership conflicts are disclosed, and the implementation can be monitored and reversed. Reject a universal winner conclusion when the evidence supports only conditional fit.
The publication supports research and executive decision preparation. It does not provide legal, financial, accounting, employment, clinical, cybersecurity, investment, procurement, or implementation advice.