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What should an AI marketing governance platform do?
It should help your organization identify where AI is used, decide what oversight each use requires, document approvals and evidence, and revisit decisions when the system or its context changes. For marketing, that means governing not only models and vendors but also the content, claims, audiences, data, and consumer-facing decisions around each use.
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Evaluate the platform against an end-to-end workflow: inventory a use case, assess and classify its risks, assign controls and reviewers, retain supporting evidence, approve the marketing output and any disclosure, and trigger reassessment when material details change. Ask to export the complete record so you can verify that governance history remains usable outside the product.
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Which capabilities matter most?
Inventory and discovery
Check whether teams can record each AI use case, model, tool, vendor, business owner, purpose, data category, affected audience, and lifecycle status. Ask how the platform detects changes or unregistered use: a system that depends entirely on manual submissions may leave gaps. Gamut and Wave2 describe inventory-related capabilities in their vendor materials, but those descriptions are not independent validation (Gamut AI documentation; Wave2).
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Risk assessment and review routing
A useful assessment captures the use context, intended purpose, potential harms, likelihood or severity judgments, mitigations, accountable reviewers, and conditions for approval. Confirm that teams can route higher-risk uses to the right people and record why a decision was made. NIST’s AI Risk Management Framework (AI RMF) is voluntary and use-case agnostic; it provides a risk-management resource, not a product certification (NIST AI Risk Management Framework; NIST AI RMF FAQs).
Control and framework mapping
Do not stop at a vendor’s list of supported frameworks. For each mapped control, ask to see its source and version, applicability rationale, linked evidence, accountable owner, and unresolved gaps. NIST says AI RMF 1.0 is under revision, so version visibility and a clear update process matter. A mapping can structure your work; it does not establish that your organization complies with a law or has implemented a control effectively (NIST AI RMF 1.0 publication record).
Evidence and audit history
Ask whether a record preserves source artifacts, approvals, timestamps, access history, changes, exceptions, and the rationale behind decisions. Test whether you can export those materials in a usable format, with enough context to understand them without relying on the platform. Gamut and Wave2 describe evidence-related functions; verify their depth directly in a demonstration (Gamut AI documentation; Wave2).
Marketing claims and generated content
Test the process for reviewing AI-assisted ad copy, images, audio, video, and synthetic personas—not just model risk in the abstract. Reviewers should be able to connect a product-performance, comparative, or endorsement claim to its substantiation, record who approved it, and preserve the version that appeared to consumers. The FTC says advertising claims must be truthful, non-deceptive, fair, and evidence-based, and its advertising guidance applies to software and services as well as other offerings (FTC Advertising and Marketing).
Consumer-facing AI transparency
Ask the platform to capture why your team decided to disclose—or not disclose—AI involvement, which risk or materiality factors informed that decision, and what wording and placement were approved. The IAB’s AI Transparency & Disclosure Framework V2, published August 18, 2026, describes a risk-based, materiality-driven approach for consumer-facing advertising and marketing. It is industry guidance, not a substitute for applicable law; check that the platform identifies the framework version it uses (IAB AI Transparency & Disclosure Framework V2).
Privacy, copyright, and data handling
Trace what data enters each workflow, who can access it, how long it is retained, how prompts are handled, what provider terms apply, and what rights or restrictions govern generated outputs. Include controls for sensitive or customer data, especially in segmentation and personalization. NIST’s Generative AI Profile recommends aligning generative AI development and use with applicable laws, including those concerning data privacy, copyright, and intellectual property; the duties that apply depend on jurisdiction, organizational role, and use case (NIST Generative AI Profile).
Ownership, exceptions, and change management
Look for named owners, role-based permissions, human-review requirements, exception handling, incident escalation, version history, and oversight reporting. Ask which changes trigger reassessment—for example, a changed model, purpose, data source, audience, or marketing claim—and how the system assigns that work. NIST’s framework addresses risk across AI design, development, use, and evaluation, with trustworthiness characteristics that include reliability, safety, security, accountability, transparency, explainability, privacy, and management of harmful bias (NIST AI RMF FAQs).
Integrations and procurement fit
Verify APIs and integrations, identity and access management, data export, retention and deletion, hosting region, subprocessors, support, implementation effort, and contract terms. These are organization-specific procurement checks; available information does not establish comparative vendor performance or contract details. Request current written proposals and verify references directly rather than assuming that a public feature page settles cost or fit.
How can you compare platforms in a demonstration?
Use the same two or three representative marketing workflows with every shortlisted vendor. For each, require a walkthrough from initial registration to final evidence export, and score demonstrated behavior rather than promised capabilities.
- AI-assisted ad copy with a measurable product claim: show how the use is registered, who owns it, how risk is assessed, where claim substantiation is attached, who approves the copy, and how the published version and decision history are retained.
- Generated campaign imagery or a synthetic spokesperson: show how reviewers assess the consumer-facing content, document the disclosure decision and approved placement, and preserve the relevant source and approval records.
- AI-supported segmentation or personalization using customer data: show how data categories, access, provider terms, privacy review, mitigations, and reassessment triggers are recorded.
For every workflow, ask what happens when the model, data, purpose, audience, or output changes. Then export the full record and note any missing controls as gaps; a framework logo is not a substitute for seeing the workflow operate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you score vendors?
Set weights that reflect your organization’s risks and operating model, then apply them consistently to each demonstration. A practical scorecard can include:
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- Inventory coverage and detection of changes or unregistered use.
- Risk assessment depth, reviewer routing, and decision rationale.
- Control mapping with source, version, applicability, and gap tracking.
- Evidence quality, audit history, and complete export.
- Marketing claim substantiation and content-review workflow.
- Disclosure decision records for consumer-facing marketing.
- Privacy and data-handling controls.
- Integrations, access controls, change management, and procurement fit.
Record whether each capability was demonstrated, merely described, or not shown. Vendor descriptions from Gamut, Wave2, and Saidot can help identify products to investigate, but they are not comparative evaluations or independent proof of effectiveness (Gamut AI documentation; Wave2; Saidot). Current pricing, implementation costs, contract terms, and independent customer outcomes are not established by these descriptions.
How should you use frameworks and guidance?
Use frameworks to organize questions and evidence, not as a pass/fail shortcut. NIST describes its AI RMF as voluntary; its use-case-agnostic structure needs tailoring to your marketing context, jurisdictions, risk tolerance, and operating model. Its trustworthiness characteristics are lenses for assessment, not a checklist that automatically makes a system trustworthy (NIST AI Risk Management Framework; NIST AI RMF FAQs).
For advertising, use FTC guidance to shape claim review and substantiation practices. For consumer-facing AI transparency, the IAB framework can inform disclosure decisions, while remaining distinct from legal requirements. For generative AI data and output risks, consult the NIST Generative AI Profile and determine which privacy, copyright, and intellectual-property obligations apply to your circumstances (FTC Advertising and Marketing; IAB AI Transparency & Disclosure Framework V2; NIST Generative AI Profile).
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