How do you check AI marketing claims?

Start with the exact public wording, then identify the claim type. A vague AI-powered label needs a workflow explanation. An accuracy number needs benchmark and error-rate details. A compliance reference needs scope and audit context. An ROI claim needs baseline and measurement method.

What belongs in an AI marketing claims checklist?

Include the phrase, claim type, evidence needed, buyer question, source URL, and checked date. The checklist should show what evidence supports the wording, not just whether the wording sounds confident or high risk.

Can marketers use this before publishing AI product copy?

Yes, as a self-check for public wording. The page still uses a buyer-side evidence standard: if a buyer would reasonably ask for benchmark, audit, outcome, or workflow evidence, the marketing copy should not imply broader support than the team can show.

AI marketing claims checklist: high-burden wording to check in public copy

Last reviewed June 5, 2026

Use this page when you need to spot high-burden phrases in public AI marketing copy—superlatives, vague AI-powered labels, outcome guarantees, and compliance references—and map each wording pattern to the evidence a buyer should request. It does not replace a full page walkthrough or a pre-purchase due diligence review.

Check a high-burden AI marketing phrase How the evidence method works

Fastest path: copy one exact vendor sentence that matches this pattern, then open the checker. Add the public URL only if you want readable page context recorded alongside the wording. The result is an evidence-burden note you can reuse in vendor follow-up or internal review, not a verdict. Not sure what a result looks like? See a sample receipt.

What to verify before you rely on the claim

Benchmark conditions that match your actual use case for any accuracy or performance claim, not only a controlled demo.
The name and description of the AI technique or model for any 'AI-powered' or 'AI native' claim.
Reference customer conditions and measurement method for any cost-saving, ROI, or outcome claim.

Sources behind High-burden AI marketing wording

ASA/CAP: AI as a Marketing Term Report ASA/CAP guidance
· 2024
UK advertising regulator report on how AI is used as a marketing term. Documents evidence expectations for accuracy, automation, compliance, and vague AI-powered claims in advertising.
FTC Crackdown on Deceptive AI Claims and Schemes FTC enforcement
· September 2024
FTC announcement of enforcement actions against companies using AI claims to deceive buyers. Documents the types of marketing wording that triggered FTC scrutiny.
FTC v. Workado LLC — AI Content Detection Accuracy Claims FTC enforcement
· April 2025
FTC order requiring a company to back AI detection accuracy claims with evidence. Numeric accuracy figures in marketing require benchmark scope, dataset description, and false positive rates.

Documented High-burden AI marketing wording examples

"the most accurate AI writing detector on the market"

Accuracy / Performance

Source and date: FTC v. Workado LLC · April 2025
Evidence signal: Superlative accuracy claim without comparison set, benchmark scope, or false positive rate.
Evidence gap: A buyer needs the benchmark design, which products were included in the market comparison, the false positive and false negative rates, and whether the test dataset matches the buyer's content type.
Buyer question: Which products and test conditions were included in the market comparison that supports this 'most accurate' claim?

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"AI-powered platform that transforms your business"

Vague AI-powered

Source and date: ASA/CAP AI as a Marketing Term Report · 2024
Evidence signal: Vague AI-powered language without description of the specific model, task, or data used.
Evidence gap: A buyer needs an explanation of what the AI component does, what model or technique is used, and what 'transforms' means in measurable terms.
Buyer question: What specific AI technique or model is used in this platform, and what does 'transforms your business' mean in measurable outcomes?

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"guaranteed ROI within 90 days"

ROI / Outcome

Source and date: FTC Crackdown on Deceptive AI Claims · September 2024
Evidence signal: Unconditional outcome guarantee without documented conditions, measurement method, or refund terms.
Evidence gap: A buyer needs the measurement method, what the guarantee covers and excludes, the conditions under which it applies, and what recourse is available if the outcome is not achieved.
Buyer question: What is the exact measurement method for this ROI guarantee, and what conditions must be met for the guarantee to apply?

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Evidence map for High-burden AI marketing wording

Claim pattern	Evidence needed	Buyer question
Most accurate, best, highest-performing, or market-leading AI	Benchmark design, products or baselines included in the comparison, test date, dataset description, false positive and false negative rates at the stated threshold.	What was compared and under what conditions, and how would those conditions hold in our environment?
AI-powered, AI native, powered by AI, or built with AI	Description of the specific model or technique, training data source, what the AI component does in the product, how it differs from rules-based logic, and the update process.	What exactly does the AI component do in this product, and what would change if it were replaced with rules-based logic?
Saves X hours, reduces cost by Y%, or guarantees Z outcome	Deployment conditions of the reference case, baseline process, measurement period, company type, and whether the conditions match the buyer's environment.	What was the baseline process and company environment in the case that produced this figure?
GDPR-ready, HIPAA-compliant, SOC 2 report-backed, or audit-ready	Certification body name, audit period, scope boundaries, whether the buyer's configuration is covered, and the customer's own remaining compliance obligations.	Who conducted the audit, what was the exact scope, and does our configuration and data environment fall within it?
Fully automated, eliminates the need for staff, or replaces a professional role	Explicit list of tasks the AI does and does not handle, failure-handling process, escalation path for errors, and where human review is still required.	Which tasks must still be reviewed by a human, and what is the process when the AI makes an error or produces an uncertain result?
Only AI tool, first AI solution, or unique AI platform that does X	Definition of the category being claimed, what alternatives were considered, how the comparison was made, and the date of the uniqueness claim.	How is the category defined, and what alternatives were excluded from the 'only' or 'first' claim?

Evidence buyers need for High-burden AI marketing wording

Benchmark conditions that match your actual use case for any accuracy or performance claim, not only a controlled demo.
The name and description of the AI technique or model for any 'AI-powered' or 'AI native' claim.
Reference customer conditions and measurement method for any cost-saving, ROI, or outcome claim.
Certification body name, audit scope, and coverage of your configuration for any compliance or certification claim.
A list of tasks the AI handles autonomously and tasks that still require human review for any automation or replacement claim.

Buyer questions for High-burden AI marketing wording

For this accuracy claim, what was the benchmark, and how closely does it match our workflow and data type?
For this 'AI-powered' claim, what is the specific model or technique, and what would the product do without it?
For this outcome or ROI claim, what were the baseline conditions of the reference customer?
For this compliance claim, what is the audit scope, and does our configuration fall within it?
For this automation or replacement claim, which tasks still require human review?
For this 'first', 'only', or 'best' claim, how is the category defined and when was the comparison made?

Safer wording for High-burden AI marketing wording

Reported X% accuracy on a named benchmark under stated conditions; performance varies by content type and deployment environment.
Uses [specific technique] to automate [named task]; human review required for [named decisions] and error handling.
Customers in [industry or size segment] reduced [named metric] by X% in [named conditions]; results vary by baseline process.
Meets [standard] requirements for [named scope] as audited by [named body] in [period]; buyer's environment may require a separate assessment.

High-burden AI marketing wording questions

How do you check AI marketing claims?: Start with the exact public wording, then identify the claim type. A vague AI-powered label needs a workflow explanation. An accuracy number needs benchmark and error-rate details. A compliance reference needs scope and audit context. An ROI claim needs baseline and measurement method.
What belongs in an AI marketing claims checklist?: Include the phrase, claim type, evidence needed, buyer question, source URL, and checked date. The checklist should show what evidence supports the wording, not just whether the wording sounds confident or high risk.
Can marketers use this before publishing AI product copy?: Yes, as a self-check for public wording. The page still uses a buyer-side evidence standard: if a buyer would reasonably ask for benchmark, audit, outcome, or workflow evidence, the marketing copy should not imply broader support than the team can show.

Method and limits

This guide maps high-burden marketing wording patterns to evidence expectations from FTC enforcement actions and ASA/CAP advertising guidance. For a single-page walkthrough, use the vendor claim evidence checklist; for pre-purchase review, use the due diligence checklist. It is not legal advice, a compliance certification, or a verdict on any company's claims.

Cite this page for source-backed evidence gaps and buyer questions, not as a truth finding, legal conclusion, compliance certification, company accusation, or company rating. If you have the exact vendor wording, Check a high-burden AI marketing phrase and paste one sentence first. If a source has changed, or you have supporting evidence or a company response, send a private correction or source note.