A risky AI claim is not always an obviously dramatic sentence. It can be a comparison without a defined baseline, a screenshot that suggests full automation or a data assurance copied from another deployment.
A focused audit should identify the expectation created by the material and check whether the company can support it.
I would begin with one AI feature, up to three important website pages and the standard sales deck. That keeps the first review concrete enough to finish and act on.
Capture the material before editing
Record the URLs, deck version, review date and exact statements. Keep screenshots where visual context matters.
Look at headlines, supporting paragraphs, diagrams, charts, footnotes and demo captions. A page can create an impression that no individual sentence fully states.
Include deck speaker notes if they carry material promises. A qualified slide can still be paired with an unrestricted spoken claim.
Review five kinds of promise
Capability: What task or action does the material say the AI performs?
Performance: What improvement, accuracy or time saving does it promise?
Autonomy: What does it imply about human review or intervention?
Data handling: What does it say about access, retention, training or protection?
Availability: Does it distinguish live, configured, beta and planned functionality?
This is my suggested review structure. It organizes communication questions; it does not establish technical compliance or product safety.
Test the evidence against the wording
For each material claim, identify the factual owner and the supporting record. Check the product version, intended use, deployment conditions, date and limits.
A result from a single task should not silently support a broader workflow claim. An old evaluation may not describe a changed release. A provider’s commitment may need confirmation for the deployment being sold.
When evidence cannot be located, record the uncertainty. Missing documentation is a reason to verify the statement, not a reason to invent a supporting explanation.
Look for promises made through layout
Illustrative audit finding: A slide places “no manual work” above a demo that shows an analyst approving every recommendation. The footnote says “subject to human review.”
The issue is the mismatch in the overall impression. A proposed revision would describe the automated preparation step and show the approval step as part of the workflow.
Another finding might be a percentage in large type with an undefined comparison. The revision needs a defined measure and scope, or the number should be removed until supported.
Prioritize by the buyer decision affected
I would address claims first when they could change a buyer’s understanding of who acts, which data is used, what outcome is demonstrated or what functionality is available.
Lower-priority editing can follow. A tone inconsistency matters, but a material capability mismatch is a more urgent decision.
Record a specific recommendation: keep within the verified scope, clarify, replace, remove or hold pending confirmation. Attach an owner and a target date.
Produce a working revision list
A useful finding contains the original wording, location, concern, evidence reviewed, proposed wording, unresolved fact and required approver.
For example: “Slide 8 implies unreviewed execution. Product confirms approval is required in this deployment. Replace headline and demo annotation; Sales Enablement owns rollout after Product verifies the revision.”
That entry tells someone what to change and how to close it.
Check the saved materials
After approved revisions are implemented, inspect the live page and final deck. Check that qualifications remain close to the claim, diagrams reflect the workflow and older versions are retired.
Send Sales a short explanation of what changed and the answer to use when a buyer asks about it. A revised file alone may leave the spoken story unchanged.
The first audit is complete when the important findings have a publishing decision, the changes are visible and the team knows who maintains the answers.
Put this into practice
The free guide gives you a quick check for one feature’s claims. My AI Claims & Messaging Review covers up to three agreed website pages and one sales deck, with findings, wording recommendations and owners for next steps.
Download the free Responsible AI Claims & Buyer Trust Check · Explore the AI Claims & Messaging Review
Related reading
- How Marketing Teams Should Talk About AI Without Overpromising
- How to Build an AI Claims Framework for Your Sales Team
Can You Prove Your AI Claims? Aligning Marketing, Sales and Product
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