Can You Prove Your AI Claims? Aligning Marketing, Sales and Product

“Our AI understands your business.”

It is an appealing line. It also leaves a buyer with several questions. What information does the product use? What can it do with that information? Where does its performance depend on a person reviewing the result?

For a B2B company, I would review AI claims as part of the work that connects product capability, evidence and customer communication.

This is where my background in positioning and sales and marketing alignment is especially relevant. A statement should help buyers understand the offering and give the internal team a defensible way to explain it.

Begin with the claims already in circulation

Review the homepage, product pages, sales decks, demo scripts, proposals, case studies and answers to customer questionnaires.

Include phrases that sound like supporting language: “fully autonomous,” “unbiased,” “enterprise-ready” and “your data is never used for training.” Each can communicate a specific expectation even when the writer intended it as a general reassurance.

Record the exact wording and where it appears. Otherwise, teams may debate a softened version of a statement while the stronger version remains live.

Match each statement to a precise question

For each claim, I would ask:

• What capability or outcome are we actually promising?
• Which product version, deployment and use case does it cover?
• What conditions must hold for the statement to be true?
• Which evidence supports it, and who has reviewed that evidence?
• What limitations should accompany it?
• Who must review the wording when the product or evidence changes?

This creates a claim register that Marketing, Product and Sales can maintain together. It should link to controlled source records rather than duplicate confidential documents everywhere.

Distinguish a demonstration from a measured result

Illustrative scenario: A sales demonstration shows an assistant drafting a strong answer to a procurement questionnaire. The proposed headline becomes “Complete security questionnaires in minutes.”

Before using that language, the team should examine what “complete” means. Does the assistant draft answers for human review? Does it identify unanswered questions? Can it substantiate statements about the customer's specific deployment?

A successful demonstration supports a description of what happened in that demonstration. A broader time-saving claim needs evidence suited to the broader claim.

NIST's Generative AI Profile recommends evaluating capability claims through empirically validated methods. That is a useful source principle for separating an attractive example from supported performance language. Source: NIST AI 600-1, action MS-2.3-002.

Use narrower language when it is more accurate

Here are illustrative revisions, to use only when the described capabilities are verified.

Broad: “Automate every security questionnaire.”

More specific: “Draft questionnaire responses from your approved answer library, with unanswered items flagged for your team to review.”

Broad: “Your data is never used for training.”

More specific: “For this contracted deployment, the provider's terms state that submitted customer data is not used to train its models.” Add any relevant exceptions, scope or dependencies, and have the responsible specialists approve the statement.

Broad: “Eliminate bias from recruiting.”

More specific: Describe the particular evaluation, conditions, limitations and available human review. If that evidence is missing, revise or remove the claim rather than inventing a replacement assurance.

The aim is to preserve a useful benefit while explaining what the buyer is receiving.

Connect claims to release and change processes

Suppose a feature gains access to another data source, changes model providers or adds an automated action. The approved wording may no longer describe the current product.

I would add a communication check to material release decisions. Identify the affected pages, decks, proposal templates and training materials. Name the person responsible for updating them.

Give Sales a current set of approved statements and a route for questions that go beyond those statements. A shared document is helpful only when people know which version to use.

Make uncertainty explicit in customer answers

A procurement request may ask about something your team has not verified. Record the question, assign it to the right owner and explain when the answer will be available.

There is a useful distinction between a capability you can demonstrate, a control you can document and a future improvement you are planning. Keep those categories separate in the response.

A roadmap item should not quietly become a present-tense assurance.

For more on the broader positioning question, see what proof enterprise buyers need from AI security vendors.

Work through one promise with the free Responsible AI Claims & Buyer Trust Check. For a review of your agreed website pages and sales deck, the AI Claims & Messaging Review provides a claim-to-evidence register, recommended wording and a buyer FAQ.

Related reading

• What Enterprise Buyers May Ask About Your AI Governance
• What an AI Governance Readiness Assessment Should Cover