Use AI to Draft Marketing Claims Without Losing the Evidence

Generative AI can make a marketing claim sound finished before anyone has checked whether the evidence supports it. The danger is not only a false sentence. A model can quietly widen the population, remove a condition, turn a test result into a universal promise or imply a comparison that the source never made. A safer workflow keeps every objective claim attached to its evidence, scope and approval decision from the first draft.

Current UK advertising guidance makes that discipline concrete. The CAP Code says marketers should hold documentary evidence before publishing objective claims, present material qualifications clearly and avoid exaggerating a product’s performance. The CMA’s Green Claims Code adds that environmental claims should be accurate, clear, backed by current credible evidence and honest about relevant conditions and the wider story. The practical lesson applies beyond the UK: copy approval should inspect claims one by one, not judge only whether a paragraph sounds persuasive.

Build a five-field claim sheet before you prompt

Give the model only the rows marked Supported or Revise, plus the required audience, tone and format. Ask it to preserve each qualification and to flag any sentence it cannot trace to a row. The model may help shape language; it should not invent a bridge between weak evidence and a stronger promise.

Worked example: a test result becomes an overclaim

Suppose a product brief contains a controlled test showing that one bottle model kept water below a stated temperature for 12 hours under specified conditions. A draft generator writes: “Keeps every drink ice-cold all day.” The sentence expands the evidence in four ways: every drink, ice-cold, all day and no test conditions. A fluent rewrite has become a different claim.

The revision may be less dramatic, but it is reviewable. If the exact temperature or conditions would make the sentence cumbersome, the sheet gives the team a clear choice: shorten the claim without changing its meaning, add an accessible qualification or remove the claim. It does not invite the model to hide uncertainty in smoother prose.

Run claim-level review, not paragraph-level approval

This method fits a broader discipline: keep generated text in a draft state until it has been checked and approved. Bokili Learn’s guide to separating drafts from records explains that lifecycle: https://bokili.com/en/learn/ai-training-employees-drafts-records. For a compact verification habit, see https://bokili.com/en/learn/how-to-verify-ai-output-at-work. To catch conclusions that change when one assumption flips, use https://bokili.com/en/learn/ai-conclusion-reversal-test.

Make traceability part of the copy

AI is useful when it reduces the work of exploring language, not when it dissolves the link between a promise and its proof. A small claim sheet gives writers room to iterate while giving reviewers something precise to inspect. The durable rule is simple: if a claim cannot point to evidence, scope and an owner, it is not ready for publication.