Which Client Videos Are Suitable for AI Generation?

Which types of marketing videos work best with AI generation?

AI works best when the video carries clear information rather than proof of a person’s lived experience. An explainer with a defined script, or a product demonstration built around verifiable features, gives the agency material it can check against a brief.

That does not make generated footage ready to publish. A human editor still needs to check whether the images match the words, whether the product is shown accurately, and whether the result feels consistent with the brand. A polished error remains an error.

There is a practical reason to keep AI in an assisting role. Storyblocks’ June 2024 survey of 731 members—creators and in-house editors, not a representative general-population sample—found that respondents most often used generative AI for brainstorming (63%) and for streamlining cuts, transitions, and polish (61%). Forty-two percent said they trusted AI to draft full content. The report also identified reliability, accuracy, inconsistent output, and content perceived as fake or generic as ongoing concerns.

Use those findings as a workflow signal, not a promise about every agency or audience. Let AI help shape or assemble material; keep people responsible for the claim, the cut, and the final call.

Can an agency use AI video for product demos and explainers?

Yes, an agency can use AI video for product demonstrations and explainers when the script and visuals remain accurate, clear, and reviewable. The format is a good fit when the value comes from explaining a feature or process, not from implying a real customer personally endorses it.

Build review into the production plan. First, confirm the claims and product details against client-approved material. Next, inspect each generated shot for mismatches: a feature that does not exist, a confusing sequence, or a visual that suggests a result the product cannot deliver. Then review pace, captions, narration, and brand tone as one finished piece.

Video can be useful for complex information. In a January 2024 survey of 2,008 consumers in the United States and United Kingdom, Idomoo and Atomik Research reported that 54% preferred video over written text for communicating complex information such as details of a new program or service. The report stated a margin of error of ±2 percentage points at 95% confidence. That finding supports testing video as a format; it does not verify the accuracy of any particular generated video.

Set approval gates with the client before production: what AI will generate, which claims require substantiation, who reviews the cut, and what changes trigger another review. When a detail cannot be verified, remove it or ask the client for evidence. Do not let a plausible-looking image stand in for proof.

When should a brand use real actors or customer testimonials instead?

Use real participants when the message depends on a real person’s identity, opinion, belief, or experience. That includes customer testimonials and personal stories presented as genuine accounts. If the viewer is meant to believe that a person actually used a product or holds a stated view, a synthetic stand-in can turn a creative choice into a misleading claim.

The Federal Trade Commission’s Consumer Reviews and Testimonials Rule took effect October 21, 2024. The FTC says advertising agencies and public-relations firms can be liable for creating or disseminating fake or false reviews and testimonials. Its guidance defines a testimonial as an advertising message consumers are likely to believe reflects a real consumer’s opinions, beliefs, or experience; a business that puts a false testimonial on its website may also be liable.

The FTC’s announcement on the rule expressly identifies AI-generated fake reviews as covered when they falsely claim to come from a real person or someone with actual product experience. Agencies should not generate a fictional customer and present that person as a genuine user. Use a real, authorized participant for a testimonial, or label and frame synthetic content so it does not claim real experience.

Disclosure deserves a planned decision, not a last-minute label. Idomoo and Atomik Research found that 70% of surveyed consumers wanted to know if a brand video was created with AI tools, while 65% said they were interested in receiving AI videos from brands. Interest and disclosure expectations can coexist. The agency should explain the production choice and agree with the client how viewers will be told.

Research on deepfake advertising points to a further trade-off. A 2025 study in the Journal of Promotion Management examined disclosure presence, timing, and language across three experiments. It reported that high-quality deepfake advertisements produced outcomes such as purchase intention comparable to their original counterparts, while disclosure presence and timing influenced consumer appraisal and emotional responses. Disclosure is part of how an ad is received, not an afterthought.

A client review workflow before delivery

Use a short, repeatable review rather than relying on a general impression. Keep a record of the decision and the client’s approval.

1. Agree on the use. Identify what AI will generate and whether the video depicts a real person, product feature, or customer experience.
2. Check the facts. Match each spoken or written claim and each product depiction to client-approved evidence.
3. Check the craft. Review pacing, edits, sound, captions, visual continuity, and whether the finished piece fits the brief and brand.
4. Check the impression. Ask what a viewer would reasonably believe about the people and experiences shown. Replace material that implies a real endorsement or experience that did not occur.
5. Agree on disclosure. Decide how and where to tell viewers about AI use, taking account of what the content depicts and how the audience could interpret it.
6. Get final approval. Share the finished version with the client and confirm approval of claims, presentation, and disclosure before publication.

This sequence separates different kinds of risk. A factual check cannot settle whether a synthetic face feels misleading; a disclosure label cannot fix an inaccurate product demonstration. Each needs its own review.