Multimodal AI is a model that can take in and reason over more than one type of input, such as text, images, audio and video, in the same request. In campaign work, it is used to audit ad creatives at scale, analyse competitor videos, turn screenshots and PDFs into structured data, check landing pages visually and draft reports that combine charts with commentary.
Models such as Google’s Gemini, OpenAI’s GPT models and Anthropic’s Claude all accept images alongside text, and some handle audio and video directly. The practical change for marketers is that tasks which used to need a person looking at every file can now be handled in a first pass by software, with people reviewing the results.
What makes multimodal different from a text chatbot?
A text-only model only knows what you describe to it. A multimodal model can look at the actual banner, listen to the radio spot or watch the first fifteen seconds of a video ad. That removes a translation step where details often get lost, such as whether the logo is visible in the first frame or whether the call to action is readable on a phone screen.
It does not make the model a reliable judge of quality. It can describe and check against criteria well, but taste, brand fit and strategy remain human calls. The value lies in consistent, tireless checking against rules you define.
Which campaign tasks benefit most?
- Creative audits: checking hundreds of ad images for logo placement, text overlay amount, legibility and brand colours.
- Video ad review: noting when the brand first appears, whether captions are present and what happens in the opening seconds.
- Competitor research: summarising messaging, offers and visual style from screenshots of ads in Meta Ad Library or Google Ads Transparency Center.
- Document extraction: pulling figures from PDF media plans, invoices or platform exports that arrive as images.
- Landing page review: comparing a screenshot to the ad it follows to spot message mismatch.
- Accessibility checks: drafting alt text and flagging low-contrast text in images.
Each task follows the same pattern: a clear checklist, a batch of files, structured output in a spreadsheet and a human who reviews the flagged items rather than every item.
How Alien Road applies multimodal AI in client campaigns
In a typical engagement, discovery starts by finding where the team spends time looking at files. For an ecommerce client running Meta and Google ads, that might be weekly creative reviews, monthly competitor scans and quarterly landing page checks. Each becomes a candidate for a multimodal workflow.
Setup
The team writes a scoring checklist for each task with the client, based on platform guidance and brand rules. For example, a video checklist might ask whether the brand appears in the first three seconds, whether the video works with sound off and whether the offer is stated on screen. The checklist becomes the model prompt, and output is forced into fixed columns so results can be sorted and compared.
Files are processed through the model’s API rather than pasted by hand, which keeps a record of inputs and outputs. Client data stays in accounts with training disabled under the provider’s business terms.
Measurement and reporting
Before trusting the workflow, a sample is scored by both a person and the model, and the two are compared. Where they disagree often, the checklist wording is revised. Once agreement is acceptable, the model handles the first pass and people review only flagged items. Monthly reports then combine performance data with creative attributes, showing for instance whether ads with captions performed differently from those without in that account.
Where does multimodal AI make mistakes?
Models can misread small text, miscount objects, invent details in blurry images and describe a video from only some of its frames. Long videos may be sampled rather than watched frame by frame, so short events can be missed. Charts are a known weak spot: a model may read a bar’s value incorrectly while sounding certain.
- Never use extracted figures in a report without checking them against the source export.
- Ask for page or timestamp references so a reviewer can verify quickly.
- Keep checklists specific; vague questions such as is this ad good produce vague answers.
- Test with deliberately flawed examples to see whether the model catches them.
What should you watch for on privacy and rights?
Images and videos often contain people. Uploading customer photos, user-generated content or call recordings to a model is processing personal data, so it needs a legal basis under GDPR or KVKK and a provider agreement that covers it. Use business accounts, check retention settings and remove material you do not need before uploading.
Competitor creatives are fine to analyse for research, but reusing them in your own output is not. Keep analysis notes separate from creative production.
How much does a multimodal workflow cost to run?
Image and video inputs are billed as tokens, and they add up faster than text. A single high-resolution image can count as hundreds or more than a thousand tokens depending on the provider and settings, and video is usually processed as sampled frames plus audio. For a creative audit of a few hundred images a month, costs are typically modest, but long videos processed weekly deserve a quick estimate first.
Reduce cost by resizing images to the resolution the checklist actually needs, trimming videos to the section being reviewed and using batch processing where the provider discounts it. Store results so unchanged creatives are not analysed twice. Set a monthly spending alert in the provider dashboard before the workflow runs unattended.
Where to start this week
Choose one review task your team repeats and write it down as a checklist of five to eight yes-or-no questions. Run twenty past creatives through a multimodal model with that checklist and compare the answers with your own judgement.
- Pick the task: creative audit, video review or competitor scan.
- Write the checklist in plain language.
- Use a business account with training off.
- Score twenty items manually and by model, then compare.
- Adjust the checklist and repeat until disagreements are rare and explainable.
If the model agrees with you on most items and the disagreements teach you something, you have a workflow worth scaling. If not, the checklist usually needs sharper questions before the model needs replacing.
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