Multimodal AI is a model that understands and produces more than one type of input, such as text, images, audio and video, within a single system. For marketers it fits best in tasks that cross formats: reviewing ad creative, describing product photos, turning a webinar into posts, analysing screenshots of dashboards and answering questions about visual content. It is weaker at precise numbers inside images and at long video.
Earlier AI tools were usually single-purpose. One model wrote text, another generated images, a third transcribed audio. Models such as OpenAI’s GPT-4o family, Google’s Gemini, Anthropic’s Claude and Meta’s Llama vision variants now accept images alongside text, and several handle audio and video as well. Gemini in particular was designed to take long video and audio inputs.
That change matters less for what the model can create and more for what it can read. A marketer can now hand a model a landing page screenshot, a ten-minute product demo or a folder of competitor ads and ask questions about them.
What can multimodal AI do for a marketing team today?
The most reliable uses are the ones where the model reads and describes, and a person decides.
Creative review
Upload a set of display or social ads and ask the model to check them against your brand guidelines: logo placement, text density, contrast, required disclaimers. It will not replace a designer’s eye, but it catches missing legal lines and off-palette colours quickly.
Product content
Give the model product photos and a spec sheet, and it can draft descriptions, alt text and attribute tags. For a catalogue with thousands of items, this speeds up work that was previously done image by image.
Repurposing long content
A recorded webinar or podcast can become a summary, a set of quotes with timestamps, social post drafts and an outline for a blog article. Video understanding means the model can also note what was shown on screen, not just what was said.
Analysing screenshots
Screenshots of analytics dashboards, competitor pricing pages or search results can be described and compared. This is useful for quick research, though exact figures should always be verified against the source.
Where does it still fall short?
Multimodal models make specific kinds of mistakes, and knowing them saves time.
- Reading small numbers and dense tables in images is error-prone; the model may misread a digit with full confidence.
- Spatial reasoning is uneven. “Is the button above or below the fold?” sometimes gets a wrong answer.
- Long video may be sampled at intervals, so brief on-screen details can be missed.
- Generated images still struggle with precise text, consistent product details and brand-accurate logos.
- Models may describe what they expect to see rather than what is there, especially in cluttered images.
There is also a rights question. Uploading client photos, voice recordings or unreleased creative to a model means sending them to a provider. Check data retention and training settings in the provider’s business terms before using real assets.
How does it change search and discovery?
Visual search is growing through tools like Google Lens and Circle to Search, where people point a camera or draw around an object to search for it. Google’s AI features can also answer questions using images on the page. For marketers this means product images, alt text, captions and structured data matter more, because a model needs clean signals to connect a photo to a product.
Practical steps include descriptive file names, accurate alt text, product schema with image properties and original photography rather than stock images that appear on many other sites.
How Alien Road applies multimodal AI
In a typical engagement multimodal models show up in three places. The first is creative QA: before a campaign launches, ad sets are checked by a model against a written checklist drawn from the client’s brand guide, and a designer reviews whatever the model flags. This shortens review rounds without removing the human sign-off.
The second is catalogue work for e-commerce clients. We start with a sample of products, generate alt text and attribute suggestions from images plus spec data, and have the client’s team grade them. Only when the acceptance rate is acceptable does the process run across the catalogue, with spot checks per batch.
The third is visibility in visual and AI search. We audit image alt text, file names and product schema, and test how AI assistants describe the client’s products from images and pages. Reports show coverage of alt text and schema before and after, plus Search Console performance for image and product results.
What should you test first?
- Pick one cross-format task your team does manually every week.
- Prepare twenty real examples and a short checklist of what a good output looks like.
- Run them through one multimodal model and grade each output.
- Note error types, especially misread numbers or invented details.
- Decide whether the time saved outweighs the checking time.
How do you write prompts for image and video inputs?
Be specific about what to look at. “Review this ad” produces a general opinion; “List any text smaller than the headline, check whether the logo appears in the top or bottom third, and quote the disclaimer if one is present” produces something checkable. Ask the model to describe what it sees before giving a judgement, which makes misreadings easier to spot.
For video, give timestamps or ask the model to return them, so a person can jump to each point and confirm it. For screenshots with numbers, ask the model to transcribe the figures first and then compare them, and check the transcription against the original before trusting any comparison.
Where to start this week
Take your last five ad creatives and your brand guideline PDF. Ask a multimodal model to check each creative against the guidelines and list problems with specific references. Compare its findings with what your designer would say.
If the model catches real issues and misses only minor ones, add it as a first-pass step in your creative review. If it misses important problems, keep it for tasks like alt text and webinar summaries, where its weaknesses matter less.
Keep reading: AI Optimization · AI Optimization