Multimodal AI workflows are repeatable processes where one model reads more than one type of input, such as images, screenshots, PDFs, audio or video, alongside text. Marketing teams use them to review ad creative, extract data from documents, summarize recorded calls and check landing pages from screenshots, saving hours on work that used to mean watching, reading and retyping.
Models such as GPT-4o and its successors, Google’s Gemini and Anthropic’s Claude accept images and documents, and several handle audio and video directly. The capability is no longer rare. What separates useful results from novelty is a defined process with clear inputs and a human check.
What does multimodal mean in practice?
A multimodal model can take a screenshot of an ad and a brand guideline PDF in the same request and tell you where the ad breaks the guideline. It can listen to a 40-minute sales call and list objections. It can read a scanned invoice and return the line items as a table.
- Images: ads, product photos, screenshots, charts, handwritten notes.
- Documents: PDFs with tables and images, slide decks, scanned forms.
- Audio: calls, podcasts, voice notes, depending on the model.
- Video: recorded webinars, ad videos and screen recordings, supported natively by some models, notably Gemini.
Limits remain. Models can misread small text, misjudge colours and miss details in long videos. Every workflow below includes a verification step for that reason.
Workflow 1: How do you check ad creative against brand rules?
Input: the brand guideline PDF and a batch of ad images. Output: a table listing each ad, any rule it breaks and the reason. Time saved: typically most of a manual review round, since the person now checks flagged items instead of every ad.
- Turn the guideline into numbered rules: logo placement, colours, font use, required legal text.
- Send the rules and up to 10-20 images per request, labelled by file name.
- Ask for a table: file name, rule number broken, short reason.
- Have a designer review flagged items and spot-check two unflagged ones.
- Add any missed issue to the rules list for the next round.
Workflow 2: Turning sales calls into marketing insight
Recorded calls hold the language customers actually use, but nobody has time to listen to them. A multimodal or speech-capable model can transcribe and summarize them, and a second step pulls out patterns. Get consent to record and analyse calls in line with local law first.
- Export recordings or transcripts from the call platform, removing personal details where possible.
- Ask for each call: main problem, objections, competitor mentions and exact customer phrases.
- Combine 20-30 summaries and ask for the five most common themes with example quotes.
- Share the themes with content and ads teams as copy and FAQ input.
- Repeat monthly to see which objections grow or fade.
Workflow 3: Landing page review from screenshots
Take full-page screenshots of a landing page on desktop and mobile. Ask the model to compare them against a checklist: is the offer visible above the fold, is the call to action clear, does the headline match the ad, are there trust signals near the form? The model sees the layout as a visitor would, which a text-only crawl cannot.
Treat its answers as a draft audit. Models are reasonable at spotting missing elements and weaker at judging subtle design quality. A designer or CRO specialist should confirm before anything changes.
Workflow 4: Document extraction for reporting
Agencies and in-house teams receive reports as PDFs: media plans, partner invoices, market research. Copying numbers into a spreadsheet is slow and error-prone. A multimodal model can read the PDF, including tables and charts, and return structured data.
How to keep extraction accurate
- Ask for a fixed output format, such as CSV columns you define.
- Ask the model to mark any value it could not read clearly.
- Check totals: extracted line items should add up to the document’s total.
- Spot-check 10 percent of rows against the source in the first month.
- Keep the original document linked next to the extracted data.
Workflow 5: Video content repurposing
A one-hour webinar can become a blog outline, five social clips and an FAQ. Models that accept video can identify the strongest segments, write timestamps and draft captions. Editing software still does the cutting, but finding the moments is the slow part, and that is what the model speeds up.
For example, a team might upload a webinar recording and ask for the six moments where the speaker answers a specific audience question, with start and end times. An editor then cuts those segments in a fraction of the time it would take to scrub through the recording.
What should you watch out for?
- Privacy: screenshots and calls often contain personal data; mask or exclude it.
- Confidentiality: use business plans with clear data terms for client material.
- Small text: models misread tiny legal copy; zoom or crop when it matters.
- Overtrust: a fluent summary can omit the most important point.
- Cost: video and audio inputs use many tokens; test cost on a sample first.
How Alien Road applies multimodal AI workflows
In a typical engagement we start by timing the client’s current review and reporting tasks for two weeks: creative checks, call reviews, PDF data entry. The tasks that combine high hours with clear rules become the first candidates.
We then write each workflow as a one-page procedure, with inputs, prompt template, output format and the human check, and run it in parallel with the old method for two cycles. Measurement compares time spent and errors found between the two. Once the new workflow matches or beats the old one, it replaces it, and the monthly report tracks hours saved and any errors caught in review.
Where to start this week
Choose the review task your team complains about most. Write its rules as a numbered list and test one multimodal model on last month’s real inputs.
- Pick one task with images, PDFs or recordings as input.
- Write the rules or questions as a numbered list.
- Run ten real examples and compare with what your team found.
- Note time taken for both methods.
- Keep the human check, whatever the result.
If the model finds most of what your team found in a fraction of the time, write the procedure down and schedule it.
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