Multimodal AI mistakes usually come from trusting a model’s reading of an image, chart, audio file or video as if it were exact. These models, such as GPT-4o class models, Claude and Gemini, can describe and reason across text, images and sound, but they misread small text, invent details and handle long media unevenly. Agencies avoid problems by testing on real material, constraining the task and checking outputs.
Multimodal means a single model accepts more than one type of input. You can upload a screenshot of an analytics dashboard and ask what changed, attach a product photo and ask for a description, or give a recorded call and ask for a summary. Some models also produce images or speech. This flexibility is why marketing teams adopted them quickly, and why errors spread just as fast.
Mistake 1: Why is trusting chart and screenshot readings risky?
Models are good at describing what a chart shows in general but less reliable at exact values. Ask for the conversion rate in week 14 from a screenshot and you may get a plausible number that is wrong. Dense tables, small fonts and overlapping labels make it worse.
The fix is simple. When numbers matter, give the model the data, not a picture of it. Export the CSV from GA4 or the ad platform. Use screenshots for layout questions, such as “is the call to action visible above the fold?”, not for figures that go into a report.
Mistake 2: Why do vague image prompts fail?
“Describe this image” returns a generic description. For marketing work, the instruction should state the use: alt text under 125 characters, a product description following a template, or a list of visible defects. Models also need to know what not to do, for instance “do not guess the material if it is not visible”.
A better prompt pattern
- State the role and the purpose of the output.
- Define the format and length.
- List the fields to extract.
- Tell the model to write “not visible” rather than guess.
Mistake 3: Does the model really watch the whole video?
Video support varies. Some models sample frames at intervals, so short events can be missed entirely. Long videos may be cut or summarised at a coarse level. A model asked whether a logo appears in a 30-minute recording may answer confidently without having looked at the frame where it does.
For compliance checks, such as whether a disclaimer appears on screen, ask for time stamps and verify them. For long files, split them into segments and process each one.
Mistake 4: How accurate is audio understanding in your language?
Transcription and audio understanding are strong in major languages but uneven elsewhere. Mixed-language speech, heavy accents, industry jargon and background noise all lower accuracy. A sales call in Turkish with English product names is a typical hard case.
Test with your own recordings. Keep a short glossary of brand and product names and include it in the instructions where the tool allows. Have a native speaker review a sample of transcripts before using them for anything client-facing.
Mistake 5: Uploading sensitive material without checking terms
Screenshots often contain more than intended: customer emails in a CRM view, personal data in a support ticket, revenue numbers in a dashboard corner. Before uploading, crop or blur what the task does not need, and check the data handling terms of the plan you use. Enterprise and API plans usually offer clearer commitments on training and retention than consumer accounts.
Generated images and voices going out unchecked
When multimodal models create images or audio, the usual risks apply: likeness of real people, trademarks, wrong product details and missing disclosure. Voice output can sound like a real person if prompted carelessly. Keep a person responsible for approving every public asset and follow platform rules on labelling synthetic media.
How do you test a multimodal workflow before relying on it?
Build a small benchmark from real material, not from vendor demos.
- Collect 30 to 50 representative inputs: real screenshots, product photos, call recordings.
- Write the expected output for each, done by a person.
- Run the model and compare, noting error types, not just a score.
- Change one thing at a time, such as the prompt or the model, and re-run.
- Keep the benchmark and run it again whenever the vendor updates the model.
Model updates can change behaviour without notice. A workflow that worked in spring can fail in autumn.
Keep the benchmark small enough that someone can rerun it in an hour. A test set that takes a week to score will not be used, and an unused benchmark gives the same false comfort as having none. Record the model name and date with every run so results can be compared over time.
Which tasks suit multimodal AI well?
Not every use is risky. Several marketing tasks play to the strengths of these models and tolerate small errors:
- First drafts of alt text that an editor checks before publishing.
- Grouping user-generated photos by theme for a campaign review.
- Summarising the main points of a recorded webinar for internal notes.
- Checking ad creatives against a brand checklist, such as logo placement and colour use.