What Is Multimodal AI? How It Works and Where It Helps

Not long ago, you typed words into a chatbot and got words back. Now you can show an assistant a photo, a screenshot or a spoken question and it responds. That shift has a name, multimodal AI, and it changes what's practical for a small business to hand over to a tool.

Multimodal AI explained simply

IBM describes multimodal AI as machine learning models that can process and integrate information from multiple modalities. A modality is just a type of input, such as text, images, audio or video.

A traditional AI system is built for one data type. A multimodal one combines several to build a fuller picture. It's a bit like the difference between reading a recipe and being able to watch the cook, hear the sizzle and read the notes at the same time.

A chatbot that began as text only

IBM gives a handy example. When ChatGPT launched, it was unimodal: text in, text out. Multimodal abilities were added later. Wikipedia also points to Google Gemini as one of the large multimodal models that have grown popular.

So when you hear that an assistant is multimodal, it generally means you're not limited to typing. Which kinds of input work, and on which accounts, differs between tools and changes over time, so the official help page of the tool you use is the place to check.

How multimodal AI works under the hood

IBM explains that these models build on large language models, which themselves are built on transformers. Specialised networks handle each data type, for example one type of network for images and a transformer for text.

The clever part is bringing everything together, which IBM calls data fusion. It describes three approaches:

• Early fusion. Different inputs are encoded into one shared representation space.
• Mid fusion. Inputs are combined at different stages of preprocessing.
• Late fusion. Separate models handle each type and their outputs are combined at the end.

You don't need to remember those labels. The takeaway is that the system translates a picture and a sentence into a form it can compare, so it can answer a question about the picture.

Everyday examples of multimodal AI

IBM lists several. A photo of a scene can produce a written summary of the place, and a written description can produce an image. A bird identification app can match a photo, then confirm it by analysing an audio clip of the song. A chatbot can recommend glasses sizes from a photo a customer shares. Virtual assistants can respond to both voice commands and visual cues.

For a small business, that opens up jobs like:

• Turning a photo of a handwritten list or whiteboard into typed notes.
• Asking questions about a chart or screenshot from your reporting software.
• Describing a product photo in words for a listing or alt text.
• Talking through an idea out loud and getting a written summary back.

Two of the best-known multimodal assistants are compared side by side in my guide to Claude and ChatGPT for business.

Why multimodal AI can be more dependable

IBM notes that multimodal systems are more resilient to noise and missing data. If one input is unreliable or unavailable, the others can help maintain performance. A blurry photo might still be understood if the caption explains what's in it.

That doesn't mean the answers are always right. The tool can still misread a picture, so treat what it says about an image the way you'd treat any AI answer and check anything important.

Challenges and privacy points for multimodal AI

Wikipedia lists a few hard problems for researchers. Alignment is about working out which parts of different inputs match each other, such as which words describe which part of an image. Fusion is about combining the information well. Sometimes a data source is missing entirely in real use.

The privacy point is worth your attention. Wikipedia warns that combining data sources can let people be re-identified even if they're anonymous in each source separately. Practically, that means thinking twice before uploading photos or recordings that show customers, staff or children, and checking the tool's own data controls page for what happens to uploads.

Multimodal AI versus generative AI

People mix these terms up. Generative AI is about creating new content. Multimodal AI is about handling more than one type of input or output. A tool can be one, the other or both. A text-only chatbot is generative but not multimodal. A system that describes photos is multimodal, and if it also draws pictures it's generative too.

Starting with multimodal AI in your business

1. Pick one small job. Something like summarising a screenshot or describing a product photo.
2. Use non-sensitive material first. Public images and your own product shots are a safe start.
3. Read the tool's help page. It will say which file types and accounts are supported.
4. Check the output. Look at the original image yourself before you rely on the description.

To see which assistants might suit your day-to-day work, take a look at my list of the best AI tools for small business owners.

Frequently asked questions

What is multimodal AI?

It's AI that can process and combine more than one type of information, such as text, images, audio and video, rather than just one.

What does modality mean in AI?

A modality is a type of data or input, for example written text, a photograph, a sound recording or a video clip.

Is ChatGPT multimodal?

IBM says it launched as text-only and gained multimodal abilities later. What a particular account can do varies, so check the official help page.

What's the difference between multimodal AI and generative AI?

Generative AI creates new content. Multimodal AI handles several kinds of input or output. A tool can be either or both.

What can a small business use multimodal AI for?

Reading photos of handwritten notes, asking questions about screenshots and charts, describing product images and summarising spoken ideas.

Are there privacy concerns?

Yes. Wikipedia warns combined data sources can allow re-identification, so avoid uploading images or audio of people without permission and check the tool's data controls.