What Is Multimodal AI? A Guide for SMEs
Erwin Berkouwer
Multimodal AI is artificial intelligence that processes text, images, audio and video together and connects them, rather than analysing each data type separately. For SMEs this matters in processes where customers or staff mix photos, speech and text, such as complaint handling or maintenance work.
Multimodal AI combines text, images and speech into one coherent understanding. Learn how it works and when it adds value for SMEs.
What is multimodal AI?
Multimodal AI is artificial intelligence that processes several types of data at once: text, images, audio, and sometimes video. Where a classic AI model only reads text or only recognises a photo, a multimodal model combines these information streams into one understanding of a situation. Models such as GPT-4o, Gemini and Claude can, for example, look at a photo of an invoice, read the text inside it, and explain in plain language what it says.
For a small or mid-sized business, the practical difference is simple: you can send an AI system a photo, a voice note, or a screenshot instead of converting everything to text by hand first.
How does multimodal AI work?
A multimodal model learns during training to translate different types of input into the same internal "language" of meaning, known as embeddings. That allows the model to connect, for instance, a photo of a broken machine with a written fault report and a mechanic's spoken explanation, and interpret all three sources together.
The core usually consists of three steps:
- Encoding: each type of input (text, image, sound) is converted into numerical representations.
- Combining: the model finds connections between those representations, even when they come from different sources.
- Generating: based on that combined understanding, the model produces a response, which can itself be text, an image, or audio.
The defining feature of multimodal AI is not that it "reads" multiple data sources, but that it interprets them together. Using a separate image recognition model and a separate language model side by side does not make a system multimodal.
Why does this matter for SMEs?
Many business processes are naturally multimodal, even if nobody calls them that. Consider:
| Process | Data sources | Multimodal AI application |
|---|---|---|
| Complaint handling | Photo of product + customer text | Automatic triage and response draft |
| Maintenance and technical work | Photo of a fault + technician's voice note | Automatically drafting a work order |
| Sales and quotes | Sketch or photo + specification text | Faster quote generation |
| Customer service | Screenshot + chat message | Faster grasp of the right context |
For smaller organisations, this mostly means time saved on work that is currently done in separate steps: looking at a photo, then typing an email, then filling in a system. Multimodal AI can merge those steps into one action.
A concrete example
An installation company receives a photo of a leaking pipe via WhatsApp, along with a short voice note from the customer. A multimodal AI system can analyse the photo to recognise the type of pipe and likely cause, transcribe the voice note into text, and combine both to draft an initial estimate of urgency and required materials for the planner. The technician no longer has to do this separately.
When to use it, and when not to
Use it when:
- Customers or staff regularly send photos, voice notes, or documents alongside text
- Manual triage or interpretation currently takes significant time
- Errors happen because context, such as a photo, isn't linked to the right text
Don't use it when:
- Your process is already fully structured, for example pure form data
- The data sources are too sensitive to process externally without clear data agreements
- There is no basic AI process in place yet; start with a single-purpose application before going multimodal
Related concepts
Multimodal AI is often mentioned alongside computer vision (image recognition as a standalone discipline), OCR (extracting text from documents and photos), and large language models (the language component). Multimodal AI combines these separate techniques into one coherent system.
Curious whether your business process is a good fit for multimodal AI? An AI scan maps where text, images, and speech are currently processed separately, and where automation saves the most time. Also see AI consultancy for implementation support, or check how AI agents can handle multimodal input within a running process.
Frequently asked questions
Is multimodal AI the same as ChatGPT?
No. ChatGPT and similar tools can use multimodal AI, for example when you upload a photo alongside a question. Multimodal AI is the underlying technology, not one specific product.
Do I need a lot of data to use multimodal AI?
For most applications you use an existing multimodal model, such as GPT-4o, Gemini, or Claude, that is already trained. You don't need your own training data to get started, but clear examples from your own process help set it up well.
Is multimodal AI expensive to implement for a small business?
That depends on scale. Individual requests through an API are often inexpensive. The cost mostly sits in setting up the surrounding process, not in the AI itself.
Can multimodal AI also handle Dutch speech and documents?
Large multimodal models generally support Dutch, although accuracy on specialised jargon or dialect can be lower than for English. Testing on your own material remains advisable.
Veelgestelde vragen
Korte, heldere antwoorden die je helpen sneller beslissen.
Is multimodal AI the same as ChatGPT?
No. ChatGPT and similar tools can use multimodal AI, for example when you upload a photo alongside a question. Multimodal AI is the underlying technology, not one specific product.
Do I need a lot of data to use multimodal AI?
For most applications you use an existing multimodal model, such as GPT-4o, Gemini, or Claude, that is already trained. You don't need your own training data to get started, but clear examples from your own process help set it up well.
Is multimodal AI expensive to implement for a small business?
That depends on scale. Individual requests through an API are often inexpensive. The cost mostly sits in setting up the surrounding process, not in the AI itself.
Can multimodal AI also handle Dutch speech and documents?
Large multimodal models generally support Dutch, although accuracy on specialised jargon or dialect can be lower than for English. Testing on your own material remains advisable.
