Definition
Multimodal AI is an artificial intelligent system that processes different kinds of data such as text, images, video and audio to produce content, form insights and forecast outcomes. According to Aaron Myers, chief technology officer at AI-powered recruiting platform Suited, “It’s really an attempt to replicate how humans perceive. We have five different senses, all of it giving us different data that we can use to make decisions or take actions. Multimodal models are attempting to do the same thing.” [1]
For instance, you can input a photo of Nike Lake in Enugu into a multimodal AI system, and produce an output summary of its characteristics: "Artificial reservoir created from former coal mines, now serving as recreational center with boat rides and fishing activities in Enugu State's metropolitan area. Or it could receive a written description with an instruction to produce an image output: "raised wooden houses on stilts near riverbank with fishing nets," generate an image of waterfront communities in Abia State.
A multimodal AI process [2]
Origin
The development of key multimodal AI models can be traced from 2018 to 2024. Major milestones include the introduction of the Transformer (2018), vision-language models such as CLIP (2021) and Flamingo (2022), generative systems like DALL·E 2 (2022), and highly integrated multimodal agents such as GPT-4o and Gemini (2024). This timeline shows the gradual integration of modalities and the movement toward unified AI capabilities [3].
Context and Usage
Though Multimodal
AI has a long way to go, and still the possibilities are limitless. It is an exciting development that can be used
in the following ways;
- Improving chatbot and virtual assistant experiences through processing a variety of inputs and creating more sophisticated outputs.
- Banking and finance sectors, among others, use multimodal AI to boost fraud detection and risk assessment.
- By combining data from multiple sensors such as cameras, radar, and lidar), self-driving cars perform better.
- It enables robots to have more human-like behavior and abilities, by helping them better understand and interact with their environment.
- Multimodal AI are used to build new medical diagnostic tools that use data such as images from scans, health records, and genetic testing results.
- Analyzing social media data including text, images, and videos for improved content moderation and trend detection [4].
Why it Matters
The field of
multimodal AI is developing at a fast pace, with new models and innovative use
cases emerging almost every day, changing the possibilities with AI. Multimodal
gen AI models are ideal for today’s business requirements. As Internet of
Things (IoT)–enabled devices collect more types and greater volumes of data
than ever before, organizations can use multimodal AI models to process and
integrate multisensory information, then deliver the increasingly personalized
experiences that customers seek in retail, healthcare, and entertainment.
Also, with Multimodal gen AI models, technology becomes easier to use for nontechnical users. Because the models can process multisensory inputs, users are able to interact with them by speaking, gesturing, or using an augmented reality or virtual reality controller. The ease of use also means that more people of varying abilities can reap the benefits that gen AI offers, such as increased productivity [5].
In Practice
Google Gemini is good example of a real-life case study of multimodal AI in practice. It connects visual and textual data to produce meaningful insights. For instance, it can analyze images and generate related text, such as creating a recipe from a photo of a prepared dish.
References
- Urwin, M. (2024). Multimodal AI: What It Is and How It Works
- Luna, J., C. (2024). What is Multimodal AI? In Researchgate.
- Andrew Ng. (2025). The Evolution of Multimodal AI: Creating New Possibilities.
- Curtis, A., Kidd, C. (2024). What Is Multimodal AI? A Complete Introduction
- Mckinsey. (2025). What is multimodal AI?