What is multimodal AI and why does it matter to digital content teams?

Real, usable AI exploded onto the planet in late 2022 with the rise of text-based ChatGPT. Shortly after, in 2023, it and other applications like Google’s Bard and Anthropic’s Claude expanded their reach by including speech input and image generation output — and they haven’t stopped there.

By introducing these multimodal capabilities, “traditional” AI platforms have become contextually richer, improving their intelligence and offerings for all consumers — from AI-powered workflows to personalization at scale, with enhanced interactions and significantly better creative assistance.

What is multimodal AI?

Current implementations of AI are mathematical (probabilistic) prediction models that process billions of nodes of data to produce a likely acceptable response. The models have been trained on as much sample data as possible (think the entire content of the internet) to be able to calculate this acceptable response. Responses can be in any one of a number of modalities, which started with text but quickly included speech, images, video, and any number of assistive mediums.

Rather than just converting everything to text, processing it, and converting it back again, multimodal AI understands these multiple media types natively. Processed through modular streams, multimodal AI tokenizes text, converts audio, and so on. The AI modular system then combines these modalities (via a process known as “fusion”) into a rich embedding that allows for understanding context from multiple inputs better.

Some examples of existing early unimodal (single-function) models include Google’s PaLM and OpenAI’s GPT-3. These models have hundreds of billions of parameters that allow them to perform well at tasks like common sense reasoning, code generation, and translation. Open AI’s DALL·E and DALL·E 2 are powerful image generation models that were developed after smaller models were trained on parameters numbering in only the tens of billions.

Starting in 2021, DALL·E and CLIP (both by OpenAI) demonstrated that text-to-image and zero-shot classification (understanding images without being trained on them) were possible. The rate of progress increased once the broader public appeal developed, and later models like GPT-4o introduced true multimodal AI that could accept and generate text, images, and audio. Future models (like GPT-5) from different vendors promise to accelerate progress with their improved reasoning and expansion into full film (video) generation and beyond.

Currently, generative models that work across mediums for both input and output include:

• GPT-4/4o by OpenAI. Processes text, images, and in some cases video, which powers the popular ChatGPT app, but can also provide enhanced surrounding awareness for those with visual disabilities.

• Google Gemini. Natively supports text, images, video, and audio and can reason, simulate, and code applications.

• Meta’s ImageBind. Supports six data modalities (image, text, audio, depth, thermal, and IMU) for cross‐modal retrieval and embedding, which could be used for activities like searching for sounds of dogs by providing an image of a dog.

• Hugging Face. Hosts a wide range of different open-source models and tools for fine-tuning, making it easier for developers to fine-tune, deploy, and integrate multimodal AI into their applications and pipelines.

Many cloud platforms offer AI capabilities, such as the AWS Machine Learning service Amazon Bedrock, Azure Cognitive Services, and Google Cloud AI. All these models have APIs providing access for platforms looking to leverage them. This goes beyond their free chatbot interfaces for paying customers looking to build their own AI-powered products. Examples of services built on top of these multimodal AI models are:

• Text- and image-based support assistants for round-the-clock customer support.

• Personalized content recommendations and product discovery, which helps increase user click-through rates.

• Visual search for product discovery from photos of objects and products.

• Ad creation generated from text-based image, audio, and video, allowing marketers and podcasters to follow a company or brand’s specific style.

• Product description management and image tagging to enhance content creation and pipeline management.

Why it matters

Multimodal AI has a better understanding of information than unimodal AI because it can consume input and produce output via different formats. As applications evolve to include more services and online capabilities, the assistance provided by multimodal AI services can encompass more input from the user, such as hearing your request and giving you information based on your image. Combining input from multiple sources and formats enables the AI to understand the material better.

Multimodal AI models and their offerings matter for developers because they can automate the generation of things like tags and metadata with smart tagging and classification. APIs also provide easier, real-time integration of intelligent features like enhanced accessibility and smarter search, leading to overall smarter applications.

Content teams can use multimodal AI to provide creative assistance for alt text, metadata, and more. Faster and more intelligent asset management, again with automated metadata and alt text, are easier to produce, allowing creatives to focus on the bulk of their article or creative editorial content.

Content teams and digital product owners are now using AI to remove these manual steps and streamlining their workflows with everything from asset management to accessibility. Teams can make use of far more digital mediums to create content and have richer, more accessible user experiences that boost engagement with automation tools like translation services.

It’s not just translation — generation of alt text or even image manipulation to produce better visibility for those with disabilities enables digital content producers to enhance the accessibility of their products. This means that an image can be described in text or speech and adapted for those with visual impairments, not just having the alt text read aloud by classic text-to-speech.

With multimodal AI services, consumers can have personalized experiences that automatically adapt digital products to their individual context, like style recommendations and personalized previews while shopping.

The practical impact of multimodal AI

Developers can integrate intelligent features into digital products with multimodal AI by adding auto-generated FAQ sections from existing content, extracting meta descriptions from content both for SEO and HTML metadata, and for optimizing keyword density by both generating and checking for keyword use.

Teams can also enhance their workflow by integrating multimodal AI services and tools to pull contextual information from the different media types they use to produce their digital content. As well as retrieving this information autonomously, AI can suggest improvements and search for content as well as verifying that certain content is safe for a given audience.

For product owners, multimodal AI makes it easier to launch multilingual apps with automatically localized descriptions. Removing the effort and cost of manual translation ensures content feels tailored to different regions, which can greatly expand the audience reach for a product. Build onto this the social media and marketing effort of targeting customer segments and AI begins to really shine as a practical solution for reducing the marketing workload.

How multimodal AI benefits composable platforms

Composable platforms allow you to bring together multiple services to build your product without building everything in-house or from scratch. As multimodal AI grows, it can assist in many areas by plugging right in like any other service and providing the content discussed above, either automatically or by offering suggestions far better than previous Web-2 solutions could.

A content platform like Contentful allows multimodal services and tools to be integrated. This enables faster and smarter tagging of any media a digital team is working with. The platform can have a much broader search to find automated suggestions for items, and the items can be related more accurately to the content being produced.

You can now better spend your time by focusing on refining the content to meet your specific needs. Once tailored to fit different countries or regions — including enriching metadata, improved wording, and associated imagery — AI makes these enhancements easier with the advanced tools integrated directly into the composable platform.

A structured content model, created and managed with the Contentful Platform, is ideal for plugging in multimodal AI components to enhance your workflow pipeline and content production. Most importantly, the structured content model at the heart of a composable platform like Contentful provides the framework to capture and deliver this intelligence consistently across channels. Together these capabilities create more accessible, personalized, and scalable digital content.

For example, a marketing team aiming to improve content performance and engagement could use Contentful for structured content, Stripe for payments, and Algolia for search. Then they could integrate a multimodal AI service to automatically generate image captions, transcribe audio from dictations, or enrich metadata for SEO. Because each service is modular and API-driven, the AI can slot into the workflow without disrupting the rest of the stack, helping the team deliver a more personalized and efficient digital experience.

Composable architectures are essential because they provide the flexibility to integrate new and emerging technologies like multimodal AI into production workflows, instead of being locked into rigid, enclosed systems. This ability to adapt not only accelerates innovation, it ensures that as AI capabilities expand, organizations can evolve without costly re-platforming and future-proofing.

Multimodal AI models and composable platforms: Creative and powerful

The multimodal AI tools available are not just about smarter tagging or metadata; they’re about unlocking true consumer personalization at scale. The key transformative benefit of having a composable platform with these AI services integrated directly into the tools you’re familiar with is flexibility. You can adapt and scale content to any target region with dozens of variations and formats without the traditional time delays and cost limitations.

This gives content teams the flexibility to experiment with and rapidly iterate on dozens of campaign variations. You now have the tools to test formats, imagery, and written content quickly and at scale so you can find what resonates with different target audiences.

When fully utilizing a platform like Contentful, you have the freedom and adaptive workflow tools to experiment with the integration options available for multimodal AI tools. For digital leaders looking to scale personalization and productivity, while keeping costs and complexity in check, Contentful offers the foundation to put multimodal AI into action today.

Take a tour

Chat with our team

Your experiences should wow, not wait. Let's talk.

Inspiration for your inbox

Subscribe and stay up-to-date on best practices for delivering modern digital experiences.