Posted by Pat TullyPublished: Dec 4, 2025Updated: Dec 8, 2025
What Is Multimodal AI?
Multimodal AI is reshaping how companies collect, understand, and act on information. Instead of relying on a single data type (like text) multimodal systems interpret multiple inputs such as images, video, audio, product attributes, and more. The result is richer context, better automation, and more accurate decision-making.
This is critical for eCommerce and product-led brands because shoppers expect faster answers, complete information, and seamless digital experiences. Multimodal AI gives retailers and manufacturers a new way to scale those experiences with intelligence that mirrors human understanding.
Pat Tully
Sr. Content Marketing Manager
Key Takeaways
Multimodal AI combines multiple data types (i.e. text, images, audio, video, and structured product data) to improve accuracy and context.
Retailers and distributors use multimodal AI to automate content creation, improve search experiences, and enhance product discovery.
Multimodal AI refers to artificial intelligence systems that process and correlate more than one type of input, such as:
Text
Images
Audio
Video
Numerical or structured data
Unlike earlier-generation models that were text-only or image-only, multimodal AI blends these formats to understand queries the same way humans do—through multiple senses.
For example:
A multimodal model can “look” at an image of a drill, understand its category, extract attributes like voltage or chuck size, and generate a product description, all in one workflow. This is possible because multimodal systems combine perception, reasoning, and generation.
Use Cases
Multimodal AI is rapidly expanding across industries, especially in eCommerce, retail, B2B distribution, and manufacturing. Key use cases include:
Automated product description generation using images + text inputs
Product tagging and enrichment based on visual attributes
Image-based search, where shoppers upload photos to find similar products
Video analysis, such as extracting attributes from product demos
Voice-enabled search or support
Quality control in manufacturing via image recognition
Customer service automation, powered by combined text, visuals, and metadata
According to research from MIT and other leading institutions, multimodal systems deliver greater accuracy because they process data in context—mirroring the way humans combine sight, sound, and language to understand the world.
Why It Matters for Retail, Manufacturing, and Distribution
Challenge #1: Product Data Is Increasingly Complex
Brands today must manage enormous volumes of product information across dozens of channels. This includes:
Technical specs
Marketing copy
Digital assets
Compliance documentation
Packaging details
Product usage videos
Different teams work with different formats, creating silos and inconsistencies.
Solution: Multimodal AI Makes Product Data Actionable
Multimodal AI helps teams convert raw assets into usable, high-quality product content. For example:
A retailer can upload a product image and let AI identify missing attributes.
A manufacturer can use multimodal models to ensure technical specs match what appears in product photos.
A distributor can extract structured data from PDFs, labels, or images to populate product catalogs faster.
This improves accuracy and reduces manual effort—two major priorities for digital commerce teams.
How Multimodal AI Improves Digital Experiences
Key Feature #1: Understanding Context, Not Just Content
Traditional AI models understand data in one dimension. Multimodal AI interprets context across formats.
For example, someone may search for “a heavy-duty drill with a side handle.” A multimodal system can:
Interpret the text query.
Scan product images for the presence of the side handle.
Check product specs to confirm torque levels.
Surface the most accurate matches.
This improves search results, recommendation engines, and product discovery—a major driver of conversion rates.
Use Case Example: Apparel & Fashion Retailers
Fashion retailers often manage thousands of SKUs with subtle variations—colors, patterns, textures, and cuts. Multimodal AI can:
Detect color accurately from images (vs relying on supplier-provided data).
Identify patterns such as stripes, floral prints, or herringbone fabric.
Automatically classify items by style, season, or use case.
Generate marketing copy that reflects both the visual and technical attributes.
This is a major time-saver for merchandising and eCommerce teams who otherwise manually enrich each product.
Multimodal and PIM: Why Product Information Matters
Multimodal AI is powerful, but it relies on clean, consistent, and centralized product information to work effectively. This is where product information management (PIM) comes in.
A PIM platform houses the structured data—attributes, descriptions, categories, relationships—that multimodal AI models reference when generating new content or validating outputs.
Without a PIM, multimodal AI may produce:
Incorrect or incomplete product descriptions
Conflicting attributes
Duplicate data
Inconsistent naming conventions
With a unified system, brands can scale AI automation safely and accurately.
For readers new to centralized product data, visit Pimberly’s guide on what PIM is and how it works to understand why structured information is essential for AI-driven commerce.
FAQs
Q: How is multimodal AI different from traditional AI?
A: Traditional AI models are single-modal, meaning they process only one data type—usually text. Multimodal AI combines images, text, video, audio, and structured data to understand inputs holistically. This makes results more accurate and contextually relevant.
Q: Do you need a large dataset to use multimodal AI?
A: Not necessarily. Modern multimodal models are pre-trained on massive datasets and can be fine-tuned with smaller sets of product images or attributes. However, the quality of the data matters far more than quantity—another reason PIM systems are critical.
Q: What industries benefit most from multimodal AI?
A: Retail, fashion, homewares, manufacturing, construction, and distribution see immediate ROI because they rely heavily on product content and visual data. Any industry managing large product catalogs or detailed technical information stands to benefit.
Q: Is multimodal AI safe for regulated industries?
A: Yes—when paired with secure infrastructure and governance. Many models allow private, isolated processing where uploaded assets are not stored or used for training. Organizations should verify data privacy policies before deployment.
What Multimodal AI Means for Digital Commerce Leaders
To summarize, multimodal AI is one of the biggest leaps forward in artificial intelligence functionality. By combining text, images, video, audio, and structured data, it gives brands a deeper understanding of their products and customers.
For retailers, manufacturers, and distributors, multimodal AI unlocks:
Faster product onboarding
Higher-quality content
Smarter search and discovery
Better personalization
Reduced manual work across teams
But multimodal AI is only as strong as the product information behind it. That’s why many companies pair AI initiatives with modern PIM platforms to ensure data quality, consistency, and scalability.
If you’re exploring ways to bring AI automation into your product workflows, improving your product data foundation is the best place to start. Strong product data + multimodal models = the future of digital commerce.
