Imagine a screen reader reading out a description of a photo on a website - not just "image," but something like: "A young woman in a wheelchair smiles as she pushes herself up a gently sloped ramp, sunlight glinting off the metal handrails. Behind her, a tree with bright orange leaves stands beside a brick building with large windows." That’s not a human writing it. That’s AI. And it’s changing how people who are blind or have low vision experience the web.

What Image-to-Text AI Actually Does

Image-to-text generative AI doesn’t just read words in pictures - that’s OCR, and it’s been around for decades. Instead, it understands what’s happening in an image. It looks at colors, shapes, actions, context, and even mood. Then it turns that into natural language. This is possible because of multimodal models like CLIP and BLIP, which were trained on hundreds of millions of image-text pairs scraped from the internet. These models don’t just match words to pixels; they learn how language and visuals relate to each other.

For example, CLIP, developed by OpenAI in 2021, uses two neural networks: one for images, one for text. Both are trained together in a shared space where similar meanings are close together. So when you show it a picture of a dog running on a beach, it finds the text embedding that’s closest to that image - maybe "golden retriever chasing waves at sunset." It’s not memorizing captions. It’s inferring meaning.

Why This Matters for Accessibility

Alt text isn’t optional. It’s required by accessibility laws like WCAG and the ADA. But writing good alt text for thousands of images? That’s a nightmare for content teams. Manual tagging is slow, expensive, and often inconsistent. That’s where AI steps in.

Companies like Shopify and Zalando started using AI-generated alt text for product images. One retailer reported a 23% increase in search relevance because customers could now find items by describing what they saw - "red dress with ruffles," not just "dress 4567." But the real win is for people who rely on screen readers. Imagine being able to understand a photo of your friend’s wedding, a news article’s headline image, or a medical diagram without needing someone to describe it to you.

Still, the technology isn’t perfect. In 2023, a nonprofit tested AI-generated alt text on 1,200 images. For simple objects - a cat, a coffee cup, a tree - it worked well 80% of the time. But for anything involving people, especially people with disabilities, it failed. One image of a man using a prosthetic leg was described as "a person with a metal stick." Another showed a guide dog leading its owner - the AI said "a dog walking beside a person." No mention of the leash, the harness, or the fact that this was a service animal. That’s not just inaccurate. It’s dangerous.

How It Works Under the Hood

Modern systems like BLIP-2 and BLIP-3 follow a three-step process:

1. Image encoding: The image is broken down into visual embeddings - numerical representations of shapes, textures, and spatial relationships.
2. Text generation: The model uses those embeddings to predict the most likely text description by comparing them to learned patterns from training data.
3. Refinement: Natural language processing cleans up the output - fixing grammar, removing repetition, and ensuring clarity.

BLIP-3, released in January 2024, was trained specifically for accessibility. It was shown 50,000 images labeled with human-written alt text focused on safety, function, and context - not just aesthetics. The result? A 92.4% accuracy rate on a new benchmark called A11yCaption. That’s a big jump from earlier models, which scored around 75%.

But even BLIP-3 struggles with abstract scenes. A photo of a protest sign reading "Justice for All" might be described as "a piece of paper with writing." It misses the emotion, the intent, the social meaning. That’s not a bug - it’s a limitation of training data. If the model never saw enough examples of protest signs paired with accurate context, it won’t know how to describe them.

Where It Fails - And Why

The biggest problems aren’t technical. They’re ethical.

Studies show CLIP models are 28.7% less accurate on images of people from non-Western cultures. That means someone in Nigeria or Indonesia is more likely to get a wrong or missing description than someone in the U.S. or Europe. Why? Because the training data was mostly Western photos from Instagram and Flickr.

Another issue: people with visible disabilities. Stanford researchers found AI-generated alt text had 31.2% higher error rates on images including people with mobility aids, hearing devices, or other assistive tools. One image of a child using a communication device was labeled as "a boy holding a plastic box." No mention of speech-generating technology. No context. Just a box.

And then there’s adversarial attacks. MIT researchers showed that adding nearly invisible noise to an image - something a human wouldn’t notice - could make the AI describe a stop sign as "a red circle with white writing." That’s not a mistake. That’s a vulnerability. If a website uses AI to auto-generate alt text for safety-critical images - like exit signs or medical diagrams - a single pixel change could mislead someone.

Real-World Use Cases and Trade-Offs

Some industries are using this tech wisely. E-commerce platforms use it for product images. Social media apps auto-generate captions for user-uploaded photos. Digital publishers use it to tag stock photos. But they all have one thing in common: human review.

Amazon’s Titan Multimodal Embeddings, launched in late 2023, is used internally to tag millions of product images. But the company doesn’t rely on it for customer-facing alt text. Instead, it uses AI to suggest tags, then humans verify them. Same with Microsoft’s Seeing AI app. The new version uses multimodal AI to describe scenes, but it still asks users to confirm or correct the output before reading it aloud.

For most organizations, the smart approach is hybrid:

• Use AI to generate a first draft of alt text for bulk images.
• Flag images with people, actions, or complex scenes for human review.
• Use a checklist: Does it mention the subject? The action? The context? Is it under 125 characters?

One web developer in Seattle told me she uses BLIP-3 with a simple rule: if the image has a person, it goes to a human. If it’s a product shot or landscape, AI is fine. She cut her tagging time by 70% and reduced accessibility complaints by 89%.

Cost and Implementation

Running this tech isn’t cheap. Deploying a model like BLIP-3 on AWS requires a p3.2xlarge GPU instance - about $3.06 per hour. You’ll need at least 16GB of VRAM. For small businesses, that’s out of reach. But you don’t need to host it yourself.

Services like Hugging Face offer pre-trained models you can plug into your CMS with a few lines of code. Shopify’s built-in tool does it for you. You pay per image processed, not per server hour. That’s more practical for most sites.

Training your own model? That’s a full-time job. You need Python, PyTorch, and access to labeled datasets. Most companies don’t have the team. But you don’t need to. Use the tools that already exist.

What’s Coming Next

The W3C is working on new guidelines for AI-generated alt text. By mid-2026, they plan to require 95% accuracy on safety-critical elements - like exit signs, medical symbols, or warning labels - before an image can be published without human review.

Microsoft, Salesforce, and Google are all racing to improve accuracy on diverse populations. BLIP-3’s training data now includes 40% more images from non-Western countries. Seeing AI’s new model can recognize 12 types of mobility aids and describe their function, not just their shape.

By 2027, experts predict AI will handle non-critical images - like decorative banners or abstract art - without human input. But for anything involving people, safety, or meaning? Humans will still be in the loop.

What You Should Do Today

If you manage a website:

• Don’t turn on auto-alt-text without testing it. Run a sample of 50 images through your system. Check for errors.
• Never use AI-generated alt text for images of people without review. Especially if they have disabilities.
• Use tools like Hugging Face’s BLIP-3 or Shopify’s built-in generator - not raw CLIP. They’re better documented and more reliable.
• Add a simple flag in your CMS: "AI-generated - needs review." Make it mandatory for images with people.
• Train your team on WCAG 2.1 alt text standards. Good alt text isn’t just descriptive - it’s functional.

AI won’t replace human judgment. But it can give us the time to do it better. The goal isn’t to automate accessibility. It’s to scale it.