AI vs. Manual Alt Text: What's the Real Difference?

If you've looked at alt-text tools before, you've probably run into two very different approaches, and neither one is quite right on its own.

Manual alt text — a person actually looking at each image and writing a description — is the most accurate option, by definition. It's also the slowest. For a handful of images on a small site, it's completely reasonable. For an e-commerce catalogue with ten thousand product photos, or a content site with years of published images, it's not realistic to do by hand, and it usually just doesn't happen.

The other common approach is what we'd call template-based generation: rules that build an alt attribute out of existing metadata — the filename, the product title, the category, maybe the page's H1. This scales instantly and costs almost nothing to run, but it isn't actually describing the image. A product titled "Blue Cotton T-Shirt" gets alt text that says "Blue Cotton T-Shirt" regardless of whether the photo shows the shirt folded, worn by a model, laid flat, or shown from the back. It satisfies the letter of "has an alt attribute" without the substance of actually describing what's visible.

AI vision models sit in a genuinely different spot: the model looks at the actual pixels of the image and generates a description based on what's there, the same way a person would, just far faster. It won't catch every nuance a careful human writer would (a subtle brand detail, an inside joke in a meme, context only a person would know), which is exactly why a review step before publishing still matters. But for the overwhelming majority of product photos, blog images, and illustrations, it produces something a template can't: a description of what the image actually shows.

The practical takeaway: AI vision generation is the first approach that's both fast enough to run against a real catalogue and accurate enough that the result is worth publishing without a full manual rewrite.