Early generative AI tools were mostly single-purpose: a model that handled text, and a separate one for images, with no shared understanding between them. Multimodal models process several types of input together, reading a chart image and the surrounding text as one connected input, or taking a spoken question and responding in natural speech, rather than treating each format as an isolated task.
This is what makes an AI system able to review a scanned document with diagrams, listen to a customer call and act on it, or generate a video walkthrough from a written brief, without stitching together several separate single-purpose tools.
The business implication is less about novelty and more about which workflows this actually unlocks: any process today that’s bottlenecked by a human converting one format into another (transcribing a call, describing an image, summarising a video) is a candidate for a multimodal system to close that gap directly.