OCR vs AI document extraction, what's actually different (and when it matters)
OCR and AI extraction get used interchangeably a lot, and that's causing real confusion when people are picking a tool for document automation.
OCR just reads the pixels. It converts an image of text into machine-readable text. That's it. If your document is a clean, consistently formatted form, OCR alone gets you most of the way there because the text is now searchable and grabbable by position or keyword.
The problem starts the moment your documents aren't consistent. Invoices from 40 different vendors, purchase orders with different templates, contracts that vary. OCR gives you raw text, but you still need something to figure out what's actually the invoice number versus a random number that shows up in the terms section. That's usually where regex and fixed capture zones come in, and where they usually break the moment a vendor changes their template slightly.
AI extraction works differently. Instead of reading position on a page, it reads meaning. It understands that a block of text represents an "invoice number" as a concept, not because it sits in a specific pixel range, but from context and pattern. That's why it survives template changes that would break a regex-based setup.
Rough rule of thumb: if every document comes from a single, unchanging template, OCR plus fixed rules is usually enough and cheaper to build. If your documents come from multiple sources or formats that shift over time, that's where AI extraction earns its complexity.
Curious what people here are actually dealing with. Are you extracting from a handful of consistent templates, or a mess of different formats?