Document AI's Shift From Reading Pages To Reasoning Across Them

Alberto Gimeno, CEO & Co-Founder at Invofox, helping 100+ software clients turn millions of documents into trusted data.

​When Google launched Gemini 3.5 Flash, it positioned the product as the next wave of AI built around autonomous agents. Anthropic, OpenAI and others are racing in the same direction. Agentic capability in enterprise software has become a table-stakes purchase consideration.​

Document-heavy industries like lending, insurance, compliance and healthcare are facing new challenges. AI agents that can’t reconcile documents won’t underwrite a loan, approve a claim or flag a compliance gap.

The document AI stack most enterprises bought two or three years ago was built for a different problem. Back then, the goal was obtaining clean, accurate data out of a single PDF. And that work is far from trivial: In dense, messy, real-world documents, high-accuracy extraction remains one of the hardest problems in the field.

What’s changed is that extraction alone no longer gets an enterprise to production. The new question to ask is: Can the system understand what these documents mean in relation to each other? Until the answer is yes, an enterprise shouldn’t put an autonomous AI workflow into production.

OCR Got Us Text, ML Got Us Fields, But Neither Got Us Understanding

Over the years, document AI has evolved as a stack of layers. Optical character recognition (OCR) converted scanned documents to text. Predefined templates pulled fields from invoices, forms and statements. Then, machine learning (ML) extended those capabilities to layouts the system had never seen.

Each of these layers works within a single document. Pulling clean, structured data from a single page is mature technology, yet accurate extraction on complex, real-world documents is genuinely hard. Even flawless single-document extraction leaves the harder problem untouched: understanding documents in relation to each other.