Yet today, even as organizations are increasingly integrating AI to help them move faster, many teams still rely on humans to read PDFs, copy fields into spreadsheets, and re-enter data into ERP, CRM and planning systems. It’s all creating significant risk; manual processing workflows lead to delays and potential revenue loss due to human error, while lack of governance means that teams cannot reliably audit their reporting.

Leaders understand that AI automation can help them overcome these challenges. However, many are reticent to fully integrate AI into their workflows, as early investments like OCR engines, contract lifecycle management systems, and domain-specific point solutions have often under-delivered. Even as organizations experiment with GenAI, many finance, legal and operations teams still report little realized value from AI investments. The issue, however, is not AI automation itself, but the fragmented, incomplete data foundations these early tools sit on.

Without a unified, well-governed data foundation, they lack industry and organizational context, are siloed from key enterprise systems, are only built for reading, not activating. Even worse, when you attempt to build an agent workflow on top, you get an experience that is disjointed, inconsistent, and impossible to scale.

The watershed moment for document intelligence comes when an enterprise evolves from managing workflows with point tool solutions to building them on a unified, governed data foundation. This shift opens the door for a truly unified, scalable multi-agent experience that enables technical and non-technical users alike to query their structured and unstructured business data, and then take appropriate action on that data.