Enterprises are sitting on vast volumes of unstructured data locked away in documents, PDFs and images, limiting how effectively they can be used in modern AI systems. As organisations accelerate investment in copilots, retrieval-augmented generation and autonomous agents, the quality and structure of underlying data have become a critical constraint. Many AI initiatives are stalling not because of model capability, but because information cannot be reliably extracted, governed or trusted at scale.

Based on Verdantix research in our upcoming report, Market Insight: Document AI Solutions For Enterprise, buyers are increasingly struggling to define what “production-grade” Document AI looks like, and how to evaluate trade-offs between scalability, governance, accuracy and flexibility across these competing approaches.

In this session, we will cut through that complexity and translate our research into insights for both enterprise buyers and vendors. We will explore how leading organisations are shifting from OCR to next-generation, agentic Document AI, what production-grade reliability means in real-world deployments, and how different architectural routes are shaping adoption.