Why AI Document Management Is the Hidden Backbone of GenAI Projects -

Generative AI systems get all the hype. But as Gartner points out in a 2025 report (via AI Business), most failed pilots have nothing to do with the model. They fail because the underlying document management backbone is weak. Scattered repositories, missing metadata, and inconsistent pipelines mean AI agents hallucinate, misinterpret, or simply can’t find the right files.

The takeaway: if you want generative AI that works, start with document management.

Market Signals: A Fast-Growing Sector

The Intelligent Document Processing (IDP) market was worth $2.3 billion in 2024 and is expected to grow at a 24.7% CAGR through 2034, according to Global Market Insights Inc.

At the same time, broader AI document management tools — including classification, governance, and search — are forecast as one of the fastest-growing enterprise software categories. That’s because document-heavy industries like banking, insurance, legal, and healthcare are desperate to turn unstructured files into structured knowledge.

Technical Foundation: Why RAG Needs Structured Documents

Most enterprise GenAI projects rely on retrieval-augmented generation (RAG). Instead of letting a large language model (LLM) “guess,” RAG retrieves relevant documents, chunks them, and feeds them as context into the model.

For RAG to work properly, several steps must be in place:

OCR (Optical Character Recognition). Many enterprise documents are scans or PDFs. OCR converts them into machine-readable text. Tools: ABBYY FlexiCapture, Kofax Transformation, Azure Form Recognizer.
Preprocessing & Chunking. Long documents must be split into meaningful sections. Bad chunking leads to missing context or irrelevant answers.
Embedding & Indexing. Each chunk is converted into a vector embedding (numerical representation of meaning). These are stored in vector databases such as Pinecone, Weaviate, Milvus.
Metadata Enrichment. Adding document type, source, author, and version improves retrieval precision. Without metadata, RAG struggles with conflicting or duplicate sources.
Governance & Access Control. RAG must respect permissions. If a junior employee queries, the system shouldn’t expose board minutes. This requires tight integration with DMS/IDP.
Query + Generation. When a user asks a question, the system retrieves relevant chunks, passes them to the LLM, and generates an answer — grounded in the retrieved content.

If any layer is weak (e.g., OCR errors, missing metadata), the LLM either fabricates or misleads. This is why Gartner insists: document management is not optional infrastructure — it’s the foundation. Teams like S-PRO help strategize such software development.

Tools Powering AI Document Management

Several tools are shaping this field:

ABBYY Vantage / FlexiCapture — advanced OCR and classification, widely used in finance and insurance.
Kofax Transformation — combines capture, workflow automation, and integration with ERP/CRM systems.
Azure Form Recognizer — Microsoft’s cloud service for extracting structured data from invoices, receipts, contracts.
Google Document AI — pre-trained parsers for tax forms, healthcare records, procurement docs.
UiPath Document Understanding — RPA + Artificial Intelligence for extracting data from semi-structured docs.

These are often combined with vector databases (Pinecone, Milvus, Weaviate) and LLMs like GPT-4o or Claude 3.5 in a RAG pipeline.

Why Poor Document Management Breaks AI

Gartner’s analysis highlights recurring pain points:

Scattered repositories: firms keep data across SharePoint, Dropbox, email, and legacy servers. AI agents fail without unified access.
Inconsistent structures: a mix of scanned PDFs, images, spreadsheets, with no standardized schema.
Governance gaps: no retention policies, weak access control, or missing version tracking.

When those problems persist, AI copilots can’t reliably fetch documents. Hallucinations rise, compliance risks spike, and users lose trust.

Business Implications

ROI Protection. Enterprises investing in AI risk millions if their document layer is broken. Gartner notes that low ROI in early GenAI pilots often ties back to poor document infrastructure.

Regulatory Pressure. Financial services, healthcare, and government face strict requirements for retention and redaction. A misfiled or wrongly retrieved document can lead to fines or litigation.

Operational Drag. Gartner estimates employees spend 20-30% of their time searching for documents. AI can’t reduce this if the documents themselves remain unstructured.