AI has finally joined the document management conversation—and according to software expert Dr. John Bates, it’s about to change everything
From LLMs and prompt engineering to fine-tuning and vector embeddings, the AI era has introduced a full alphabet soup of jargon to get to grips with. One of the least glamorous terms of the bunch is ‘RAG’—an unfortunate acronym, perhaps, but also one of the most quietly transformative technologies for enterprise users: retrieval-augmented generation.
What’s increasingly clear—and arguably more important than model selection itself—is that real success with AI begins not with the algorithm, but with how well you manage and work with your own documents. In knowledge-heavy environments especially, the most effective AI applications are those that seamlessly integrate existing enterprise data.
This is where document management (DM)—sometimes dismissed as old-fashioned—proves unexpectedly central. In fact, it’s the foundation on which cutting-edge AI capabilities like RAG can truly deliver. Let’s explore why that is.
From RAG to AI riches
Underneath all the jargon, RAG is simply a technique that helps an AI system find the most relevant content to a question by using a search method called vector search. The system then uses the retrieved results as source material to answer your question more accurately.
That means it’s not ‘artificially intelligent’ in itself—at its core, RAG is a data retrieval and analysis technique designed to dynamically pull in external knowledge. And it’s not just model-makers or enterprise AI developers who are excited about what RAG can do. A useful way to think about it: RAG doesn’t magically produce the final answer—it gets you closer to it. Personally, I’m far better at editing an article someone else has drafted than writing one from scratch. RAG works the same way: your LLM isn’t guessing—it’s reviewing real documents, surfacing the most relevant ones, and then adding value by summarizing or synthesising the results.
That’s why RAG-LLM outputs often feel more grounded and reliable. Even before the AI boom, there was growing interest in this technique across industries. Developers have used RAG to enhance everything from content creation to financial reporting—helping systems produce more relevant, usable results.
Thanks to use cases like training up chatbots, RAG is increasingly recognised as a reliable way to help AI systems deliver more accurate and current responses. AWS, for instance, recommends it as an effective method for feeding GenAI models the latest research, statistics, or breaking news. Some teams are even connecting their LLMs directly to live social media streams or news sites—enabling their systems to tap into constantly updating information sources.
But so far, there’s been a clear disconnect between RAG applications and the real heart of most businesses’ knowledge—the core documents and workflows that truly run operations. That separation has made sense: until now, most AI initiatives have lived on the edge of the enterprise—in sandboxes, pilots, or specific functions—not at the centre.
To become a truly mainstream capability, though, AI—and RAG—must migrate inward. It needs to work directly with the content that matters most: the invoices, reports, contracts, and internal documents that teams rely on every day to make decisions, fulfil obligations, and keep business moving.
News? Maybe not to all of us
Analysts know this and are starting to call this out more directly: integration between GenAI and core business content can’t wait. Some were surprised, for example, when Gartner recently stated that successful Generative AI deployments are “best empowered” by a strong Document Management strategy.
Specifically, Gartner notes that enterprise GenAI success hinges on having robust document management in place to provide relevant, high-quality, and secure information for grounding. Meanwhile, Bain & Company echoed the point, stating that “data remains the biggest challenge and the biggest opportunity” for AI. Their takeaway from Nvidia’s 2025 AI Developer Conference? Every standout AI use case presented only worked because it was built on clean, connected, and accessible business information.
But anyone with experience in Enterprise Content Management saw it coming. While much of the public conversation around AI fixates on which model you use—ChatGPT vs. Claude, proprietary vs. open source—the real differentiator for enterprises lies in the data layer. Increasingly, document management is becoming the infrastructure of that layer.
Modern document management isn’t just about indexing files. It’s about creating a live, contextualised, and navigable knowledge graph of your organisation’s information. Document Management itself isn’t new—enterprises have been archiving, tagging, and securing files for decades. What has changed is the complexity of those documents and the tools now available to interpret them and automate processes around them. Today’s files are often semi-structured, multi-format, and scattered across ERP systems, CRMs, email chains, and more.
That complexity is exactly where AI—and RAG in particular—can shine, but only if the underlying documents are accessible and properly integrated. That’s why I, and many others in the community, strongly agree with the direction analysts like Gartner and Bain are taking: encouraging application leaders to invest in a robust, intelligent document management strategy as the foundation for successful and responsible GenAI deployment.
I’d also argue that implementing RAG—and complementary techniques like pruning or quantisation—works best when integrated with the full suite of capabilities offered by a modern DM system. For example, our users report that pairing RAG with metadata search enables a much more precise, ‘hard’ drill-down into their information space. This layered approach boosts accuracy, relevance, and ultimately delivers greater value.
After all, no LLM has ever been trained specifically on your documents or your business, so it can’t provide domain-specific answers on its own. But by using RAG with a document management software, the model can dynamically query exactly what you need and return answers accompanied by specific citations—across potentially millions of documents—showing precisely how it arrived at those answers. This level of transparency and specificity is simply not possible with basic ChatGPT-style systems.
Expecting cordon bleu food with none of the ingredients
RAG delivers its power by combining an LLM’s generative capabilities with real, relevant data sources—which, for practical and non-generic applications, means your enterprise documents. Rather than generating responses based solely on broad training data, RAG retrieves pertinent documents or excerpts from your company’s own knowledge base and integrates that context into the model’s output.
The outcome, practitioners say, is markedly improved accuracy, fewer hallucinations, and—most importantly—answers that reflect the specific truth of your business rather than generic internet content. It’s therefore a misconception to view RAG and document management as competing approaches. In reality, RAG depends on strong document management to function effectively. The more organised, contextualised, and accessible your enterprise documents are, the more successful your RAG deployment will be.
But that intelligence only works if the underlying documents are well-prepared, which is where the strength of a mature document management system becomes crucial. Think of it as setting the table before the meal: structured data, tagged metadata, content hierarchy, permissions, and formats all play a vital role in how effectively a RAG-based system performs. As one customer I recently worked with put it, using RAG without that foundation is like “trying to cook a gourmet meal with all the great ingredients still in the fridge.”
This is also why the shift toward ‘composable AI’ matters so much. Instead of searching for one-size-fits-all AI platforms, forward-thinking enterprises are building tailored AI pipelines that blend technologies like OCR, vector databases, document intelligence, and—of course—RAG, all grounded in a robust document management core.
In this new paradigm, modern, integrated document management is no longer a back-office afterthought—it’s a strategic enabler. It will usher in a new era of what we call ‘Super Human Search’, where business users can simply ask, ‘What are the payment terms on our top five vendor contracts from last year?’ and receive a contextualised, accurate answer instantly.
Sort your document stack
But none of this works without good document management—or, as Bain would put it, good data. If your documents are inconsistent, unlabeled, or siloed across disconnected systems, the retrieval layer will struggle. And if your search infrastructure is brittle or shallow, the LLM will end up guessing instead of grounding its answers. That’s why, as a company, we put so much focus on end-to-end document intelligence: capturing, understanding, automating, managing, integrating and collaborating with content throughout its lifecycle.
The key message here is that DM system owners can become crucial enablers of AI-powered business intelligence. But to get there, the focus shouldn’t be solely on your LLM or RAG capabilities—it starts with the basic health of your knowledge base. Is your content centralised? Is it properly tagged and searchable? Can it interface seamlessly with modern AI tools?
AI isn’t a magic wand—it’s a mirror reflecting the quality of your data, and it can’t rescue you from poor document hygiene. But if your documents are in order, RAG can make them sing. In a world drowning in information, turning enterprise content into intelligent conversation is what transforms AI from a curiosity or toy into a true source of competitive advantage.
