Is document management the real hero of the AI boom?

This is a guest blogpost by John Bates, chief executive officer of SER Group.

If you hadn’t noticed, document management (DM) is having a moment. Gartner had previously phased out the term in favour of new concepts like ECM or Content Services. However, recognising the persistent centrality of documents in business operations and mindsets, it has reintroduced the term, marking a notable shift in perspective.

Why? Because it’s becoming increasingly clear that to really get anywhere with AI, success doesn’t start with model selection, but with the right way of working with your dataset. In fact, the most effective applications — particularly in knowledge-intensive environments —succeed largely because of their ability to integrate and leverage existing documents, mapping and referencing the institutional wisdom they contain, even as they process the constant influx of new information flowing into the business.

For example, retrieval-augmented generation (RAG), which enhances large language models with external data, is emerging as a central strategy in generative AI-based search, particularly for high-stakes, real-world applications. Major cloud providers are positioning it as essential for enhancing generative AI with live data, research, and insights. Gartner forecasts that within the next three years, 80% of generative AI business applications will embed RAG as a foundational element of their architecture.

But effective RAG — like other AI optimisation techniques such as pruning and quantisation — often depends on the foundational capabilities of a robust document management system to ensure that documents, and the knowledge they contain, are accessible, well-organised, and trustworthy.

After all, no large language model (LLM) has been trained on an individual organisation’s internal documents. That means it can’t provide domain-specific answers out of the box. With RAG, however, the model can not only retrieve relevant content for a given query, but also return precise, source-based citations from millions of internal documents—something you simply can’t expect from generic, ChatGPT-level systems.

Set the table before you serve the AI

That’s because RAG only delivers value when it combines the generative power of an LLM with real, trusted data sources. For anything beyond generic or proof-of-concept use cases, that means drawing directly from your enterprise documents.

In the context of DM, RAG retrieves relevant documents or snippets from an organisation’s own knowledge base and feeds that context into the model’s response. The result: dramatically improved accuracy, fewer hallucinations, and outputs grounded in the actual reality of your business—not vague generalities from the public internet.

But RAG is only as good as the content it pulls from. The more structured, contextualised, and accessible your enterprise documents are, the more effective and reliable your RAG implementation becomes.

That intelligence depends on the health of your underlying content. This is where a mature DM system proves essential, organising data with structured formats, metadata tagging, hierarchical context, permission control, and more. In short, it sets the table so the AI can serve something truly useful.

The difference becomes stark when your documents are inconsistent, unlabeled, or siloed across fragmented systems. In such cases, the retrieval layer falters — and if your search infrastructure is shallow or brittle, the LLM isn’t grounding its output, it’s guessing. That’s why true AI success hinges on end-to-end document intelligence: the ability to capture, understand, manage, and integrate content across its full lifecycle.

It also explains the growing shift toward composable AI and intelligent agents. Forward-thinking organisations are building tailored AI pipelines for specific use cases—combining multiple specialised AI algorithms with RAG, all anchored by a strong document management core—to deliver true ‘document intelligence’.

In this new paradigm, modern DM is no longer a back-office function. It’s a strategic enabler. It powers what we call Super Human Search, where a business user can ask, “What are the payment terms on our top five vendor contracts from last year?” or “Does this contract conflict with any other contract we’ve signed?” and instantly receive an accurate, contextualised answer.

Analysts agree. Gartner recently announced that successful generative AI (GenAI) deployments are “best empowered” by a good Document Management strategy as successful deployments of enterprise GenAI depend on robust enterprise document management. In parallel, management consultants Bain have also stated that every successful AI deployment presented at Nvidia’s 2025 AI developer conference was there because they all rested on clean, connected, and accessible business information.

The customers I speak to echo the same sentiment: the document is definitely having a ‘moment’, if not a whole AI-driven renaissance.