Recently, at an executive conference in Europe, what started as a typical AI discussion quickly turned into something much more eye-opening. Instead of the usual vendor pitches, we dove into a real conversation about AI’s role in document management. Three key themes emerged that show just how much businesses are shifting in their thinking.
First, AI is moving from basic automation to intelligent automation. It’s not just organising files, but reading contracts, flagging risks, and predicting future trends. Second, AI is becoming smarter and more self-sufficient, learning from the documents it processes and improving over time. Lastly, AI is enhancing collaboration, helping teams work together in real time by suggesting edits and tracking changes.
What stood out most was how quickly these leaders saw AI as a true game-changer, not just a tool for efficiency. The future of document management is here, and AI is at the heart of it.
The battle of the models: Proprietary vs generic LLMs
The first major debate focused on the choice between proprietary models and generic Large Language Models (LLMs), like those from OpenAI and Anthropic. While generic LLMs excel at basic tasks, such as summarising meetings, processing mortgage applications or handling customer service interactions, organisations are increasingly questioning whether these solutions can handle their specific needs.
While the rise and success of companies in this space has paved the way for a gold standard of AI solutions, many organisations are still unclear on what is best for them. And, as more start to explore technologies like agentic AI solutions, the business discussions on “what is the best model for us” only continues to intensify.
Beyond traditional analytics: AI’s expanded role
On top of this, perceptions of AI have changed dramatically over the past year. In document management, AI now transcends the traditional analytics and machine learning applications built on enterprise data warehouses. Pleasingly, there is a consensus that simply relegating AI oversight to data scientists risks creating yet another siloed data repository, whether called a lakehouse or otherwise. The real challenge here for many industries lies in preserving data context and maintaining connections to original sources.
The rise of real-time agentic systems
In today’s microservices-driven world, real-time capabilities have become essential for enterprise-wide generative AI. We are now witnessing the rise of agentic AI solutions, increasingly adopted to support enterprise applications.
Operationally, rather than adding another layer to existing systems, agentic AI must be integrated seamlessly with functional microservices. Ideally, agentic components should operate transparently within services rather than as separate entities requiring further integration. The reason being that they excel by enabling micro-agents to communicate with one another, resulting in more refined and precise models.
Built on data rather than rigid rules, agentic systems also mitigate the risk of hallucinations – often a concern for businesses working on AI implementations. The accuracy of agentic AI stems from its ability to process and learn from millions of decisions, creating a robust foundation for enterprise applications. This precision allows workers to focus their attention where it matters most, stepping in only for the 2% of cases where the system flags potential inconsistencies or uncertainties. This significant reduction in manual oversight transforms how companies leverage human expertise.
So, what is the modern approach to document management?
Traditional document management systems were built for compliance, focusing on core attribute extraction and document storage. Most IT departments are focused on operational data and access formats, meaning an overlooked opportunity lies in existing document management systems.
Organisations often sit on decades of archived materials, which are a potential goldmine for AI training data. The revolutionary aspect is that every piece of historical data can now be activated and utilised. Businesses can quite literally evolve their archives to become dynamic, knowledge resources.
Today’s AI-powered solutions, like Encore, are revolutionising this approach. These systems can reprocess historical documents, including scanned materials and PDFs, and use generative AI to extract comprehensive information. While MongoDB can store processed data in our modern document databases, these solutions also allow organisations to leverage both RAG and agentic systems for optimal results. In short, transforming static archives into dynamic, actionable data stores is key to achieving a truly modern approach to documentation in 2025.
A new era of document intelligence
The evolution alone over the last year from basic RAG architectures to today’s sophisticated environment marks a significant advancement in itself. We’re seeing a dual approach emerge, with process-driven, generative AI handling data services on one side, paired with interactive generative AI that enhance user experiences. This combination of RAG and agentic systems creates a powerful framework where every piece of historical data can be lifted into active use, creating new value from previously dormant information.
The success of this requires continuous model training, fine-tuning and optimisation. However, the businesses and industries I frequently speak with would jump at the chance to change what was once a passive document archive into an active, valuable resource for both model training and practical applications. This shift represents not just a technological advancement, but a fundamental change in how organisations can work.
Many of my counterparts talk about 2025 as the year for preparation. If that is the case, we could very well soon see organisations leveraging their historical data for future innovation become the norm of digital document management in 2026 and beyond.
