AI Is Accelerating – Your DMS Will Determine Whether You Keep Pace

By Neil Araujo, CEO, iManage.

In a relatively short span of time, it’s become increasingly clear that AI will have a profound impact on the quality, effectiveness, and efficiency of legal work. What has become equally evident is that the effectiveness of the large language models (LLMs) that power AI is significantly higher when you combine them with relevant data and the appropriate context.

The good news here for most law firms and legal departments is they’ve had discipline around managing their data going back decades, often with a document management system (DMS) playing a central role. That long-established piece of information infrastructure – and the organizational processes that surround it – are bearing new fruit in the AI era.

Better data quality translates into better AI outputs

While a DMS provides a foundational layer for AI efforts, it’s not just about capturing data, but data quality. Lawyers need to be able to identify their best work and capture the signals that go beyond the document itself. A good LLM can pick up on those signals to create better outputs.

Take jurisdiction, for example. It’s essential to know which country or region’s law a document is relying on, because a clause valid in one region may not be valid in another. Typically, jurisdiction wasn’t captured in any structured way – instead, it was inferred from language buried in the document.

If you build custom models that are trained specifically for that one task – to identify the jurisdiction – a lawyer can then say, “Find me all share purchase agreements where the governing law is England and Wales,” and you can ask a subsequent question like: “What was the termination clause for agreements that were generated in that particular jurisdiction?”

In this way, the DMS goes from capturing data to capturing data as well as signals that inform.

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Guardrails and governance safely unlock agentic workflows

It’s worth stating here that guardrails matter immensely: there needs to be rock-solid information governance around the data in any DMS controlling what data can be accessed, where it is processed, and how long it lives before it is obsolete. And in the current AI era, when the “individual” who needs access to data might be an AI agent, having structured, governed data is critical – otherwise you risk chaos.

Consider a use case like search, where an AI agent is tasked with finding a particular file or set of files.

You’d give the agent some broad instructions, and it would search through everything available and say, “OK, I found twenty of these files, but that might be too overwhelming for you to process. Let me try to refine it further and get to just the one or two documents that most represent what you’re looking for.”

An agentic search layer will take natural language queries and use an LLM to create the search. It will also look at how the search is performing – is it giving back results that match the original intent of the search? If not, it will tweak the search query accordingly.

From there, it takes the results set, examines it, and provides a summary in addition to the underlying documents. For the end user, that means that rather than seeing a list of dozens of files that you have to scroll through, you get a much smaller set back – and as a result, you quickly get an answer to the question that you were asking.

Alternately, think about someone looking for a clause, or looking for an exception to a particular clause that is typically in every single agreement. A lawyer can ask those types of questions and receive answers within seconds.

Or maybe you create an agent to look at all of the leases you have for a particular landlord. You can have the agent tabulate who the tenants are, tabulate what the termination date is for each tenant, and generate an email for anyone who’s terminating in the next month. And through new open source AI standards like model context protocol (MCP), all that automation is possible without a single line of code.

As powerful as these use cases are, none of them are possible without that context provided by the DMS.

Strong information management still wins in the AI era

When we cofounded iManage 30 years ago, there was no way we could have predicted how technology would evolve. However, the one thing that we have always said is that good information management is a cornerstone for whatever might come next – and that has certainly held true. In fact, it’s actually been magnified in the AI era.

Today, the iManage platform captures documents, email, and knowledge in a governed core that preserves and enhances context, enforces permissions, and connects with AI models on your terms, so every AI interaction starts from the right foundation.

The way you keep pace with AI and new developments like agentic workflows is by focusing on these fundamentals and ensuring the right foundations are in place. Ultimately, that’s going to determine the speed at which your organization can move, at a time when the pace is only accelerating.

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To download the iManage Knowledge Work Benchmark Report 2026 see here.

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[ This is a sponsored thought leadership article by iManage for Artificial Lawyer. ]