The iManage context fabric™: The foundation AI needs to deliver better decisions

At ILTACON this year, you'll hear one word repeatedly:

Context.

That raises an important question:

If everyone is talking about context, why should legal and knowledge professionals care?

The answer is simple.

As AI becomes increasingly powerful and accessible, access to AI is no longer the competitive advantage. The quality of the context behind it is.

The organizations that achieve the greatest value from AI won't necessarily be those with the most advanced models. They'll be the organizations that provide those models with the most accurate, relevant, and trusted understanding of their business.

In other words: Getting value out of AI isn't a model challenge. It's a context challenge.

Search retrieves information. Context creates understanding.

For decades, legal technology has focused on helping professionals find information. That was an important challenge. But today, AI is forcing us to confront a different one: Finding information is not the same as understanding it.

Consider these questions:

"Does this expert report support or undermine our current theory of the case?"

Or

"Does this deposition transcript contradict anything we've confirmed?"

A traditional search engine might return hundreds of documents containing similar language. But the real question isn't whether a document contains matching words.

The real question is:

• Is it the right precedent?

• Is it the latest version?

• Was it successful?

• Is the matter comparable?

• Does it align with firm or legal guidance?

• Would the lawyers who handled it use it again today?

Those answers require context.

Search retrieves. Context interprets and shapes data for AI.

Why context matters now

The first wave of AI was focused on generating content. The next wave will be judged on producing trustworthy outcomes. That requires more than connecting a large language model to a document repository as there is only so much context contained within the four corners of a document. To understand what is important, AI must combine document content with the decisions, actions and related matter, client and financial data that provide the broader context.

AI needs to understand:

• relationships between matters and clients

• expertise across the organization

• approved knowledge and guidance

• matter or engagement history and evolution

• time billed and profitability from financial systems

• governance and permissions

• provenance and version history

Without context, AI must infer meaning. With context, AI can reason from understanding. That difference has profound implications for both performance and economics.

Better precision

The first wave of AI was focused on generating content. The next wave will be judged on producing trustworthy outcomes. That requires more than connecting a large language model to a document repository as there is only so much context contained within the four corners of a document. To understand what is important, AI must combine document content with the decisions, actions and related matter, client and financial data that provide the broader context.

Lower AI costs

Context improves efficiency as well as accuracy. When organizations provide AI with enriched, relevant context up front, users spend less time prompting and models spend less effort processing irrelevant information and unnecessary content. Less noise means fewer tokens, more efficient reasoning, and lower operating costs.

Gartner puts a number on this: Organizations that prioritize semantics in AI-ready data can increase agentic AI accuracy by up to 80 percent and reduce AI operating costs by up to 60 percent.

Greater trust

Perhaps most importantly, context strengthens confidence. Legal and knowledge professionals need to understand where answers came from, whether they can be trusted, and how they were derived. Context connects answers back to evidence, provenance, permissions, and governance.

What intelligent context makes possible

The value of context becomes apparent when you move beyond theory and into daily work. Imagine joining an active matter and immediately understanding the key parties, major decisions, strategic shifts, and current status without spending hours reconstructing history from emails and documents.

Imagine finding not just a similar document, but the most relevant precedent based on matter type, industry, transaction characteristics, outcome, and organization expertise. Imagine identifying exactly which lawyers have handled comparable situations before and understanding the approaches that led to successful outcomes.

Imagine discovering that a newly surfaced fact changes the assumptions underlying a draft motion, witness preparation strategy, or settlement position before that risk becomes a problem. Imagine every AI-generated recommendation automatically grounded in approved guidance, trusted knowledge, and governed content.

These are not search problems. They are context problems. And solving them changes the quality of decisions professionals can make.

Why iManage is uniquely positioned

This is where iManage has a distinct advantage. For decades, iManage has sat at the center of knowledge work for legal and professional services organizations. The platform already understands:

• matters

• clients

• people

• knowledge

• expertise

• communications

• governance

That creates something far more valuable than a collection of documents.

That understanding runs deep. The next-generation iManage platform attaches extensive structured context to every result: matter, client, people, outcome, knowledge, and governance. Not a handful of keyword tags bolted onto a search index. This is the practical gap between search and context: a search overlay can tell you a document exists; a governed context layer can tell you what it is, whose work it represents, why it matters, whether it can be trusted, and who is allowed to see it.

It creates an intelligent, governed context layer.

iManage calls this the context fabric™: a governed layer — every relationship and inference it surfaces inherits the same permissions, ethical walls, and retention rules already enforced across the iManage platform, rather than a separate governance model bolted on top — that understands and reasons over content, communications, engagements, relationships, and real-time activity across the organization, continuously enriched by what people and agents are doing right now.

That layer is built on the same three components Gartner defines as the foundation of a real context layer for AI agents: semantics, operational state, and provenance, engineered into the platform from the start, not bolted on after the fact.

A foundation that can power search, AI assistants, agents, knowledge discovery, and future AI experiences with a consistent understanding of how work is performed across the organization.

The context layer beneath every AI or agent action

New models, AI assistants, and agents are arriving constantly, and most organizations will use several of them. This is why context matters more, not less, as agentic AI matures: assistants proliferate, but context should consolidate. Whichever AI an organization chooses, AI impact is driven by the context and governance underneath it.

While others may be trying to assemble context when a question is asked, iManage has been helping organizations build, enrich, and govern context throughout the lifecycle of work.

The iManage platform is already providing a governed, enriched context layer to agents today, via the iManage MCP Server, so governed context and content connects to any AI whether it’s an AI-native application or a foundational model.

And that difference matters. Because the most important question organizations will ask of every AI solution is no longer: “What can it do?” It's: “Why should I trust it?” The answer increasingly comes down to context.

The future belongs to organizations that invest in context

AI models will continue to improve. New assistants will emerge. New agents will automate more work. But every one of those innovations depends on the same foundation.

Context.

The organizations that create a rich, governed understanding of their matters, clients, engagements, expertise, and institutional knowledge will generate more accurate AI outputs, lower operating costs, improve relevance, and make better decisions.

The next competitive advantage in AI won't come from having access to another model. It will come from providing that model with better context. And that's exactly why the intelligent context layer is becoming one of the most strategic assets for modern legal and knowledge teams.

At ILTACON, we'll show what the iManage context fabricTM looks like. Stop by the iManage booth #431 to see it in action.