An alternative KM strategy for law firms – Atlas by Clearpeople

Most law firms already hold their most valuable knowledge inside their document management system (DMS). For many, that system is iManage or NetDocuments. Precedents, playbooks, checklists, clause banks, client guidance, and approved templates all sit inside the DMS.

The real strategic question is not where knowledge lives. It is whether your firm can consistently find, trust, reuse, and scale that knowledge for AI and firm collaboration.

If lawyers still ask colleagues before searching the system, if knowledge is duplicated, or if AI pilots feel risky because of content inconsistency, then your iManage knowledge is not yet fully unlocked.

This article explores three strategic approaches:

1. Managing KM entirely within your DMS
2. Keeping your DMS as the system of record while introducing a knowledge layer
3. Elevating knowledge into a dedicated knowledge layer

Why DMS based knowledge is both an asset and a constraint

The DMS is often the most governed system in the firm. It has security, ethical walls, auditability, and strong document version control. It is also where lawyers naturally store work product. That makes it a logical place to anchor KM. The problem is that a DMS is optimized for document management, not necessarily for knowledge reuse at scale.

However, as the iManage Knowledge Work Maturity Model highlights, advancing knowledge maturity requires alignment across people, process, technology, and culture. A DMS alone does not automatically deliver reusable, contextualized, AI-ready knowledge.

Approach 1: Managing knowledge inside iManage (DMS centric KM model)

Managing knowledge entirely within your DMS, such as iManage or NetDocuments, is a rational and often conservative strategy. It builds on existing infrastructure, leverages established governance controls and avoids introducing another platform into an already complex technology landscape.

Firms adopting this approach position the DMS as both the authoritative system of record for KM content and the primary user experience for finding, validating, and reusing that knowledge, effectively relying on the document management platform to serve as the foundation for knowledge lifecycle management.

This approach works best where:

• Metadata discipline is already strong
• KM collections are well defined, relatively stable and limited in scope
• You have dedicated resources to manage the DMS
• Search needs are largely document centric rather than insight driven
•  AI ambitions are cautious or limited

Pros

• Governance alignment with content remaining in the same controlled environment
• Lower technology change
• KM teams retain direct stewardship of content

Cons

• Implementing a tool such as Insight+ demands substantial configuration effort, robust metadata governance design, and careful change management
• Implementation services would be required, which materially increases both cost and delivery risk
• Designed for document control, not contextual knowledge delivery aligned to specific tasks or client needs
• Duplication increases when authoritative versions are unclear, reducing reuse and consistency
• Weak lifecycle discipline allows outdated or superseded materials to remain accessible
• The rapid evolution of AI is reshaping how lawyers search for, access, and work with knowledge. As a result, large investments in traditional, search‑centric infrastructure risk becoming increasingly redundant over time.

Managing KM within your DMS is a defensible and often necessary starting point. It strengthens governance and centralizes control. The strategic question for leadership is whether that foundation alone is sufficient to support the firm’s ambitions around productivity, cross-practice collaboration, and AI enabled knowledge discovery at scale.

Approach 2: Unlocking iManage knowledge through a structured knowledge layer

This approach does not replace iManage. Instead, it introduces structure by separating reusable knowledge from matter content.

Client work remains in iManage as the secure system of record. However, curated KM content is copied or promoted into a dedicated iManage workspace designed specifically for reusable knowhow. Atlas then layers automated tagging, contextualization, and enhanced search on top of that curated environment.

In architectural terms, iManage manages documents. The knowledge layer manages meaning, context, authority, and discoverability.

What this means in practice:

• Matter documents stay within their original iManage workspaces
• Approved (and redacted) precedents and knowhow are promoted into a separate KM workspace in iManage
• Atlas automatically enriches, tags, and surfaces that KM workspace content in Microsoft 365
• Lawyers access authoritative knowledge in context, without navigating folder structures
• Simplified and unified search across Microsoft 365 and iManage
• Clear marking of authoritative or gold standard content
• Simpler and automated knowledge lifecycle management controls
• Knowledge delivered directly within Teams, Outlook, and Microsoft 365 workspaces

This reflects the broader shift from traditional knowledge management to knowledge productivity, where knowledge is contextual, automated, and embedded into daily workflows rather than accessed as a static library.

Pros

• Clear separation between live matter work and reusable knowledge.
• Strong governance alignment with existing DMS controls.
• Improved findability and contextual delivery that are designed around lawyer tasks and questions, not matter folders.
• Automated tagging and enrichment shift the KM team away from repetitive classification tasks and towards governance and quality oversight.
• Knowledge appears within the flow of work inside Microsoft 365 and Teams, reducing friction and improving adoption.
• Structured foundation for AI grounded in curated KM collections.

Cons

• Content rationalization needed: Legacy duplication and outdated materials must be addressed to avoid amplifying noise through improved search and AI surfacing.
• Requires governance clarity: Authority, ownership, lifecycle standards, and review cadence must be clearly defined before scaling visibility.

This approach works best where:

• There is recognition that reusable knowledge must be separated from matter noise
• Lawyers need improved findability and contextual surfacing without abandoning iManage as the primary source.
• The firm wants stronger AI readiness
• The firm is ready to introduce knowledge automation

In summary, introducing a structured knowledge layer around iManage is an incremental maturity step. It retains iManage as the authoritative store while introducing structure and automation through Atlas.

Approach 3: Moving reusable knowledge into Atlas (dedicated knowledge layer)

This approach makes a deliberate distinction between document management and knowledge management.

It is the most strategic model, with iManage remaining the system of record for client and matter documents. Reusable KM content is migrated into Atlas as the primary knowledge layer, which then acts as the authoritative environment for precedents, playbooks, guidance, and curated expertise. Knowledge is structured, contextualized, and delivered directly within Microsoft 365 workflows.

This represents a shift from thinking in terms of repositories to thinking in terms of knowledge infrastructure.

What this looks like in practice:

• Approved KM content is extracted from iManage
• Content is rationalized, deduplicated, and lifecycle governed
• Automated tagging and enrichment are designed for contextual delivery
• Gold standard content is clearly marked
• Knowledge is surfaced in Teams, SharePoint, and Microsoft 365 workspaces
• AI tools interact primarily with curated Atlas knowledge collections

This aligns directly with knowledge productivity principles, where contextual, automated, and workflow-embedded knowledge replaces static libraries

Pros 

• Clear architectural separation with document control and knowledge experience are treated as distinct capabilities.
• Improved knowledge clarity, whereby Atlas becomes the single authoritative layer for reusable knowhow.
• Legacy duplication in matter folders no longer pollutes the KM experience.
• Stronger AI foundation as AI interacts with structured, lifecycle governed content.
• Scalable AI governance model with lifecycle, ownership, and taxonomy management.

Cons 

• Requires rationalization of knowledge content.
• Cultural change required whereby lawyers must understand that reusable knowledge no longer “lives” in matter folders.

This approach works best where:

• The firm views knowledge as enterprise infrastructure
• There is significant duplication or entropy within matter workspaces
• AI is a strategic priority requiring authoritative, structured knowledge collections
• Cross-border or cross-practice collaboration requires consistent taxonomy and lifecycle discipline
• Leadership is willing to redesign the KM operating model

Approach 3 represents a strategic shift. It treats knowledge as core enterprise infrastructure rather than a curated subset of documents. Client work remains securely governed in iManage, while reusable knowledge is moved into Atlas, where it is structured, enriched, contextualized, and made AI‑ready.

For firms serious about AI at scale, cross-border collaboration, and measurable knowledge ROI, this model provides the cleanest long-term architecture.

Final perspective: from iManage repository to knowledge layer 

Moving from a DMS-centric model to a knowledge layer is an operating model change. The aim is not to move everything out of iManage, but to identify and elevate reusable, high-value know-how so it is easier to find, trust, reuse, and use safely with AI.

Successful firms keep iManage as the secure system of record and add a knowledge layer to operationalize a defined subset of KM content. This preserves governance, avoids duplicating controls, and focuses effort where it delivers measurable impact.

The shift from DMS-centric KM to a knowledge layer is about extracting more value. By retaining iManage as the system of record and operationalizing knowledge through a structured layer, firms improve findability, increase reuse, reduce manual KM effort, and enable safer AI adoption. This is the difference between knowledge being stored and knowledge being usable.

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