How to build a consistent, scalable, high performing KM function

As organizations invest in Knowledge Management (KM) and enterprise AI, many are expanding their KM teams by hiring Knowledge Lawyers, knowledge and AI specialists. Yet while significant effort is often spent recruiting experienced professionals, far less attention is given to how they are onboarded.

Research from the Society for Human Resource Management (SHRM) shows that structured onboarding improves employee performance, engagement, and retention. For Knowledge Management teams, effective onboarding has an additional benefit: it establishes consistent ways of creating, governing, and sharing knowledge from the very beginning.

A well-designed onboarding program does much more than help people settle into a new role. It creates shared standards, accelerates productivity, improves collaboration, and ensures every member of the KM team contributes to a consistent, high quality, AI ready knowledge ecosystem.  

While this blog is written primarily for legal knowledge management professionals and Knowledge Lawyers, many of the principles and best practices are equally applicable to KM teams in other knowledge-intensive industries. 

Why legal KM teams face unique onboarding challenges

Legal knowledge work differs significantly from many other professions.

Knowledge Lawyers often balance multiple responsibilities including:

• Developing precedents, work product and guidance materials
• Monitoring legislation and case law
• Capturing legal know how
• Supporting lawyers and practice groups
• Managing legal content
• Supporting client delivery and performing occasional client-billable work
• Training fee earners and acting as a sounding board for legal queries
• Leading innovation initiatives
• Preparing knowledge for AI

Unlike fee earners, they can work across multiple practice groups and increasingly own the quality, governance, and usability of enterprise knowledge.

As firms grow internationally, each office may also develop its own ways of working. Without structured onboarding, new Knowledge Lawyers often inherit local practices rather than consistent firm wide standards.

The 5 C's framework for KM team onboarding

One of the most widely adopted onboarding frameworks is Bauer's 4 C's of Onboarding, which focuses on:

• Compliance: Policies, governance, guidelines and handbooks.
• Clarification: Understanding roles (job descriptions), orientation and performance criteria.
• Culture: Learning how the organization works and shares knowledge.
• Connection: Building interpersonal relationships and information networks.

These provide an excellent foundation for integrating new employees into any organization.

However, growing KM functions require an additional dimension: Consistency.

As teams expand, consistency becomes essential for ensuring that knowledge is created, classified, governed, and maintained using common standards. Without it, every Knowledge Lawyer develops their own approach to metadata, taxonomy, governance, content quality, and AI readiness.

Rather than viewing onboarding as simply introducing new employees to the organization, leading KM teams increasingly see it as establishing a consistent operating model.

Best practice: Review the 5 C's with every new KM team member during their first month and revisit them during the probation period to reinforce expectations.

Knowledge transfer is more than documentation

One of the most influential ideas in Knowledge Management comes from Michael Polanyi, who observed that "we know more than we can tell."

Much of what makes an exceptional Knowledge Lawyer cannot simply be documented.

Skills such as facilitating knowledge capture, influencing partners, recognising valuable precedents, or deciding what information should become reusable organizational knowledge are developed through experience.

Similarly, Nonaka and Takeuchi's SECI Model highlights that organizational knowledge is created through continuous interaction between tacit and explicit knowledge. During onboarding, documented standards and playbooks are important, but observation, mentoring, and practical experience are equally critical for transforming knowledge into capability.

This is why effective onboarding combines documentation with mentoring, coaching, shadowing, and collaborative learning.

The goal is consistency, not conformity

One of the biggest misconceptions is that standardized onboarding limits professional judgement. It does not.

Instead, it standardizes repeatable activities while allowing experts to exercise discretion where it matters most.

Think of it as creating common operating principles rather than rigid procedures.

Every KM team member should understand:

• What good knowledge looks like
• How knowledge should be classified
• How metadata should be applied
• When content should be reviewed or archived
• Who owns knowledge assets
• How knowledge is captured
• How AI consumes knowledge
• How quality is measured

Consistency creates confidence as well as enables every team member to contribute knowledge that is easier to discover, govern, maintain, and reuse.

Build onboarding around the knowledge lifecycle

Rather than structuring onboarding around departments or technology, consider organizing it around the knowledge lifecycle.

This approach helps every new KM team understand how knowledge flows through the firm, from its creation through to governance, reuse, and continuous improvement. It also reinforces that every stage contributes to the quality of enterprise search and AI-powered experiences.

A practical onboarding framework might look like this:

Structuring onboarding around the knowledge lifecycle helps new team members understand not just what they need to do, but how their work contributes to the wider knowledge ecosystem. It also establishes consistent practices across the KM function, ensuring knowledge is captured, governed, and maintained in a way that supports both today's users and tomorrow's AI capabilities.

Best practice: Introduce each stage of the knowledge lifecycle through a combination of documented standards, practical exercises, shadowing experienced colleagues, and real-world scenarios. This helps new team members move beyond understanding the process to applying it consistently in their day-to-day work.

Create a Knowledge Management playbook

One of the most valuable investments growing KM teams can make is developing a KM Playbook.

A typical playbook includes:

• Mission
• Principles
• Standards
• Governance
• Technology
• Roles and responsibilities
• Templates
• Quality expectations
• KPIs
• AI readiness guidance

The playbook becomes the single source of truth for every new team member.

Build mentoring into onboarding

Research consistently shows that mentoring remains one of the most effective ways to transfer tacit knowledge.

Mentoring should include:

• Stakeholder management
• Facilitating knowledge capture
• Working with partners
• Influencing lawyers
• Handling resistance
• Balancing strategic and operational priorities

Standardize common KM processes

Growing teams should establish standard operating procedures for repeatable work such as:

Research on cross-functional knowledge management shows that organizations perform more effectively when teams share common standards, clear task orientation, strong communication, trusted relationships, and consistent coordination across functions. These practices improve knowledge sharing while reducing duplication and inconsistency.

Train for AI readiness from day one

Every Knowledge Lawyer now contributes directly to AI quality.

Modern onboarding should include:

• What makes knowledge AI ready
• Authoritative sources
• Structured content
• Metadata quality
• Governance
• Permissions
• Context preservation
• Content freshness

Industry research consistently shows that organizations are discovering AI success depends less on the technology itself and more on the quality, governance, and accessibility of enterprise knowledge. Organizations that invest in trusted, well-governed knowledge foundations are better positioned to realize value from Microsoft Copilot and enterprise AI initiatives.

Measure onboarding success

Many organizations simply measure whether onboarding has been completed.

Instead, measure business outcomes.

Create a culture of continuous learning

Onboarding should mark the beginning of continuous professional development rather than its conclusion.

Leading KM teams encourage ongoing learning through:

• Communities of Practice
• Peer reviews
• Lunch and learns
• Knowledge sharing sessions
• AI experimentation
• Industry conferences
• Professional certifications
• Retrospectives

This helps ensure the KM function continues to evolve alongside changing business priorities and emerging AI capabilities.

Common onboarding mistakes

Even well-established knowledge management teams can struggle to onboard new team members consistently. As teams grow, onboarding often evolves organically rather than being intentionally designed, leading to different practices, inconsistent knowledge quality, and varying user experiences.

Growing KM functions frequently encounter similar challenges:

• • • No structured onboarding plan
      Best practice: a structured 30, 60, and 90-day onboarding plan should be developed with clear learning objectives, milestones, and success measures. 
    • Allowing every Knowledge Lawyer to develop different working practices
    • Failing to document standards
      Best practice: develop a KM Playbook that documents agreed standards, templates, governance, and best practices.
    • Neglecting mentoring
      Best practice: pair every new team member with an experienced mentor and provide opportunities to observe knowledge capture sessions, stakeholder meetings, and governance reviews. 
    • Providing limited exposure to legal practice groups
    • Overlooking governance training
      Best practice: ensure every team member  understands ownership, review cycles, retention policies, permissions, and lifecycle management from day one. 
    • Not measuring onboarding success

The good news is that most onboarding challenges are avoidable with the right operating model.

How AtlasFuse supports consistent KM onboarding

As KM teams expand, consistency becomes increasingly difficult when knowledge is distributed across SharePoint sites, Microsoft Teams, document management systems, intranets, email, and business applications.

AtlasFuse provides a unified knowledge layer that brings together authoritative content, taxonomy, metadata, governance, expertise, and AI ready knowledge into a single intelligent platform.

This enables new Knowledge Lawyers to understand how knowledge is organized, discover trusted content, identify subject matter experts, and contribute using consistent governance and metadata standards from day one.

Rather than learning multiple disconnected repositories, new joiners develop a shared understanding of the firm's knowledge architecture, supporting faster onboarding and more consistent knowledge creation.

For firms investing in enterprise AI, AtlasFuse also reinforces these onboarding principles by ensuring governed, permission aware, and contextual knowledge is consistently available across Microsoft 365, enterprise search, intranets, client portals, Copilot, and AI agents.

Final thoughts

As Knowledge Management functions mature, the differentiator is no longer the expertise of individual team members but the consistency with which that expertise is applied across the organization.

A well-designed onboarding program transforms individual experience into organizational capability. It ensures every Knowledge Lawyer understands not only what to do, but how to create, manage, govern, and continuously improve knowledge in a way that aligns with the firm's strategy, governance model, and AI ambitions.

As enterprise AI becomes increasingly dependent on trusted, structured, and governed knowledge, onboarding is no longer simply an HR activity. It is a strategic investment in building a scalable, high performing Knowledge Management function that can support the future of legal services.

Academic references

• Bauer, T. N. (2010). Onboarding New Employees: Maximizing Success. SHRM Foundation. 

• Davenport, T. & Prusak, L. (1998). Working Knowledge. Support the importance of governance, knowledge quality, and organizational knowledge sharing.

• Nonaka, I. & Takeuchi, H. (1995). The Knowledge-Creating Company. Introduce the SECI model to explain how onboarding accelerates the conversion of tacit and explicit knowledge.

• Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity. Justify Communities of Practice, peer learning, and mentoring as essential components of KM onboarding.

Industry reads

• KM’S Role In The Lawyer Lifecycle - https://www.iltanet.org/blogs/adam-dedynski1/2022/02/18/kms-role-in-the-lawyer-lifecycle

• APQC. (2026). Knowledge Management Priorities and Trends Survey Report.

• KMWorld. (2026). State of KM & AI Report.

• Forrester Consulting. (2025). Unlocking Efficiency: The Inevitable Rise of Knowledge Work Automation.

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