Key Takeaways
- Knowledge management covers three knowledge types (explicit, tacit and implicit), and each requires a different capture method.
- The knowledge management process runs through six stages, from capture to review and retirement, and many programs skip the final stage.
- A knowledge management system combines repositories, search, collaboration and governance tools, with SharePoint serving as a common enterprise hub.
- Copilot and AI agents ground answers in existing enterprise content and inherit its permissions, so content quality and access hygiene directly affect AI accuracy.
- Effective knowledge management strategies assign content owners, set review cycles and track metrics such as findability and content freshness.
Every organization runs on knowledge stored in documents, sites, chats and the experience of its people. When that knowledge is hard to find, outdated or overshared, productivity drops and risk rises. Generative AI raises the stakes because AI assistants answer from whatever content they can reach. This guide explains knowledge management, its types, process, systems and tools, and how to prepare knowledge for AI.
What Is Knowledge Management?
Knowledge management (KM) is the process of identifying, capturing, organizing, storing, sharing and governing an organization's knowledge so the right people can access accurate information at the right time. Knowledge includes documented assets such as policies, procedures and project files, along with the expertise and know-how employees carry. KM combines people, processes and technology to turn that knowledge into consistent decisions and repeatable work.
KM is a recognized management discipline. ISO 30401, published by the International Organization for Standardization, sets requirements for establishing, implementing, maintaining and improving a knowledge management system within an organization.
Why Knowledge Management Matters More In The AI Era
The readiness gap is well documented. In Deloitte's 2020 Global Human Capital Trends survey, 75% of organizations said creating and preserving knowledge across evolving workforces was important or very important to their success, yet only 9% said they were very ready to address it.
Investment is rising to close that gap. APQC's 2025 Knowledge Management Priorities and Trends Survey Report, based on 340 global participants, found that almost two-thirds of respondents expect their organizations' KM investment to increase over the next 12 to 18 months, with new technology such as AI driving part of that spending.
AI also changes how knowledge is consumed. Enterprise AI assistants retrieve content at the moment a user asks a question, so the quality, currency and access controls of stored knowledge now shape every AI-generated answer.
Types Of Knowledge Management
Knowledge management practice classifies organizational knowledge into three types, and each type requires a different capture and sharing method. KM programs also use two delivery approaches: codification, which documents knowledge in repositories, and personalization, which connects employees with experts.
Explicit Knowledge
Explicit knowledge is documented and easy to share. Examples include policies, standard operating procedures, product specifications, contracts and training materials stored in SharePoint libraries, wikis and knowledge bases.
Tacit Knowledge
Tacit knowledge is experience-based expertise that is difficult to articulate, such as how a senior engineer diagnoses a system failure. It transfers through mentoring, job shadowing, communities of practice and recorded expert sessions.
Implicit Knowledge
Implicit knowledge is applied know-how that can be documented but often is not, such as workarounds, team conventions and undocumented process steps. Process mapping and after-action reviews convert implicit knowledge into explicit knowledge.
Table 1: Types Of Knowledge At A Glance
| Knowledge Type | Example | How To Capture It | Typical Location |
|---|---|---|---|
| Explicit | HR policy, product manual, SOP | Templates, document libraries, metadata | SharePoint, Google Drive, knowledge bases |
| Tacit | Expert troubleshooting judgment | Mentoring, recorded sessions, communities of practice | People, meeting recordings, Teams channels |
| Implicit | Team workarounds, unwritten process steps | Process mapping, after-action reviews | Chats, email, personal notes |
The Knowledge Management Process In Six Stages
The knowledge management process is the lifecycle that moves knowledge from creation to retirement. Many published models stop at sharing and use. A governed lifecycle adds a final review stage so outdated content does not stay in circulation.
Stage 1: Capture And Create
Teams create new knowledge and capture existing knowledge from documents, meetings, chats and subject matter experts. Standard templates and project close-out reviews reduce knowledge loss.
Stage 2: Organize, Classify And Tag
Content receives metadata, taxonomy terms and sensitivity labels. Data classification determines both how knowledge is found and who can see it.
Stage 3: Store And Secure
Knowledge is stored in designated repositories with defined owners, permissions and backup coverage. Clear storage rules prevent duplicate copies across file shares, sites and personal drives.
Stage 4: Share And Find
Search, navigation, intranet pages and AI assistants deliver knowledge to users. Findability depends on consistent metadata, clean permissions and one authoritative version of each asset.
Stage 5: Use And Reuse
Employees apply knowledge to decisions and tasks and reuse proven assets instead of recreating them. Usage analytics show which content delivers value and which goes unused.
Stage 6: Review, Retain And Retire
Owners review content on a set cycle, update what is current and archive or dispose of redundant, obsolete or trivial (ROT) content. A data retention policy and information lifecycle management rules define how long each knowledge category is kept.
What Is Knowledge Transfer In Management?
Knowledge transfer in management is the structured process of moving knowledge from one person, team or system to another so it remains available when roles change. It is a core KM activity during onboarding, retirements, reorganizations and project handoffs. Common methods include:
- Mentoring, job shadowing and paired work for tacit knowledge.
- Documented runbooks, process maps and handover checklists.
- Communities of practice that connect employees with subject matter experts.
- Recorded meetings, transcripts and AI-generated summaries stored in governed locations.
Measure knowledge transfer with time to proficiency for new hires, the share of critical processes with documented owners and the volume of repeat questions sent to experts.
Knowledge Management Vs. Information Management
Knowledge management vs. information management is a distinction of purpose. Information management (IM) controls how information is collected, stored, secured and retrieved. Knowledge management focuses on how people apply information and experience to make decisions. Records management and document management are related disciplines with narrower scopes.
Table 2: Knowledge Management Vs. Information, Records And Document Management
| Discipline | Primary Focus | Core Output | Relevance To AI |
|---|---|---|---|
| Knowledge management | Applying knowledge and expertise | Reusable knowledge and expert networks | Determines the quality of AI grounding content |
| Information management | Lifecycle of information assets | Organized, secured, retrievable information | Supplies the metadata and access controls AI relies on |
| Records management | Evidence of business activity | Retention schedules and defensible disposal | Limits outdated records AI can surface |
| Document management | Individual files and versions | Version control and approval workflows | Ensures AI reads the authoritative version |
What Is A Knowledge Management System?
A knowledge management system (KMS) is the combination of technology and processes an organization uses to capture, store, find and share knowledge. Most enterprises run a distributed KMS across several platforms rather than a single application.
What Are Knowledge Management Tools?
Knowledge management tools are the software components that make up a KMS. They fall into six categories:
- Repositories and document management: SharePoint, OneDrive and Google Drive.
- Knowledge bases and wikis: internal help centers, team wikis and Salesforce Knowledge.
- Intranets and portals: SharePoint communication sites and employee portals.
- Collaboration platforms: Microsoft Teams, Google Chat and similar tools where knowledge is created in conversation.
- Enterprise search and AI assistants: Microsoft Search, Microsoft 365 Copilot, Gemini Enterprise and Agentforce.
- Governance and analytics tools: classification, permissions management, retention, usage analytics and backup.
Knowledge Management In SharePoint
SharePoint is a common enterprise knowledge hub because it stores documents, hosts intranet sites and serves as a primary content source for Microsoft 365 Copilot. Knowledge management in SharePoint depends on site ownership, consistent content types and metadata, sensitivity labels, version limits and controlled sharing. Without these controls, environments accumulate duplicate sites, broken permission inheritance and organizationwide sharing links that make knowledge harder to trust.
AI Knowledge Management For Copilot And AI Agents
AI knowledge management is the practice of preparing, governing and maintaining enterprise knowledge so AI systems can retrieve and use it accurately and securely. It also covers AI-assisted KM tasks such as auto-tagging, summarization and expert identification.
Knowledge Management For Copilot
Microsoft 365 Copilot grounds responses in content the requesting user already has permission to access. Copilot does not correct oversharing or outdated content; it surfaces both faster. Knowledge management for Copilot means confirming authoritative sources, removing ROT content, applying sensitivity labels and right-sizing permissions as part of AI readiness.
Knowledge Management For AI Agents
AI agents built in tools such as Copilot Studio, Agentforce or Gemini Enterprise use defined knowledge sources to answer questions and take actions. Each agent needs a documented knowledge scope, an owner, least-privilege access and a review date. AI agent management and shadow AI controls keep unapproved agents from grounding on sensitive repositories.
Retrieval-Augmented Generation And Content Quality
Retrieval-augmented generation (RAG) is the technique AI systems use to retrieve relevant documents and add them to a prompt before generating an answer. RAG output depends on retrieval inputs. Duplicate versions, outdated policies and conflicting drafts produce answers that sound confident but are wrong, which makes content currency a measurable KM control.
How To Build A Knowledge Management Strategy
A knowledge management strategy defines the business outcomes KM supports, the knowledge that matters most and the governance model that keeps it accurate. Use these seven steps:
- Define outcomes such as faster onboarding, fewer repeat support tickets or higher AI answer accuracy.
- Inventory knowledge across Microsoft 365, Google Workspace, Salesforce and file shares through data discovery.
- Identify critical knowledge and the experts and processes that depend on it.
- Assign owners and decision rights for each repository and content type.
- Set classification, permission and retention rules, and enforce them through automation.
- Select and integrate KM tools, including the AI assistants that will consume the knowledge.
- Measure, review and improve on a fixed cadence, and report results to leadership.
Knowledge Management Best Practices: How To Improve Knowledge Management
To improve knowledge management, focus on ownership, currency and access. These knowledge management best practices apply across platforms:
- Name an accountable owner for every site, library and knowledge base.
- Set review dates by content type and flag content past its review date.
- Maintain one authoritative version of each policy and procedure, and archive superseded versions.
- Apply sensitivity labels and remove organizationwide or anonymous sharing links from sensitive content, supported by identity and access management controls.
- Retire ROT content on a schedule to reduce storage costs and AI grounding errors.
- Back up knowledge repositories so critical content can be restored after deletion, corruption or ransomware.
Knowledge Management Metrics
Table 3: Knowledge Management Metrics That Track AI Readiness
| Metric | What It Measures | Why It Matters For AI |
|---|---|---|
| Findability rate | Share of searches that end in a successful result | Indicates whether AI can locate the right source |
| Content freshness | Share of content reviewed within its cycle | Reduces outdated answers |
| Ownership coverage | Share of sites and libraries with an active owner | Ensures someone is accountable for accuracy |
| Overshared content rate | Share of sensitive items with broad access | Limits sensitive knowledge AI can surface |
| Reuse and adoption | Views, reuse and Copilot usage of key assets | Shows which knowledge delivers value |
How AvePoint Helps Organizations Govern Knowledge For AI
Knowledge management determines whether enterprise AI produces trusted answers. Governed knowledge is current, classified, correctly permissioned and recoverable, and it must stay that way across every cloud where knowledge lives.
AvePoint, the AI Trust Company, has spent 25 years helping organizations manage, secure and govern content across Microsoft 365 and SharePoint. More than 28,000 organizations and 6,000 channel partners rely on AvePoint across Microsoft, Google, Salesforce and other leading cloud environments. The AvePoint Confidence Platform applies three capabilities to enterprise knowledge:
- Security: See and close exposure from the data layer to the agent layer with data security posture management that identifies overshared knowledge before Copilot and AI agents surface it.
- Governance: Turn policy into evidence auditors and boards accept with AvePoint Opus for AI-powered classification, retention and defensible disposal, AvePoint tyGraph for knowledge usage and Copilot adoption analytics, and agentic AI governance for agent inventory and oversight.
- Resilience: Recover any asset (data, infrastructure or agent) when it matters with AvePoint Cloud Backup for SharePoint, Teams, OneDrive, Google Workspace and Salesforce knowledge repositories.
The result is enterprise knowledge AI can use safely across your entire AI estate, so innovation scales without scaling risk. Explore the AvePoint Confidence Platform to deploy AI with confidence.
Turn Your Knowledge Into A Trusted Foundation For AI
Copilot and AI agents are only as reliable as the knowledge they read. AvePoint AI Readiness helps you clean up redundant and outdated content, find and fix overshared knowledge, and track adoption across Microsoft Copilot and Google Gemini, so every AI answer starts from current, correctly permissioned content.
Frequently Asked Questions
Clara Hinchcliffe is a Product Marketing Manager at AvePoint, working on go-to-market strategy for AvePoint’s data security and information lifecycle solutions. With a background in market research, Clara brings a data-driven mindset to product marketing, spearheading initiatives like customer focus groups to ensure product-market fit. In her spare time, Clara enjoys traveling, hiking, and discovering new live music venues.