Knowledge has always been an organization’s most strategic asset, but in the age of AI, its value and its risks have multiplied. AI tools such as Microsoft Copilot and other generative assistants now sit inside daily workflows, shaping decisions, client service, and operational performance. Yet research shows a consistent pattern: many AI initiatives underperform not because of the models, but because of the state of the knowledge feeding them.
This makes the knowledge lifecycle even more strategically important. In this blog, I’ll explore the lifecycle, compare leading academic models, explain why govern must now sit across all stages, and highlight how Atlas operationalizes this model inside Microsoft 365.
The knowledge lifecycle is the end-to-end process by which organizations create, capture, organize, share, apply, and improve knowledge. It ensures that institutional insight—whether from people, documents, systems, or experience—is transformed into accessible, trusted, and actionable assets.
While models vary, the core lifecycle stages remain consistent across decades of KM scholarship—including Wiig (1993), Meyer & Zack (1996), Bukowitz & Williams (1999), McElroy (2003), and Dalkir (2005). The most modern and comprehensive, the Knowledge Management Cycle (KMC) from Evans, Dalkir & Bidian, integrates these traditions into a holistic lifecycle.
Across all models, the essential stages include:
This structure is reflected in the knowledge management model cycle used by academic researchers and global organizations alike.
|
Model |
Key Stages |
Distinguishing Features |
|
Wiig (1993) |
Build → Hold → Pool → Use |
Early emphasis on organized, usable knowledge. |
|
Meyer & Zack (1996) |
Acquire → Refine → Store/Retrieve → Distribute → Present |
Focus on quality control through the “knowledge refinery.” |
|
Bukowitz & Williams (1999) |
Get → Use → Learn → Contribute → Assess → Build/Sustain |
Adds strategic knowledge decisions and divestment. |
|
McElroy (2003) |
Knowledge Production → Knowledge Integration |
Introduces knowledge claim formulation and validation. |
|
Dalkir (2005) |
Create/Capture → Share/Disseminate → Acquire/Apply → Update |
A practical, integrated cycle widely adopted. |
|
KMC Model (Evans, Dalkir, Bidian, 2014) |
Identify → Store → Share → Use → Learn → Improve → Create |
The most holistic and modern lifecycle; includes double-loop learning. |
These models provide the basis for understanding how knowledge flows—and how organizations can systematically improve that flow.
Knowledge emerges through research, client work, insight, conversations, and problem-solving. The KMC model notes that creation and identification often occur concurrently as individuals recognize gaps or generate new solutions.
This stage converts tacit and experiential insight into explicit, reusable knowledge. Tactics include after-action reviews, lessons learned, interviews, observational capture, and AI-assisted codification. Researchers note that tacit knowledge remains one of the hardest—and most essential—elements to preserve.
Storage must be structured, governed, and enriched with metadata. The academic literature is clear: organizing knowledge is essential for it to be valuable.
Poorly organized repositories are the root cause of AI hallucinations, duplication, and misinformation.
Sharing knowledge—across functions, teams, and systems—is critical for organizational learning. Heisig’s review of 160 KM frameworks found sharing as one of the six universal KM activities.
Knowledge only creates value when used in decisions, client matters, innovation, or daily workflows. This stage is where KM connects directly to business outcomes.
Knowledge must stay relevant. The KMC model includes learning, improving, updating, and retiring obsolete information.
This closes the feedback loop and strengthens the entire cycle.
Traditionally, governance was implicit in lifecycle models—not a formal stage. But AI has fundamentally reshaped the stakes. As one PDF notes:
“Gen AI now permeates every step of the knowledge lifecycle—capture, enrichment, retrieval, drafting, review.”
This requires governance to appear not as a phase, but as a horizontal layer applicable across all stages:
Govern (Cross-Lifecycle Layer)
Governance ensures:
Academic justification
Across the models, researchers consistently reference activities that are inherently governance-oriented:
Regulatory justification
The rising regulatory demands related to AI such as the EU AI Act, US Executive Order on Safe, Secure, Trustworthy AI and NIST AI Risk Management Framework require organizations to demonstrate:
Without governance, the knowledge lifecycle cannot support AI. With governance, it becomes the foundation for safe, explainable, high-performing AI systems.
Atlas brings the knowledge lifecycle to life inside Microsoft 365 by supporting every stage with automated structure, contextual intelligence, and governance that runs across all phases. Rather than treating knowledge management as an isolated function, Atlas embeds lifecycle practices directly into daily work, enabling organizations to capture and transform insight at scale.
Atlas enables the creation and identification of knowledge by providing guided contribution processes, structured templates, and an environment that supports clarity and consistency. Users can create new knowledge through ConneX workspace templates, rich SharePoint pages, or collaborative tools such as Teams. The Add It capability allows employees to rapidly surface items that should become part of the knowledge ecosystem, even when those items originate from unstructured or informal interactions.
Atlas captures and codifies knowledge at scale by automating metadata enrichment, classification, and tagging. Content collected from documents, chats, pages, and external sources is consistently enriched with standard metadata, allowing it to be interpreted and reused across systems. The platform applies metadata automatically and in context, which reduces manual work and improves the uniformity of captured knowledge. This approach aligns directly with academic research that highlights the challenge of capturing tacit insight and transforming it into explicit, context rich knowledge.
Atlas provides a unified structure for organizing knowledge through its intelligent knowledge fabric. Knowledge Collections allow teams to group, standardize, and govern content in ways that connect directly to how people work. The platform brings together content from Microsoft 365, email, chats, external sources, and third party systems, creating a single pane of glass for organized and trustworthy knowledge. This structure reduces duplication, eliminates outdated information, and establishes a foundation for precise AI responses. The approach is consistent with long standing KM models that emphasize the importance of organized and reliable repositories as a prerequisite for effective knowledge use.
Atlas supports the distribution of knowledge across the enterprise by presenting relevant, contextual information wherever people work. The platform powers enterprise intranets, extranets, and client portals, enabling firms to share knowledge securely with internal and external audiences. It includes targeted communications, personalized feeds, Mandatory Reads, and rich navigation models that ensure knowledge reaches the individuals who need it. The sharing capabilities are aligned with the academic consensus that dissemination is one of the core and universal activities in KM frameworks.
Atlas enables the application of knowledge directly in the flow of work. Through capabilities such as In Focus enterprise search and the AI powered Atlas Assistant, people can apply validated knowledge seamlessly. These tools allow users to retrieve information from across Microsoft 365 and beyond, supported by contextual metadata, lineage information, and authoritative sources. This integration addresses the well documented frustration that workers often struggle to locate the knowledge they require, even when repositories exist. Atlas supports decisions by ensuring the use of accurate, contextualized, and authoritative information, which is essential for safe and beneficial AI adoption.
Atlas provides structured mechanisms for maintaining knowledge accuracy and currency. Content lifecycle workflows support scheduled reviews, version history, and controlled updates. Knowledge health dashboards help teams identify outdated, unused, or low value content, enabling organizations to maintain relevance and reduce risk. This approach reflects academic commentary that stresses the need for continuous knowledge improvement, particularly in the final stages of the lifecycle, so that knowledge remains trustworthy and beneficial.
Governance is the connective layer that spans all stages of the knowledge lifecycle. Atlas embeds governance throughout the entire process by enforcing metadata standards, maintaining provenance, applying sensitivity labels, and supporting regulatory compliance obligations such as those arising from the EU AI Act, the United States Executive Order on Safe and Trustworthy AI, and the NIST AI Risk Management Framework. The platform provides transparent lineage, model boundaries, and content controls that are essential in an era where generative AI interacts with enterprise content at every step of the lifecycle. Governance is not treated as an isolated activity but as a continuous outcome that ensures trust, safety, and compliance across creation, capture, storage, sharing, use, and improvement.
Atlas operationalizes governance in a way that aligns precisely with academic research. Models such as Meyer and Zack, McElroy, and the integrated KMC model all highlight quality control, validation, currency, and relevance as continuous responsibilities that apply at every phase. Atlas is one of the first platforms to translate these responsibilities into operational reality within Microsoft 365.
The knowledge lifecycle remains the essential model for KM. But the rise of AI means we must elevate governance into a cross-lifecycle discipline that ensures safety, accuracy, relevance, and trust.
Organizations that embed governance across the lifecycle will deliver:
And with Atlas, they can operationalize this model within the tools employees already use every day.
Download "The Modern Knowledge Lifecycle e-book" which presents a practical, modern framework for managing the knowledge lifecycle in the AI era and shows how Atlas operationalizes it end to end.
The knowledge lifecycle is the end-to-end process by which organizations create, capture, organize, share, apply, and improve knowledge. It ensures that insight from people, documents, systems, and experience is transformed into accessible, trusted, and actionable knowledge assets. Decades of knowledge management research show that while models vary, these core stages remain consistent.
Many AI initiatives underperform not because of the AI models themselves, but because of the state of the knowledge feeding them. Poorly structured, outdated, duplicated, or ungoverned knowledge leads to unreliable retrieval, misinformation, and AI hallucinations. In the age of tools like Microsoft Copilot, the quality and governance of organizational knowledge directly determine AI performance.
The modern knowledge lifecycle consists of six stages:
Create / Identify: Knowledge emerges through research, client work, collaboration, and problem-solving.
Capture / Codify: Tacit and experiential knowledge is converted into explicit, reusable forms.
Store / Organize: Knowledge is structured, governed, and enriched with metadata.
Share / Disseminate: Knowledge is distributed across teams, functions, and systems.
Apply / Use: Knowledge is used in decisions, workflows, client service, and innovation.
Review / Improve: Knowledge is continuously updated, improved, or retired to remain relevant.
Governance must span the entire lifecycle because generative AI now interacts with knowledge at every stage—capture, enrichment, retrieval, drafting, and review. Governance ensures quality, compliance, security, lineage, and AI safety on a continuous basis. Without cross-lifecycle governance, organizations cannot support safe, explainable, or compliant AI outcomes.
Atlas operationalizes the knowledge lifecycle inside Microsoft 365 by embedding structured creation, automated metadata enrichment, intelligent organization, secure dissemination, AI-powered retrieval, and continuous review directly into daily work. Governance runs across all stages through enforced metadata standards, provenance, sensitivity controls, and compliance alignment, enabling trusted knowledge and reliable AI at scale.
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