As organizations race to adopt AI, implement advanced analytics, or embed new knowledge management (KM) strategies into their ways of working, the way they capture, organize, and transform knowledge becomes the foundation for success. While many organizations invest heavily in new tools and well-crafted KM strategies, they often overlook a critical enabler: the operating model, which is a framework of roles, structures, and governance that ensures KM and AI efforts do not just launch, but scale and sustain. 

This blog is the first in a two-part series exploring how organizations can design and sustain an effective KM operating model. This first blog focuses on one essential component of the operating model: the framework of roles that enable KM efforts to scale and deliver sustained impact. Clearly defining these roles and their structure helps organizations integrate related disciplines, such as data and AI, avoid duplication, and ensure teams work toward shared outcomes. In the second blog, we will share a practical roadmap for designing an operating model that aligns KM, data, and AI to maximize long-term value.

What is a KM Operating Model?

An operating model defines how an organization functions to serve its vision and realize its strategic goals by aligning elements like roles and responsibilities, organizational structure, governance frameworks, decision-making processes, and change management approaches. 

For KM, a strong operating model outlines:

• How knowledge flows across the organization
• Who owns and governs it 
• What processes and key interaction points enable it
• Which tools and standards are applied to deliver value 

In other words, it integrates people, processes, governance, and resources to ensure KM becomes a sustainable organizational capability, rather than a temporary initiative or toolset. 

What an Operating Model Looks Like in Practice

When a large automotive manufacturer wanted to implement a Knowledge Portal and improve the way knowledge was captured and transferred throughout its North American factory and business units, Enterprise Knowledge (EK) worked with the organization to design an operating model with a centralized Knowledge Management Center of Excellence (CoE) to align with current ways in which the company operates. Staffed by a Program Director, Knowledge Manager, KM System Administrator, and a Knowledge Modeling Engineer, these core roles would lead the charge to align business units in improving content quality, knowledge capture and transfer, and drive KM adoption and value, as well as scale the Knowledge Portal. In considering how to successfully roll out the technical solution, complementary content, and KM strategies to nearly 20,000 employees, EK recommended partially dedicated KM support roles within individual organizational units to reinforce KM adoption and deliver support at the point of need. By training existing employees already embedded within an organizational unit on KM initiatives, support comes from familiar colleagues who understand the team’s workflows, priorities, and pain points. This helps surface obstacles, such as competing demands, legacy processes, or resistance to change, that might otherwise hinder KM adoption, while also ensuring guidance is tailored to the realities of daily work within the organization. This strategy was intended to not only strengthen employees’ ability to find, share, and apply knowledge in their daily work, but also to build a network of formal KM champions who would be equipped to help inform and embed the KM CoE’s enterprise vision. This new network would also support the planned future implementation of AI capabilities into the Knowledge Portal and in knowledge capture and transfer activities.

Example Operating Model with a KM Center of Excellence:

In another case, a global conservation organization sought to remedy struggling KM efforts and an organizational structure that lacked effectiveness and authority. With a focus on maturing both their KM program and its facilitating framework, EK developed a new operating model seeking cross-functional coordination and KM alignment. The new model also accompanied an effort to advance their technology stack and improve the findability of knowledge assets. A newly retooled KM Enablement Team would provide strategic oversight to operationalize KM across the organization with focused efforts and dedicated roles around four key initiatives: Knowledge Capture & Content Creation, Taxonomy, Technology, and Data. This enablement framework required Workstream Leads to participate in regular meetings with the KM Enablement team to ensure initiative progress and alignment to the advancing KM solution. Designed to not only guide the implementation of an enterprise-level KM Program, this framework would also sustainably support its ongoing governance, maintenance, and enhancement.

Example Operating Model with a KM Enablement Team:

Why You Should Develop an Operating Model

Without a clear operating model, even the most promising KM initiatives risk stalling after the initial launch. Roles become unclear, priorities drift, and the connection between KM strategy and day-to-day work weakens. An operating model creates the structure, accountability, and shared understanding needed to keep KM efforts focused, adaptable, and impactful over time. 

As organizations evolve, their KM efforts must keep pace, not just growing in capability but in navigating new challenges. Without this evolution, misalignment creeps in, draining value and creating costly friction. At the same time, the boundaries between KM, data, and AI are blurring, making collaboration not only beneficial but necessary. Understanding these dynamics is critical to appreciating why a thoughtfully designed operating model is the backbone of sustainable knowledge management.

The Evolution of Knowledge Management Maturity

Most organizations do not start with a fully mature KM program or operating model. They evolve into them. Often, KM efforts begin as isolated, informal initiatives and grow into structured, enterprise-wide models as KM needs and capabilities mature.

The EK KM maturity model outlines five stages, from ‘Ad-Hoc’ to ‘Strategic’, that reflect how KM roles, tools, and outcomes mature over time. In the less mature stages, an inconsistent KM strategy is met with operating models that lack intention and legitimacy to sustain KM. At these stages, roles for KM are not formalized or are minimally visible and cursory. As maturity grows, increasing alignment between KM practices and business or AI goals gets supported by an operating model with clearer ownership and dedicated roles, scalable governance, and integrated systems.

By mapping existing systems, structures, and people roles onto the model, EK diagnoses the current state of client KM maturity and identifies the maturity characteristics that would support relevant KM evolution.

The Cost of Misalignment

When an organization rolls out a new enterprise KM, AI, or data solution without clearly identifying and establishing the roles and organizational structure needed to support it, those solutions often struggle to deliver their intended value. This is a common challenge that EK has observed when organizations overlook how the solution will be governed, maintained, and embedded in day-to-day work. This misalignment creates real risk as the solution can become ineffective, underutilized, or scrapped entirely. 

When the necessary roles and organizational framework do not exist to drive or sustain KM intentions, common resulting conditions arise, including:

• Deteriorating content quality: Information can become outdated, fragmented, duplicated, or hard to find, undermining trust in the KM solution. 
• Solution misuse: Employees remain unclear about the solution’s purpose and benefits, leading to incorrect usage and inconsistent solution outcomes.
• Technology sitting idle:  Despite technical functionality and success, solutions fail to integrate into workflows, and the anticipated business value is not met.

These costly outcomes represent more than just implementation challenges–they are a missed opportunity to legitimize the value of KM as a critical enabler of AI, compliance, innovation, and business continuity. 

The Convergence Factor

As organizations begin to better understand the need for an operating model that supports their transformational efforts, formalized cross-collaborative teams and frameworks are becoming more popular. The push toward integrating KM, data, and AI teams is not coincidental; several forces and potential benefits are accelerating the move toward converging teams:

• Demands of changing technology: The rise of semantic layers, large language models (LLMs), and AI-enabled search surface the need for structured, standardized knowledge assets, historically unique to the realm of KM, but now core to AI and data workflows. Collaboration from subject matter experts from all three areas ensures the inputs needed for these technologies, like curated knowledge and clean data, as well as the processes that ensure those, are present to produce the intended outputs, such as generative content that is accurate and properly contextualized.
• Leaner operations: While budgets may shrink, expectations for more insights and automation are growing. Instead of hiring new roles for new solutions, some companies are being asked to retool existing roles or merge disparate teams to oversee new needs. The convergence of roles in these scenarios offers opportunities to show how integration reduces redundancy and strengthens solution delivery.
• Shared systems, shared stakes: KM platforms, data catalogs, and AI training environments are increasingly overlapping or built on the same tech stack. Integration helps ensure these tools are optimized and governed collectively.
• Scalability: Unified teams create structures that scale enterprise initiatives more effectively; reinforcing standards, enabling shared support models, and accelerating adoption across business units. When KM, data, and AI teams move from siloed functions to integrated workflows, their collective influence helps scale solutions that no single team could drive alone.

Enterprise Knowledge (EK) has seen firsthand how organizations are recognizing the value of cross-functional collaboration catalyzed by KM. For example, a large construction-sector client came to EK to bolster internal efforts to connect KM and data functions. This led to the alignment of parallel initiatives, including content governance, data catalog development, and KM strategy. EK’s engagement helped accelerate this convergence by embedding KM specialists to support both streams of work, ensuring continuity, shared context, and a repeatable governance model across teams.

Closing Thoughts

Your knowledge management strategy is only as effective as the operating model behind it. By intentionally designing clear KM roles and responsibilities to support your KM goals and initiatives, you create the foundation for sustainable, scalable KM that is ready for AI and data advancement. In Part 2 of this blog series, we will walk through how to design and implement a KM operating model that leverages team integration and supports maturing strategies.

If you are unsure where your organization sits on the KM Maturity Ladder–or need support designing an operating model that enables sustainable, high-impact knowledge management–EK is here to help. Contact us to learn how we can support your KM transformation and build a model that reflects your goals.