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.

The post What is a KM Operating Model and Why You Need One appeared first on Enterprise Knowledge.

The extent to which a company makes use of the collective insights and expertise that it accumulates over time—what we refer to as institutional knowledge—can significantly impact operational performance. When successfully captured and made easily accessible to employees (and to machines and the systems we work with), institutional knowledge can propel efficient and effective processes, enabling the company to function like a well-oiled machine. However, when institutional knowledge is poorly managed, bottlenecks and disrupted operations inevitably emerge.

These disruptions typically stem from two primary flaws. First, companies may fail to capture knowledge entirely, leaving it trapped in the minds of individual employees. When this occurs, the availability of experts directly determines whether critical tasks can be completed. Second, when knowledge is captured, employees may not effectively utilize it—whether they are unaware it exists, they do not trust its accuracy, they choose to ignore it, or they cannot easily access it when needed. In these cases, task execution by non-experts becomes slow and error-prone, creating growing backlogs and requiring frequent rework. When employees lack the expertise or resources needed to perform essential tasks, the result is often the same: compromised operational effectiveness.

A Peek into US Manufacturing

Based on a number of converging trends and factors, a strong case can be made for assessing institutional knowledge and its role in the manufacturing industry in particular. Downtime (when production halts or slows in manufacturing) has major implications to companies in terms of costs. A 2024 report indicates that an hour of complete downtime for major manufacturers can cost between $36,000 in fast moving consumer goods to $2.3 million for automobile manufacturing. When compared to five years prior to the report, the average recovery time increased 65% based on the report findings (from 49 minutes to 81 minutes). The increase is partially reflected by the lost skilled labor during the “great resignation” following the COVID-19 pandemic, which contributed to gaps in knowledge among workers. This challenge has intensified as US manufacturing also faces a concerning productivity decline. A review of US manufacturing data over the past twenty years shows an industry slowdown in labor productivity overall. From 1987 to 2007, labor productivity grew on average 3.4 percent each year, compared to a 3.9 percent drop per year in the same measure from 2010 to 2022. Meanwhile, an aging manufacturing workforce has been top-of-mind for industry leaders, as a study by the Manufacturing Institute recorded that 97 percent of industry respondents noted some level of apprehension about the aging workforce and its negative impact on institutional and technical knowledge. As experienced workers retire, they take with them decades of process knowledge and troubleshooting expertise that can play a part in helping to minimize production disruptions. These converging factors create a perfect storm where institutional knowledge gaps translate into longer production bottlenecks, extended downtime events, and increasingly costly disruptions.

A Case Study in Knowledge Management and Operational Effectiveness

EK partnered with an international goods manufacturer operating in more than 12 countries across the Americas to address critical content and knowledge management issues affecting their operations. Our initial assessment revealed significant bottlenecks and disruptions stemming from poorly preserved and inaccessible knowledge, most evident in their critical operational procedures and knowledge assets crucial for maintaining operations and ensuring compliance across multiple jurisdictions. Experts stored these assets on local hard drives and shared them through email, creating confusion with duplicate and conflicting versions. The absence of formal processes to track changes, establish any formal governance, or manage accountability for these vital resources eroded employee trust in the existing documentation. Consequently, employees relied heavily on individual experts to validate processes, creating workflow bottlenecks that dramatically slowed operations.

Following our evaluation, EK developed targeted solutions to dismantle knowledge silos and enhance knowledge capture across the organization. Our approach embedded knowledge management into daily operations with the implementation of improved systems and mechanisms to encourage capture and transfer at the right times. For example, development of a content model for process instructions would better enable employees to author and update process versions as they were realized, while implementing a compliance communications workflow would identify when and how employees were informed of regulatory changes that affected their day-to-day work. We also recommended establishing designated shared repositories to improve accessibility for non-experts and better match content needs with system capabilities. For example, we recommended using a product lifecycle management (PLM) system to manage formulas and product label designs for better versioning management, while SharePoint could effectively manage permits, registries, and licenses. We devised a plan for enriching knowledge resources with metadata and a semantic layer to enhance findability and discoverability. The workflow initiated for departing employees would trigger newly structured offboarding procedures to preserve critical knowledge. Furthermore, to help rebuild employee trust in the upkeep of company knowledge and systems, we designed a knowledge management governance plan and operating model with clear guidelines and measurable success criteria. Overall, the approach aimed to eliminate the barriers that stifled institutional knowledge capture and sharing and operational effectiveness. 

Lessons Learned and Considerations

Leveraging institutional knowledge to prevent bottlenecks and enhance business functions has proven to yield a positive return for many organizations. Below I outline the lessons learned and additional considerations that help improve successful operations:

• Embedding knowledge capture and retrieval into existing processes so it becomes an expected and intentional part of regular workflows, normalizing and promoting a knowledge culture. Consider building and implementing standardized operating procedures (SOPs) for common processes early, so employees come to expect and adhere to guidance that plainly states how tasks should be performed and encourages them to initiate changes to written procedures when they are needed.
• Designating specific repositories to store knowledge and writing clear purpose statements for them, so employees become familiar with the right repository for the right knowledge. Ensure employees have appropriate and easy access to query relevant knowledge from those systems, whether through an integrated search or alternative solution. 
• Templatizing key types of knowledge that supports the application of consistent metadata, so that knowledge can be formatted for quick consumption and easily retrieved from knowledge bases. Building a business taxonomy to provide structure for metadata profiles further helps to standardize the language that is used around company concepts and terms. 
• Integrating large language models (LLM) with knowledge graphs to analyze and synthesize content. With a semantic approach that embeds business context into meaning, utilizing artificial intelligence (AI) with large and/or complex data saves employees time and enables processing that may be beyond the ability of employees alone. 
• Building a search engine for employees to query enterprise content with natural language. Leveraging LLMs that can interpret conversational language in a search engine or portal can deliver accurate results and reduce the cognitive load on employees who need information in workflows quickly.

Closing

Bottlenecks and disrupted processes frequently signal underlying knowledge management challenges, as employees struggle to access or apply critical information to their daily work. A solution that starts by diagnosing these knowledge management challenges is often a winning strategy for improving operational workflows and outcomes. 

Enterprise Knowledge’s team of experts evaluate knowledge management gaps behind operational bottlenecks and disruptions, providing tailored solutions to improve information access and workflows. To understand how our services can help serve your needs, reach out to us at info@enterprise-knowledge.com to learn more.

Institutional knowledge is the sum of experiences, skills, and knowledge resources available to an organization’s employees. It includes the insights, best practices, know-how, know-why, and know-who that enable teams to perform. This knowledge is the lifeblood of work happening in modern organizations. However, not all organizations are capable of preserving, maintaining, and mobilizing their institutional knowledge—much to their detriment. This blog is one in a series of articles exploring the costs of lost institutional knowledge and different approaches to overcoming challenges faced by organizations in being able to mobilize their knowledge resources.

The post Using Knowledge Management to Prevent Bottlenecks and Disrupted Operations – A Case Study appeared first on Enterprise Knowledge.

Institutional knowledge loss erodes an organization’s effectiveness by neglecting critical collective wisdom and work previously produced by others. It is a challenge that is cumulative in its effect. The more time that goes by where an organization fails to protect its institutional knowledge, the more diminished it becomes. If a company ignores this problem long enough, they risk losing the building blocks that led to their success in the first place. Because employee makeup constantly changes—new employees join while existing employees exit or move to new roles and teams—companies must find ways to successfully sustain and share institutional knowledge during these routine employee development processes. 

Employee learning and development, like onboarding and upskilling, present ideal opportunities to intentionally and programmatically transfer institutional knowledge during key moments of the employee lifecycle. Quantifying the effects of employee learning on workers and organizations has been a hot topic over the years, as experts consider the impact to employee experience, retention, and productivity. A 2022 study of new employees confirms how pivotal the onboarding process is, with 70% of respondents indicating that onboarding “can make or break a new hire’s experience.” Fifty-two percent felt their onboarding left them undertrained for their role, with the vast majority of those respondents (80%) planning to leave their company soon. Additionally, a global 2024 study found that employees with upskilling opportunities describe themselves as 3.3 times more productive and are nearly six times more likely to endorse their employer as a great place to work when speaking with others. For a manufacturing client, EK is applying knowledge management practices to reduce the average time it takes to upskill employees from three years to one year. The impact of this achievement could also produce substantial cost savings, increased efficiency in employee time, and contribute to improved employee retention and satisfaction. When employees positively perceive their learning and development experiences and the transfer of knowledge meant to empower them, organizations help to enhance employee learning while taking steps to preserve institutional knowledge at the same time. 

When institutional knowledge—previously built and validated by others—is scarce or is ineffectively leveraged during employee development opportunities like onboarding and upskilling, it can produce negative effects on both the company and its workers. These risks and outcomes can compound over time, making it essential for organizations to address these knowledge transfer challenges. Before developing solutions to better nurture and harness their institutional knowledge to enhance learning, however, companies should pinpoint the unique combination of factors perpetuating the loss in the first place.

A Case Study in Employee Learning

The Learning and Development Group within the National Park Service (NPS) sought EK’s help to address growing challenges around employee learning events and training resources. As a geographically dispersed network of 22,000 professionals across 400 parks and National Monuments, the NPS was a difficult place for newcomers to naturally connect with the deep experience of established employees. Training resources were siloed on disparate department websites, and this lack of visibility resulted in duplicated training materials and poor discoverability of related content across departments. The NPS also relied heavily on volunteers during peak seasons to support their work, adding complexity to their need. EK helped them improve knowledge management practices and strengthen institutional knowledge, targeting two major root causes: inaccessible, siloed departmental trainings and insufficient support connecting professionals across the organization.

In collaboration with the NPS, EK designed, developed, and implemented the Common Learning Portal (CLP) to support both formal and social knowledge transfer. The portal provided the entire network access to newly aggregated training resources with improved discoverability of relevant topics (powered by a new enterprise taxonomy) and enhanced user experience (via faceted search). Additionally, social networking features built into the portal created space for Communities of Practice (CoPs) around common interests to emerge, which made it easier for colleagues to discuss issues and share advice and resources in dedicated forums. NPS staff then helped curate and formalize training that arose from CoP discussions, creating a new method to identify gaps in their learning resources. By improving access to learning materials and enabling meaningful connections across its network, the NPS diminished institutional knowledge loss and improved onboarding and expertise development for employees.

Additional Solutions for Employee Learning

Organizations can leverage institutional knowledge to enrich (and often accelerate) employee learning in a variety of ways, so long as the interventions and improvements address the root causes of initial learning barriers. From traditional knowledge management programs to advanced technology approaches, EK develops relevant and customized solutions for clients, such as:

• Job shadowing and mentoring programs to drive learning through experiential and observational methods, providing alternatives to static learning materials and practices. Employees build relationships with more tenured colleagues that possess specific skills, insights, and organizational knowledge, while advancing their own understanding in those same areas.
• Expert finders to provide employees of any tenure visibility into the expertise of their colleagues. This mechanism empowers employees through self-service searching to make connections or seek guidance regarding specific skills, tasks, or topics. Employees waste less time and experience fewer obstacles when a tool exists to find expertise at their point of need.
• Semantic technologies to provide training and knowledge discovery recommendations tailored to employees. For a learner, Artificial Intelligence (AI) can provide a skills gap analysis, recommending training and resources to fill desired gaps, as well as automated learning path assembly, crafting personalized training paths from learner profiles. Similarly, recommendation engines can help surface content curated specifically for the employee executing the search. These technologies provide resources and answers more quickly by identifying the unique needs of users.

Regardless of the solutions investigated and piloted to elevate employee learning, establishing clear success metrics and feedback mechanisms is essential. This helps to not only measure program impact, but also identify opportunities where improvements can be made to the employee learning experience. For example, assessing efficiency gains in knowledge acquisition and employee satisfaction against set goals are common success metrics for employee learning efforts. Whereas targeted surveys and check-ins are common mechanisms to extract input and suggestions from employees for continuous improvement. 

Closing

Onboarding, upskilling, and developing expertise among employees are fundamental, recurring business functions. Organizations that effectively introduce institutional knowledge into these transitions help improve outcomes for individual learning, reduce company costs, and also preserve institutional knowledge by its very use. 

Enterprise Knowledge customizes knowledge management strategies for clients around the globe to capture institutional knowledge to improve learning and business outcomes. If your organization needs a knowledge management strategy for your learning content or employee development processes, you can reach out to us at info@enterprise-knowledge.com.

Institutional knowledge is the sum of experiences, skills, and knowledge resources available to an organization’s employees. It includes the insights, best practices, know-how, know-why, and know-who that enable teams to perform. This knowledge is the life blood of work happening in modern organizations. However, not all organizations are capable of preserving, maintaining, and mobilizing their institutional knowledge—much to their detriment. This blog is one in a series of articles exploring the costs of lost institutional knowledge and different approaches to overcoming challenges faced by organizations in being able to mobilize their knowledge resources. 

The post Harnessing Institutional Knowledge to Enhance Employee Learning appeared first on Enterprise Knowledge.