Enterprise Knowledge COO Joe Hilger speaks with Michal Bachman, CEO at GraphAware. GraphAware provides technology and expertise for mission-critical graph analytics, and its graph-powered intelligence analysis platform — Hume — is used by democratic government agencies (law enforcement, intelligence, cybersecurity, defense) and Fortune 500 companies across the world.

In their conversation, Joe and Michal discuss how you can use a graph to investigate criminal networks, what’s next with graphs (hint: ensuring trustworthy AI doesn’t just mean supporting the machines), and some helpful books that experts at GraphAware have released recently.

Check out ⁠Knowledge Graphs and LLMs in Action⁠ and ⁠Neo4j: The Definitive Guide⁠ to dive deeper into the topics discussed in this episode!

If you would like to be a guest on Knowledge Cast, contact Enterprise Knowledge for more information.

The post Knowledge Cast – Michal Bachman, CEO of GraphAware appeared first on Enterprise Knowledge.

The Retirement Cliff and Its Implications

The “retirement cliff” refers to the impending wave of retirements as a significant portion of the workforce—particularly Baby Boomers—reaches retirement age. According to labor market trends, millions of experienced professionals are set to retire in the coming years, posing a critical challenge for organizations. The departure of seasoned employees risks the loss of institutional knowledge, technical expertise, and key relationships, leading to operational disruptions and costly efforts to regain lost expertise.

One of the most immediate financial consequences Enterprise Knowledge has seen on several of our engagements is the growing reliance on retirees returning as contractors to fill knowledge and capability gaps, often at significantly higher costs than their original salaries. While this can provide a short-term fix, it also creates a long-term liability. Research from Harvard Business Review and other labor market analyses shows that rehiring former employees without structured knowledge transfer can perpetuate a cycle of dependency, inflate workforce costs, and suppress the development of internal talent. Organizations may pay premium contract rates while still losing institutional knowledge over time, especially if critical expertise remains undocumented or siloed. Without proactive strategies, such as structured succession planning, mentoring, and systematic knowledge capture, organizations risk operational disruption, weakened continuity, and increased turnover-related costs that can amount to billions of dollars annually.

The Role of Knowledge Management in Succession Planning

Knowledge management plays a vital role in succession planning by implementing systems and practices that ensure critical expertise is systematically captured and transferred across generations of employees. Documenting key insights, best practices, and institutional knowledge is essential for mitigating the risk of knowledge loss. This process helps to strengthen organizational continuity and ensures that employees have the knowledge they need to perform their roles effectively and make informed decisions.

The Retirement Cliff: Challenges & Solutions

While the table highlights immediate challenges and corresponding solutions, organizations benefit from a deeper set of strategies that address both near-term risks and long-term sustainability. The following sections expand on these themes, outlining actionable approaches that help organizations capture critical knowledge today, while laying the foundation for resilient succession planning tomorrow.

Near-term Strategies: Mitigating Immediate Risk

Engage Employees in Knowledge Capture Efforts

Long-tenured employees approaching retirement have accumulated invaluable institutional knowledge, and their sustained tenure itself demonstrates their consistent value to the organization. When a retirement cliff is looming, organizations should take action to engage those employees in efforts that help to capture and transfer key institutional knowledge before it is lost.

Cast a Wide, Inclusive Net

Organizations often lack visibility into actual retirement timelines. Rather than making assumptions about who might retire or inadvertently pressuring employees to reveal their plans, frame knowledge transfer efforts as part of comprehensive KM practices. By positioning these initiatives as valuable for all long-tenured employees—not just potential retirees—organizations create an inclusive environment that captures critical knowledge. This broader approach not only prepares for potential retirement-related knowledge gaps but also establishes ongoing knowledge transfer as a standard organizational practice.

Acknowledge and Thank Employees

Explicitly acknowledge the expertise and contributions of key knowledge holders participating in efforts. By recognizing their professional legacy and expressing the organization’s desire to preserve and share their wisdom with others, leaders can create a foundation for meaningful participation in knowledge transfer activities. This approach validates key members’ career impact while positioning them as mentors and knowledge stewards for the next generation. Consider setting aside some time from their normal responsibilities to encourage participation.

Reward Knowledge Sharing

Employees are far more likely to engage in knowledge transfer when it is seen as both valuable and valued. In EK’s experience, organizations that successfully foster a culture of knowledge sharing often embed these behaviors into their core talent practices, such as performance evaluations and internal recognition programs. For example, EK has helped to incorporate KM contributions into annual review processes or introduce peer-nominated awards like “Knowledge Champion” to highlight and celebrate individuals who model strong knowledge-sharing behaviors.

Enable Employees to Capture Knowledge

Effective knowledge transfer begins with capturing critical institutional knowledge. This includes both explicit knowledge, such as processes and workflows, and tacit knowledge, such as decision-making frameworks, strategic insights, and the rationale behind past choices. To guide organizations in successful knowledge capture and transfer practices, EK recommends implementing a variety of strategies that help build confidence and make the process manageable.

Provide Documentation Training and Support

Organizations should consider offering dedicated support through roles and teams that naturally align with KM efforts, such as technical documentation, organizational learning and development, or quality assurance. These groups can help introduce employees to the practice and facilitate more effective capture. For example, many organizations focus solely on documenting step-by-step processes, overlooking the tacit knowledge that explains the “why” behind key decisions to provide future employees with critical context. In EK’s experience, preserving and transmitting knowledge of past actions and opinions has given teams the confidence to make more informed decisions and ensure coherence in guidance. This approach is especially valuable from a legal perspective, where understanding the rationale behind decisions is crucial for consistency and compliance.

Help Prioritize the Knowledge to Capture

Organizations can help focus knowledge capture efforts, without overwhelming employees, by prioritizing the types of knowledge to capture. If knowledge falls into one of these categories, it is ideal to prioritize:

1. 1. Mission-Critical Knowledge – High-impact expertise that is not widely known (e.g., decision-making rationales, specialized processes) is at greatest risk for loss. Encourage employees to prioritize this knowledge first.

1. 1. Operational Knowledge – Day-to-day processes that can be captured progressively over time. Suggest to employees that they take advantage of workflows and cycles as they are completed to document knowledge in real time from beginning to end.

1. 1. Contextual Knowledge – Broader insights from specific projects and initiatives are best captured in collective discussions or team reflections from various participants. Aim to make arrangements to put team members in conversation with one another and capture insights.

Embed Knowledge Capture into Workflows

Rather than treating documentation as a separate task, organizations should embed it into the existing processes and workflows where the knowledge is already being used. Integrating documentation creation and review into regular processes helps normalize knowledge capture as a routine part of work. In practice, this may look like employees updating Standard Operating Procedures (SOPs) during routine tasks, recording leadership reflections during key decisions, or incorporating “lessons learned” or retrospective activities into project cycles. Additionally, structured after-action reviews and reflective learning exercises can further strengthen this practice by documenting key takeaways from major projects and initiatives. Beyond improving project and knowledge transfer outcomes, these habits also build durable knowledge assets that support AI-readiness.

Design Succession-Focused Knowledge Sharing Programs

Cultural silos and resistance to sharing knowledge often undermine succession planning. Employees may hesitate to share what they know due to fears about losing job security, feeling undervalued, or simply lacking the time to do so. To overcome these challenges, organizations must implement intentional knowledge transfer programs, as outlined below, that aim to prevent a forthcoming retirement cliff from leaving large gaps.

Create Knowledge Transfer Interview Programs

Pairing long-tenured staff with successors ensures that critical institutional knowledge is passed on before key departures. Create thoughtful interview programming that takes the burden off the experienced staff from initiating or handling administrative efforts. EK recently partnered with a global automotive manufacturing company to design and facilitate structured knowledge capture and transfer plans for high-risk roles that were eligible for retirement, including walkthroughs of core responsibilities, stakeholder maps, decision-making criteria, and context around ongoing initiatives. These sessions were tracked and archived, enabling smoother transitions and reducing institutional memory loss. EK also supported a federal agency in implementing a leadership knowledge transfer interview series with retiring senior leaders to capture institutional knowledge and critical insights from their tenure. These conversations focused on navigating the agency’s operations, lessons for successors, and role-specific takeaways. EK distilled these into concise, topical summaries that were tagged for findability and reuse, laying the foundation for a repeatable, agency-wide approach to preserving institutional knowledge.

Foster Communities of Practice

Encourage cross-functional collaboration and socialize knowledge sharing across the organization by establishing communities of practice.  The programs provide opportunities for employees to gather regularly and discuss a common professional interest, to learn from each other through sharing ideas, experiences, and best practices. Involve long-tenured staff in these efforts and encourage them to develop topics around their expertise. EK has seen firsthand how these practices promote ongoing knowledge exchange, helping employees stay connected and informed across departments, even during leadership transitions.

Offer Formal Knowledge Exchange Programs

Knowledge Exchange Programs, like job shadowing, expert-led cohorts, and mentorship initiatives, create clear pathways for employees to share and document expertise before transitions occur. Long-tenured employees are often excellent candidates to take the leading role in these efforts because of the vast knowledge they hold.

Ultimately, effective succession planning is not just about capturing what people know—it is about creating a culture where knowledge transfer is expected, supported, and celebrated. By addressing resistance and embedding knowledge-sharing into the rhythm of daily work, organizations can reduce risk, improve continuity, and build long-term resilience.

Long-term Strategies: Building Sustainable Knowledge Flow

While short-term efforts can help reduce immediate risk, organizations also need long-term strategies that embed knowledge management into daily operations and ensure continuity across future workforce transitions. That is why EK believes Artificial Intelligence (AI) and Knowledge Intelligence (KI) are essential tools in capturing, contextualizing, and preserving knowledge in a way that supports sustainable transitions and continuity.

Below are long-term, technology-enabled strategies that organizations can adopt to complement near-term efforts and future-proof institutional knowledge.

Structure and Contextualize Knowledge with a Semantic Foundation

EK sees contextual understanding as central to KM and succession planning, as adding business context to knowledge helps to illuminate and interpret meaning for users. By breaking down content into dynamic, structured components and enriching it with semantic metadata, organizations can preserve not only the knowledge itself, but also the meaning, rationale, and relationships behind it. EK has supported clients in building semantic layers and structured knowledge models that tag and categorize lessons learned, decisions made, and guidance provided, enabling content to be reused, assembled, and delivered at the point of need. This approach helps ensure continuity through leadership transitions, reduces duplication of effort, and allows institutional knowledge to evolve without losing its foundational context.

Leverage Knowledge Graphs and Intelligent Portals

Traditional knowledge repositories, while well-intentioned, often become static libraries that users struggle to navigate. EK has helped organizations move from these repositories to dynamic knowledge ecosystems by implementing knowledge graphs and a semantic layer. These approaches connect once disparate data, creating relationships between concepts, decisions, and people.

To leverage the power of the knowledge graph and semantic layer, EK has designed and deployed knowledge portals for several clients, providing a means for users to engage with the semantic layer. These portals consolidate information from multiple existing systems into a streamlined, user-friendly landing page. Each portal is designed to serve as a central hub for enterprise knowledge, connecting users to the right information, experts, and insights they need to do their jobs, while also supporting smoother transitions when staff move on or new team members step in. With intuitive navigation and contextualized search, the portal helps staff quickly find complete, relevant answers across multiple systems, explore related content, and access expertise—all within a single experience.

Augment Search and Discovery with Artificial Intelligence

To reduce the friction of finding and applying knowledge, EK has helped clients enhance knowledge portals with AI capabilities, integrating features like context-aware search, intelligent recommendations, and predictive content delivery.  These features anticipate user intent, guide employees to relevant insights, and surface related content that might otherwise be missed. When paired with a strong semantic foundation, these enhancements transform a portal from a basic search tool into an intelligent instrument that supports real-time learning, decision-making, and collaboration across the enterprise.

Automate and Scale Tagging with AI-Assisted Curation

Manual tagging is often cited as one of the more time-consuming and inconsistent aspects of content management. To improve both the speed and quality of metadata, EK has helped clients implement AI-assisted tagging solutions that automatically classify content based on a shared taxonomy.

We recommend a human-in-the-loop model, where AI performs the initial tagging, and subject matter experts validate results to preserve nuance and apply expertise. This approach allows organizations to scale content organization efforts while maintaining accuracy and alignment.

For example, we partnered with a leading development bank to build an AI-powered knowledge platform that processed data from eight enterprise systems. Using a multilingual taxonomy of over 4,000 terms, the platform automatically tagged content and proactively delivered contextual content recommendations across the enterprise. The solution dramatically improved enterprise search, reduced time spent locating information, and earned recognition from leadership as one of the organization’s most impactful knowledge initiatives.

Integrate Technology, People, and Process Within Succession Planning

The most successful organizations do not treat knowledge technologies as standalone tools. Instead, they integrate them into broader KM and succession planning strategies, ensuring these solutions support, rather than replace, human collaboration and expertise.

In EK’s experience, when AI, knowledge graphs, and semantic metadata are used to enhance existing processes—like onboarding, leadership transitions, or project handovers—they become powerful enablers of continuity. These tools help protect institutional knowledge, reduce bottlenecks, and enable repeatable practices for knowledge transfer across roles, teams, and time.

As part of a long-term KM strategy, this allows organizations to evolve from reactive knowledge capture to proactive, ongoing knowledge flow.

Measuring Knowledge Transfer Impact

As we have provided the tools and advice for ensuring impactful knowledge captures and transfers, measuring the effectiveness of knowledge transfer initiatives is the essential next step to ensure that succession planning goals are being met and that knowledge transfer efforts are producing meaningful outcomes. Key performance indicators (KPIs) and metrics can help track the success of these initiatives and provide insights into their impact on the organization’s leadership pipeline.

Closing

Navigating the retirement cliff requires both immediate action and long-term planning. By addressing resistance, dismantling silos, embedding knowledge-sharing into daily work, and leveraging technology, organizations can reduce risk, preserve critical expertise, and build long-term resilience. Need help developing a strategy that supports both near-term needs and long-term success? Let’s connect to explore tailored solutions for your organization.

The post Navigating the Retirement Cliff: Challenges and Strategies for Knowledge Capture and Succession Planning appeared first on Enterprise Knowledge.

Enterprise Knowledge’s Lulit Tesfaye, VP of Knowledge & Data Services, speaks with Daan Hannessen, Global Head of Knowledge Management at Shell. He has over 20 years experience in Knowledge Management for large knowledge-intensive organizations in Europe, Australia, and the USA, ranging from continuous improvement programs, KM transformations, lessons learned solutions, digital workplaces, AI driven expert bots, enterprise search, and much more.

In their conversation, Lulit and Daan discuss the importance of senior leadership support in ensuring the success of KM initiatives, emphasizing “speaking their language” as key to implementing KM and the semantic layer at a global scale. They also touch on how to measure the success of AI, when AI-generated content can be considered valuable insights, and why to invest in a semantic layer in the first place, as well as Daan’s talk at the upcoming Semantic Layer Symposium.

For more information on the Semantic Layer Symposium, check it out here!

If you would like to be a guest on Knowledge Cast, contact Enterprise Knowledge for more information.

The post Knowledge Cast – Daan Hannessen, Global Head of Knowledge Management at Shell – Semantic Layer Symposium Series appeared first on Enterprise Knowledge.

Enterprise Knowledge’s Lulit Tesfaye, VP of Knowledge & Data Services, speaks with Ben Clinch, Chief Data Officer and Partner at Ortecha and Regional Lead Trainer for the EDM Council (EMEA/India). He is a sought-after public speaker and thought leader in data and AI, having held numerous senior roles in architecture and business in some of the world’s largest financial and telecommunication institutions over his 25 year career, with a passion for helping organizations thrive with their data.

In their conversation, Lulit and Ben discuss Ben’s personal journey into the world of semantics, their data architecture must-haves in a perfect world, and how to calculate the value of data and knowledge initiatives. They also preview Ben’s talk at the Semantic Layer Symposium in Copenhagen this year, which will cover the combination of semantics and LLMs and neurosymbolic AI. 

For more information on the Semantic Layer Symposium, check it out here!

If you would like to be a guest on Knowledge Cast, contact Enterprise Knowledge for more information.

The post Knowledge Cast – Ben Clinch, Chief Data Officer & Partner at Ortecha – Semantic Layer Symposium Series appeared first on Enterprise Knowledge.

The Solution

To unlock data insights across trials in one accessible location, EK designed and developed a Power BI dashboard to visualize data from multiple trials in one centralized location. To begin developing dashboards, EK met with the client to confirm the business questions the dashboards would answer, ensuring the dashboards would visually display the patient and trial information needed to answer them. To remedy the varying data formats sent by vendors, EK mapped data values from trial reports to each other, normalizing and enriching the data with metadata and lineage. With structure and standardization added to the data, the dashboards could display robust data insights into patient status with filterable trial-specific information for the clinical imaging team.

EK also worked to transform the company’s data management environment—developing a medallion architecture structure to handle historical files and enforcing data cleaning and standardization on raw data inputs—to ensure dashboard insights were accurate and scalable to the inclusion of future trials. Implementing these data quality pre-processing steps and architecture considerations prepared the company for future applications and uses of reliable data, including the development of data products or the creation of a single view into the company-wide data landscape.

The EK Difference

To support the usage, maintenance, and future expansion of the data environment and data visualization tooling, EK developed knowledge transfer materials. These proprietary materials included setting up a semantic modeling foundation via a data dictionary to explain and define dashboard fields and features, a proposed future medallion architecture, and materials to socialize and expand the usage of visualization tools to additional sections of the company that could benefit from them.

Dashboard Knowledge Transfer Framework
To ensure the longevity of the dashboard, especially with the future inclusion of additional trial data, it was essential to develop materials for future dashboard users and developers. The knowledge transfer framework designed by EK outlined a repeatable process for dashboard development with enough detail and information that someone unfamiliar with the dashboards can understand the background, use cases, data inputs, visualization outputs, and the overall purpose of the dashboarding effort. Instructions for dashboard upkeep, including how to update and add data to the dashboard as business needs evolve, were also provided.

Semantic Model Foundations: Data Dictionary
To semantically enhance the dashboards, all dashboard fields and features were cataloged and defined by EK experts in semantics and data analysis. In addition to definitions, the dictionary included purpose statements and calculation rules for each dashboard concept (where applicable). This data dictionary was created to prepare the client to process all trial information moving forward and serve as a reference for the data transformation process.

Proposed Future Architecture
To optimize data storage in the future, EK proposed a medallion architecture strategy consisting of Bronze, Silver, and Gold layers to preserve historical data and pave the way for matured logging techniques. At the time EK engaged the client, there was no proper data storage. EK’s architecture strategy detailed storage preparation considerations for each layer, including workspace creation, file retention policies, and options for ingesting and storing data. EK leveraged technical expertise and a rich background in architecture strategies to provide expert advisory on the client’s future architecture.

Roadshow Materials
EK developed materials that summarized the mission and value of the clinical imaging dashboards. These materials included a high-level overview of the dashboard ecosystem so all audiences could comprehend the dashboard’s purpose and execution. With a KM-angled focus, the overall purpose of the materials was to gain organizational buy-in for the dashboard and build awareness of the clinical imaging team and the importance of the work they do. The roadshow materials also sought to promote dashboard adoption and future expansion of dashboarding into other areas of the company.

The Results

Before the dashboard, employees had to track down various spreadsheets for each trial sent from different sources and stored in at least four different locations. After the engagement, the company had a functional dashboard that displayed on-demand data visualizations across four clinical trials that pulled from a single data repository, creating a seamless way for the clinical imaging team to identify trial data and patient discrepancies early and often, preventing errors that could have resulted in unusable trial data. In all, having multiple trials’ information available in one streamlined view through the dashboard dramatically reduced the time and effort employees had previously spent tracking down and manually analyzing raw, disparate data for insights, from as high as 1–2 hours every week to as low as 15 minutes. Clinical imaging managers are now able to quickly determine and share trusted trial insights with their leadership confidently, enabling informed decision-making with the resources to explain where those insights were derived from.

In addition to the creation of the dashboard, EK helped develop a knowledge transfer framework and future architecture and data cleaning considerations, providing the company with a clear path to expand and scale usage to more clinical trials, other business units, and new business needs. In fact, the clinical imaging team identified at least four additional trials that, as a result of EK’s foundational work, can be immediately incorporated into the dashboard as the company sees fit.

Want to improve your organization’s content data quality and architecture? Contact us today!

 

Download Flyer

 

The post Data Quality and Architecture Enrichment for Insights Visualization appeared first on Enterprise Knowledge.

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.

Enterprise Knowledge’s Lulit Tesfaye, VP of Knowledge & Data Services, speaks with Dawn Brushammar, currently an independent KM consultant, advisor, and frequent contributor at industry events. She has spent her 25+ year career connecting people to relevant knowledge and information. Her experience across industries and geographies includes leading an internal Knowledge Management team at McKinsey and Company, building databases for the Oprah Winfrey Show, running research services for a division of American Express, and managing academic librarianship at several universities and an environmental and sustainability research institute.

In their conversation, Lulit and Dawn discuss the similarities between their early career paths and KM journeys, the evolving role of the modern librarian, and how KM and semantics support AI technologies. They also define what a “knowledge-first organization” should look like, and touch on Dawn’s upcoming talk at the Semantic Layer Symposium on the rising importance of library science to the Semantic Layer.

For more information on the Semantic Layer Symposium, check it out here!

If you would like to be a guest on Knowledge Cast, contact Enterprise Knowledge for more information.

The post Knowledge Cast – Dawn Brushammar, Independent Knowledge Management Consultant & Programme Chair of KMWorld Europe – Semantic Layer Symposium Series appeared first on Enterprise Knowledge.

Enterprise Knowledge’s Lulit Tesfaye, VP of Knowledge & Data Services, speaks with Paco Nathan, Developer Relations (DevRel) Leader for the Entity Resolved Knowledge Graph Practice at Senzing. He is a computer scientist with over 40 years of tech industry experience and core expertise in data science, natural language, graph technologies, and cloud computing. He’s the author of numerous books, videos, and tutorials about these topics. He also hosts the monthly “Graph Power Hour!” webinar.

In their conversation, Lulit and Paco discuss Paco’s background in the graph space, as well as current graph trends and scalable use cases for the Semantic Layer. They also touch on how to convince organizations to prioritize investments in semantic technologies and data management, and Paco shares more details on his talk about financial crimes and Semantic Layers at the upcoming Semantic Layer Symposium in Copenhagen.

For more information on the Semantic Layer Symposium, check it out here!

If you would like to be a guest on Knowledge Cast, contact Enterprise Knowledge for more information.

The post Knowledge Cast – Paco Nathan, Principal DevRel Engineer at Senzing – Semantic Layer Symposium Series appeared first on Enterprise Knowledge.

Drawing from multiple client engagements across sectors, Tesfaye and DeMay shared patterns and lessons learned from initiatives where KM, Data Management, and AI teams are working together to create a more connected and intelligent enterprise. They highlighted the growing need for integrated strategies that bring together semantic modeling, content management, and metadata governance to enable intelligent automation and more effective knowledge discovery.

The presentation emphasized how KM professionals can lead the way in designing sustainable semantic architectures, building cross-functional partnerships, and aligning programs with organizational priorities and AI investments. Presenters also explored how roles are shifting from traditional content stewards to strategic enablers of enterprise intelligence.

Session attendees walked away with:

• Insight into how KM roles are expanding to meet enterprise-wide data and AI needs;
• Examples of how semantic layers can enhance findability, improve reuse, and enable automation;
• Lessons from organizations integrating KM, Data Governance, and AI programs; and
• Practical approaches to designing cross-functional operating models and governance structures that scale.

The post The Evolution of Knowledge Management & Organizational Roles: Integrating KM, Data Management, and Enterprise AI through a Semantic Layer appeared first on Enterprise Knowledge.