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.

In this presentation, Galdamez and Cross will go into an in-depth explanation of Knowledge Portals, their value to organizations, the technical components that make up these solutions, lessons learned from their implementation across multiple clients in different industries, how and when to make the case to get started on a Knowledge Portal design and implementation effort, and how these solutions can become a catalyst for a knowledge transformation within organizations.

Find out more about the event and register at the conference website.

The post Galdamez and Cross to Speak at the APQC 2025 Process & Knowledge Management Conference appeared first on Enterprise Knowledge.

In 2019, Gartner published research emphasizing the end to “a single version of the truth” for data and knowledge management and that by 2026, “active metadata” will power over 50% of BI and analytics tools and solutions to provide a structured and consistent approach to connecting instead of consolidating data.  

By employing semantic components and standards (through metadata, business glossaries, taxonomy/ontology, and graph solutions), a semantic layer arms organizations with a framework to aggregate and connect siloed data/content, explicitly provide business context for data, and serve as the layer for explainable AI. Once connected, independent business units can use the organization’s semantic layers to locate and work with not only enterprise data, but their own, unit-specific data as well. 

Incorporating a semantic layer into enterprise architecture is not just a theoretical concept, it’s a practical enhancement that transforms how organizations harness their data. Over the last ten years, we’ve worked with a diverse set of organizations to design and implement the components of a semantic layer. Many organizations we work with support a data architecture that is based on relational databases, data warehouses, and/or a wide range of content management, cloud, or hybrid cloud applications and systems that drive data analysis and analytics capabilities. These models do not necessarily mean that organizations need to start from scratch or overhaul their working enterprise architecture in order to adopt/implement a semantic layer. To the contrary, it is more effective to shift the focus on metadata and data modeling or designing efforts by adding models and standards that will allow for capturing business meaning and context in a manner that provides the least disruptive starting point. 

Though we’ve been implementing the individual components for over a decade, it has only been the last couple years where we’ve been integrating them all to form a semantic layer. The maturity of approaches, technologies, and awareness have all combined with the growing need of organizations and the AI revolution to create this opportunity now.

In this article, I will explore the top three common approaches we are seeing at play in order to weave this data and knowledge layer into the fabric of enterprise architecture, highlighting the applications and organizational considerations for each.

1. A Metadata-First Logical Architecture: Using Enterprise Semantic Layer Solutions

This is the most common and scalable model we see across various industries and use cases for enterprise-wide applications. 

Architecture 

Implementing a semantic layer through a metadata-first logical architecture involves creating a logical layer that abstracts the underlying data sources by focusing on metadata. This approach establishes an organizational logical layer through standardized definitions and governance at the enterprise level while allowing for additional, decentralized components and solutions to be “pushed,” “published,” or “pulled from” specific business units, use cases, and systems/applications at a set cadence. 

Pros

Using middleware solutions like a data catalog or an ontology/graph storage, organizations are able to create a metadata layer that abstracts the underlying complexities, offering a unified view of data in real time based on metadata only. This allows organizations to abstract access, ditch application-centric approaches, and analyze data without the need for physical consolidation. This model effectively leverages the capabilities of standalone systems or applications to manage semantic layer components (such as metadata, taxonomies, glossaries, etc.) while providing centralized storage for semantic components to create a shared, enterprise semantic layer. This approach ensures consistency in core or shared data definitions to be managed at the enterprise level while providing the flexibility for individual teams to manage their unique secondary and group-level semantic data requirements.

Cons

Implementing a semantic layer as a metadata architecture or logical layer across enterprise systems requires planning in phases and incremental development to maintain cohesion and prevent fragmentation of shared metadata and semantic components across business groups and systems. Additionally, depending on the selected synchronization approach of the layer with downstream/upstream applications (push vs. pull), data orchestration and ETL pipelines will need to plan for a centralized vs. decentralized orchestration that ensures ongoing alignment. 

Best Suited For

This approach is our most deployed and well-suited for organizations that want to balance standardization with the need for business unit or application level agility in data processing and operations in different parts of the business.

2. Built-for-Purpose Architecture: Individual Tools with Semantic Capabilities

This model allows for greater flexibility and autonomy at the business unit or functional level. 

Architecture 

This architecture approach is a distributed model that leverages each standalone system or application capabilities to own semantic layer components – without a connected technical framework or governance structure at the enterprise level for shared semantics. With this approach, organizations typically identify establishing semantic standards as a strategic initiative but each individual team or department (marketing, sales, product, data teams, etc.) is responsible for creating, executing, and managing its semantic components (metadata, taxonomies, glossaries, graph, etc.), tailored to their specific needs and requirements.

Most knowledge and data solutions such as content or document management systems (CMS/DMS), digital asset management (DAMs), customer relationship management (CRM), and data analytics/BI dashboards (such as Tableau and PowerBI) have inherent capabilities to manage simple semantic components (although with varied maturity and feature flexibility levels). This decentralized architecture results in the implementation of multiple system-level semantic layers. Let’s take SharePoint as an example, an enterprise document and content collaboration platform. For organizations that are in the early stages of growing their semantic capabilities, we leverage the Term Store for structuring metadata and taxonomy management within SharePoint, which allows teams to create a unified language, fostering consistency across documents, lists, and libraries. This helps with information retrieval and also enhances collaboration by ensuring a shared understanding of key metrics. On the other hand, Salesforce, a renowned CRM platform, offers semantic capabilities that enable teams across sales, marketing, and customer service to define and interpret customer data consistently across various modules.

Pros

This decentralized model promotes agility and empowers business units to leverage their existing platforms (that are built-for-purpose) as not just data/content repositories but as dynamic sources of context and alignment, driving consistent understanding of shared data and knowledge assets for specific business functions.

Cons

However, this decentralized approach typically leads those users who need cohesive organizational content and data to do so through separate interfaces. Data governance teams or content stewards are also likely to manage each system independently. This leads to data silos, “semantic drifts,” and inconsistency in data definitions and governance (where duplication and data quality issues arise). This ultimately results in misalignment between business units, as they may interpret data elements differently, leading to confusion and potential inaccuracies.

Best Suited For

This approach is particularly advantageous for organizations with diverse business units or teams that operate independently. It empowers business users to have more control over their data definitions and modeling and allows for quicker adaptation to evolving business needs, enabling business units to respond swiftly to changing requirements without relying on a centralized team. 

3. A Centralized Architecture: Within an Enterprise Data Warehouse (EDW) or Data Lake (DL)

This structured environment simplifies data engineering and ensures a consistent and centralized semantic layer specifically for analytics and BI use cases.

Architecture

Organizations that are looking to create a single, unified representation of their core organizational domains develop a semantic layer architecture that serves as the authoritative source for shared data definitions and business logic within a centralized architecture – particularly within an Enterprise Data Warehouse or Data Lake. This model makes it easier to build the semantic layer since data is already in one place, and analytics solutions that are using cloud-based data warehousing platforms (e.g., Amazon Redshift, Google BigQuery, Snowflake, Azure Blob Storage, Databricks, etc.) can serve as a “centralized” location for semantic layer components. 

Building a semantic layer within an EDW/DL involves consolidating and ingesting data from various sources into a centralized repository, identifying key data sources to be ingested, defining business terms, establishing relationships between different datasets, and mapping the semantic layer to the underlying data structures to create a unified and standardized interface for data access. 

Pros

This model architecture is a common implementation approach we support specifically within a dedicated team of data management, data analytics, and BI groups that are consistently ingesting data, setting the implementation processes for changes to data structures, and enforcing business rules through dedicated pipelines (ETL/APIs) for governance across enterprise data. 

Cons

The core consideration here (that usually suffers) is collaboration between business and data teams that is pivotal during the implementation process, guides investment in the right tools and solutions that have semantic modeling capabilities, and supports the creation of a semantic layer within this centralized landscape. 

It is important to ensure that the semantic layer reflects the actual needs and perspectives of end users. Regular feedback loops and iterative refinements are essential for creating a model that evolves with the dynamic nature of business requirements. Adopting these solutions within this environment will enable the effective definition of business concepts, hierarchies, and relationships, allowing for translation of technical data into business-friendly terms.

Another important aspect with this type of centralized model is that it is dependent on data that is consolidated or co-located and requires upfront investment in terms of resources and time to design and implement the layer comprehensively. As such, it’s important to start small by focusing on specific business use cases, the relevant scope of knowledge/data sources and foundational models that are highly visible, and focused on business outcomes. This will allow the organization to create a foundational model that will expand across the rest of the organization’s data and knowledge assets, incrementally. 

Best Suited For

We have seen this approach being particularly beneficial for large enterprises with complex but shared data requirements and that have the need for stringent knowledge and data governance and compliance rules – specifically, organizations that produce data products and need to control the data and knowledge assets that are shared internally or externally on a regular basis. This includes, but is not limited to, financial institutions, healthcare organizations, bioengineering firms, and retail companies. 

Closing

A well-implemented semantic layer is not merely a technical necessity but a strategic asset for organizations aiming to harness the full potential of their knowledge and data assets, as well as have the right foundations in place to make AI efforts successful. The choice of how to architect and implement a semantic layer depends on the specific needs, size, and structure of the organization. When considering this solution, the core decision really comes down to striking the right balance between standardization and flexibility, in order to ensure that your semantic layer serves as an effective enabler for knowledge-driven decision making across the organization. 

Organizations that invest in an enterprise architecture through the metadata layer and those that rely on experts with modeling experience that are anchored in semantic web standards find it the most flexible and scalable approach. As such, they are better positioned to abstract their data from vendor lock and ensure interoperability to navigate the complexities of today’s technologies and future evolutions.

When embarking on a semantic layer initiative, not understanding or planning for a solid technical architecture and phased implementation approach leads to unplanned investments or failure for many organizations. If you are looking to get started and learn more about how other organizations are approaching scale, read more from our case studies or contact us if you have specific questions.

The post The Top 3 Ways to Implement a Semantic Layer appeared first on Enterprise Knowledge.

EK is one of only a small number of dedicated services organizations that made the list, delivering end-to-end services around advanced semantics, knowledge graphs, and artificial intelligence, including use case definition, technology selection, design, implementation, and support services for the full range of Enterprise AI components. Uniquely, EK also delivers training programs to help organizations scale AI to the enterprise, and offers AI pilots to prove out the value and feasibility for organizations seeking to demonstrate business value quickly.

“Today, AI has the potential to impact almost every part of an organization’s structure and operations, including their customer-facing presence,” remarked Tom Hogan Jr., publisher of KMWorld. “We see AI reaching into marketing, customer service, legal, finance, human resources, compliance, fleet maintenance, manufacturing, sales, and many other business units.”

Lulit Tesfaye, EK’s Vice President of Knowledge and Data Services, stated, “With all the recent developments in AI, we are thrilled to be recognized again for the foundational work and AI capabilities we have helped our clients realize. We are proud to be in a position to continue to innovate and bring lessons learned, experience, and thought leadership to bear for our industry.”

EK CEO Zach Wahl added, “EK has been making AI real for organizations for years now, allowing us to lead at the intersection of Knowledge, Data, and Information to make Artificial Intelligence work for our customers. I appreciate KMWorld’s continued recognition of our work in this space.”

To read more about the recognition, visit Lulit’s AI Spotlight article on KMWorld and explore EK’s knowledge base for the latest AI and KM thought leadership.

About Enterprise Knowledge

Enterprise Knowledge (EK) is a services firm that integrates Knowledge Management, Information and Data Management, Information Technology, and Agile Approaches to deliver comprehensive solutions. Our mission is to form true partnerships with our clients, listening and collaborating to create tailored, practical, and results-oriented solutions that enable them to thrive and adapt to changing needs.

About KMWorld

KMWorld is the leading information provider serving the Knowledge Management systems market and covers the latest in Content, Document and Knowledge Management, informing more than 21,000 subscribers about the components and processes – and subsequent success stories – that together offer solutions for improving business performance.

KMWorld is a publishing unit of Information Today, Inc.

The post EK Recognized as Leader in Artificial Intelligence appeared first on Enterprise Knowledge.

Professionals trying to garner support for a Knowledge Management (KM) initiative within their team and/or organization are often asked by well-meaning colleagues and executives:

For all professionals seeking to make KM a priority within their organizations, being able to concisely articulate what KM is and the value that it brings is paramount. As such, a KM elevator pitch is an apt solution for those seeking to garner buy-in and excitement for KM within their organization, as it provides individuals with a shared understanding of KM, the organizational challenges it can address, and the business value it can provide. In this blog, I break down the key steps to create a successful KM elevator pitch that will help you to champion and gain momentum for KM initiatives at your own organization. 

Definition of KM

An effective KM elevator pitch must include a definition of what encompasses KM. EK uses the following definition when it embarks on a KM Strategy engagement: 

Knowledge Management (KM) involves the People, Processes, Culture, and enabling Technologies necessary to capture, manage, share, and find knowledge and create value from it. This covers a wide spectrum of tacit and explicit knowledge as well as structured data and unstructured content.

To incorporate KM into teams, workstreams, and/or on an enterprise scale, KM supporters must provide a definition that is concise, thorough, and customized for the specific needs of their organization. Asking the following scoping questions can help produce a more specific KM definition:

• What unique knowledge (i.e. tacit knowledge, information, data) do employees need to accomplish both daily work and the organization’s higher-level strategic plans?
• Are you defining knowledge management for the enterprise or for a specific department, function, or team? 
• What is the organization’s unique knowledge in service of? What is it used for?
• What aspects of knowledge management are most important for your organization (i.e. finding, capturing, sharing, managing, storing, enhancing, etc.)?

Current organizational challenges as related to knowledge and information 

With a definition of KM in mind for your organization, the next step is to identify and highlight those organizational challenges staff are experiencing as related to knowledge and information. When crafting a KM elevator pitch, professionals should specify whether their outlined challenges manifest within a particular team or department, or if the obstacles correlate to a chronic occurrence at the enterprise-level. Additionally, you should also consider who you need to sell KM to, and what will specifically resonate with them. The KM challenges that front line staff, managers, and executives each experience will be distinct from one another, based on their role-specific needs and responsibilities. Based on an understanding of your colleagues’ specific obstacles, it is possible to customize an elevator pitch to speak directly to their challenges and increase the likelihood of support and buy-in for KM projects. Possible challenges can include loss of institutional knowledge following employee departure or retirement, inconsistent conventions for naming and storing critical information, and time wasted in the search for reliable information. 

KM business value statements 

The most important step of a KM elevator pitch is tying the identified organizational challenges to business value statements that speak to how KM will provide tangible benefit to your organization. Business value statements about KM should be measurable (through different types of metrics) and aligned with enterprise-wide strategic goals to ensure buy-in. A possible business value statement could be: “Institutional knowledge is not lost when employees retire or leave the company because the organization has established processes to capture key information, ensuring operational effectiveness.” This business value statement incorporates the organizational challenge of tacit knowledge loss from employees’ departures and provides actionable KM solutions to combat it. With this comprehensive yet concise business value statement, the organizational stakeholder has seen the definition of KM applied in action (rather than through an isolated definition), an outlining of challenges, and a KM solution that can save the organization time and money and improve employee productivity. 

Having considered how to define KM for your organization, along with associated challenges and opportunities for KM to provide tangible value, consider the following elevator pitch-specific “best practices” to ensure that you are as concise and powerful as possible:

• Utilize a “hook:” If you were to write a headline about what benefits KM could bring to your organization, what elements would you most want to capture? Part of the art of the KM elevator pitch is encapsulating the “aboutness” of KM while also addressing the question of “how can this help our organization” into an ultra-brief, understandable statement. News headlines are famous for not only their clever wordplay, but also their ability to simultaneously convey a message while drawing readers in. Your KM elevator pitch should do the same.
• Make it memorable: We’ve all been in a presentation that could use a bit of excitement – ensure that your elevator pitch doesn’t leave people with the same sentiment. Imagine your listener taking only one thing away from this brief interaction; what would that be? (Hint: Ideally, your listener should come away from your interaction with a baseline understanding of what KM entails and how it can help address your organization’s challenges.)
• Keep it short: As the name implies, your KM elevator pitch should coherently convey your message to your fellow elevator passenger before they exit for their floor (a time span of approximately 20 to 30 seconds). The success of your elevator pitch depends on your ability to keep it short and to the point. 

The ability to concisely convey the basic elements and enduring importance of knowledge management can serve as your best marketing tool, costing virtually nothing but providing continual value. Every effective KM elevator pitch should include an applicable definition of KM as it relates to your organization, the challenges that it can mitigate, and the ways in which it provides specific business value for your enterprise. Want KM experts to help you craft your KM elevator pitch and champion KM across your organization? Connect with us here.

The post Crafting an Effective KM Elevator Pitch appeared first on Enterprise Knowledge.

Similarities of KM and RM

Records Management and Knowledge Management are part of the broader family of the information profession. Organizations rely on their RM and KM teams to adequately manage important information for their business. Both RM and KM seek to capture, categorize, and preserve different types of documents and information throughout their life cycles, so that it can later be found and leveraged by members of the organization for different business purposes. However, it is these business purposes that can create seemingly opposing directions for KM and RM.

Differences

Records Management initiatives and activities are generally driven by compliance requirements. An organization needs to keep items of information to present them to regulating bodies and other authorities if requested, or in the case of litigation. Because of this, the concept of a record is well defined, and commonly accepted to be a piece of information that serves as evidence of a business transaction, process, or activity. The RM function makes sure that not only the organization keeps a copy of the records that it needs, but also that they are easy to find and, most importantly, that they only keep records for only as long as they are needed. 

The drivers behind Knowledge Management, in contrast, tend to be more diverse. KM can be driven by an organization’s need to learn from previous initiatives, to become more efficient by not repeating work that has been done in the past, by spurring innovation, or to upskill and onboard staff faster. As opposed to the definition of records, the definition of knowledge is much broader, and usually dependent on an organization’s objectives and constraints. However, knowledge tends to be more fluid and changing in nature, as opposed to the mostly static and unchanging nature of records. 

It is because of these different purposes that two main conflicts arise when preserving information:

• Not all information will be preserved as a record. Knowledge can be embedded and codified in a number of documents and information that do not necessarily qualify as records. For instance, unused drafts, templates, and working documents are valuable from a KM perspective, but from the Records Management perspective, these may not be worthy of preservation.
• Retention schedules will trigger the disposal of useful knowledge. Within records management, best practices seek to limit an organization’s liability of legal and regulatory risk by deleting records that have reached the end of their life cycle. However, some records have knowledge embedded in them that others may find useful. The most commonly cited example is usually email. In many organizations, staff use their inbox as a personal knowledge base. However, strict disposition policies mandate that email is deleted after a few months, leaving staff feeling helpless in losing an important knowledge repository.

Reconciling Differences

So, what can we do to overcome these challenges? 

• Capture knowledge from records and enhance it. As I mentioned earlier, knowledge is highly dynamic, and can be converted from one form to another. If important knowledge is embedded in records that are bound to be deleted, it can be synthesized and converted into other formats which will either not qualify as a record, or qualify as a record but with longer retention schedules. For example, knowledge in records can be extracted and synthesized into lessons learned, updated policies, enhanced training material, and new templates. 
• Open up opportunities for new knowledge creation. Often, staff capture their knowledge in formats that may not be the most appropriate for storage, preservation, and eventual dissemination because it is easy and convenient for them to do so. As knowledge managers we need to make it simple for knowledge to deliberately and systematically be embedded in the appropriate formats. We must identify the critical moments where knowledge can be collected, and make sure that it is embedded in content that is fit for its purpose.
• Integrate change management. The prior two recommendations will require individuals to adopt new behaviors and perform unfamiliar activities. You should introduce changes clearly and deliberately so that your colleagues can understand why it is important for both themselves and the larger organization to change the way knowledge is preserved.

Is your organization seeking to put in place processes, tools, and practices to preserve your institutional knowledge? We can help. Contact us at info@enterprise-knowledge.com to start a conversation.

The post Overcoming Records Management Constraints for Knowledge Continuity appeared first on Enterprise Knowledge.

The panel will focus on Knowledge Management, current techniques and approaches, how organizations are applying KM to improve organizational efficiencies, and the future of the discipline.

Registration to the event is open, and you can register at https://www.eventbrite.com/e/rocky-mountain-sla-8th-annual-mini-conference-tickets-169027747543.

The post EK at the Rocky Mountain SLA Virtual Conference appeared first on Enterprise Knowledge.

This is the fourth consecutive year that EK has achieved a place on the Inc. 5000. Of the tens of thousands of companies that have applied to the Inc. 5000 over the years, only a fraction have made the list more than once, let alone four times in a row. In addition to being listed as one of the country’s fastest growing companies, EK was also included again this year on Inc’s list of the best workplaces in the country.

“Our fourth consecutive year on this list is a great achievement for EK, and one that belongs to each member of our growing team” said Zach Wahl, CEO of EK. “What is most important to me is that we’ve successfully sustained our culture of kindness and collaboration as we’ve grown.” 

Joe Hilger, EK COO added, “Our growth means greater depth and breadth of capabilities to serve our clients, continuing to work at the intersection of Knowledge Management, Advanced Technologies, and Enterprise Artificial Intelligence.”

“The 2021 Inc. 5000 list feels like one of the most important rosters of companies ever compiled,” says Scott Omelianuk, editor-in-chief of Inc. “Building one of the fastest-growing companies in America in any year is a remarkable achievement. Building one in the crisis we’ve lived through is just plain amazing. This kind of accomplishment comes with hard work, smart pivots, great leadership, and the help of a whole lot of people.”

About Enterprise Knowledge

Enterprise Knowledge (EK) is a services firm that integrates Knowledge Management, Information Management, Information Technology, and Agile Approaches to deliver comprehensive solutions. Our mission is to form true partnerships with our clients, listening and collaborating to create tailored, practical, and results-oriented solutions that enable them to thrive and adapt to changing needs.

Our core services include strategy, design, and development of Knowledge and Information Management systems, with proven approaches for Taxonomy and Ontology Design, Project Strategy and Road Mapping, Brand and Content Strategy, Change Management and Communication, and Agile Transformation and Facilitation. At the heart of these services, we always focus on working alongside our clients to understand their needs, ensuring we can provide practical and achievable solutions on an iterative, ongoing basis.

More about Inc. and the Inc. 5000 Methodology

Companies on the 2021 Inc. 5000 are ranked according to percentage revenue growth from 2017 to 2020. To qualify, companies must have been founded and generating revenue by March 31, 2017. They must be U.S.-based, privately held, for-profit, and independent—not subsidiaries or divisions of other companies—as of December 31, 2020. (Since then, some on the list may have gone public or been acquired.) The minimum revenue required for 2017 is $100,000; the minimum for 2020 is $2 million. As always, Inc. reserves the right to decline applicants for subjective reasons. Growth rates used to determine company rankings were calculated to three decimal places. There was one tie on this year’s Inc. 5000.  Companies on the Inc. 500 are featured in Inc.’s September issue. They represent the top tier of the Inc. 5000, which can be found at http://www.inc.com/inc5000.

About Inc. Media

The world’s most trusted business-media brand, Inc. offers entrepreneurs the knowledge, tools, connections, and community to build great companies. Its award-winning multiplatform content reaches more than 50 million people each month across a variety of channels including web sites, newsletters, social media, podcasts, and print. Its prestigious Inc. 5000 list, produced every year since 1982, analyzes company data to recognize the fastest-growing privately held businesses in the United States. The global recognition that comes with inclusion in the 5000 gives the founders of the best businesses an opportunity to engage with an exclusive community of their peers, and the credibility that helps them drive sales and recruit talent. The associated Inc. 5000 Vision Conference is part of a highly acclaimed portfolio of bespoke events produced by Inc. For more information, visit www.inc.com.

The post EK Included on Inc. 5000 for Fourth Consecutive Year appeared first on Enterprise Knowledge.

Align KM With Your Organization’s Strategic Goals

At Enterprise Knowledge (EK), we make the distinction between KM Objectives and Business Objectives. Too often, when developing a case for an investment in KM, managers focus on outcomes that prove that KM strategies were effective in connecting people to the knowledge and information they needed to complete a task or make a decision, such as, improved findability, reuse, and retention of content. Although this approach can be useful in demonstrating the success of a KM effort, it is not sufficient nor as impactful as tying those goals to the overall strategic business objectives of the organization. Business cases for KM that make the connection between KM goals and specific business goals, such as increased productivity, market share, innovation, and revenue help to pique C-suite level interest in your ideas for leveraging KM as a means to achieving the objectives that matter most to the organization.

Whether on an annual basis or every three to five years, organizations typically identify the top initiatives that will not only help them survive in a rapidly changing economy, but ideally, gain the competitive advantage they need to stay on top. As these initiatives are being formulated, take note of what initiatives are gaining momentum and make connections with the individuals in your organization who are beginning to socialize them. It’s important to make KM a part of what will eventually become the organization’s priorities over the next few years. Whether it is an initiative focused on digital transformation, innovation that leads to new products and services, operational excellence, or a focus on developing human capital, help your organization’s influencers to see how KM can set the necessary foundation or even fast track the initiatives that will ultimately become the organization’s most critical investments. 

Present Costs and Opportunity Cost

In many cases, KM has the ability to sell itself because of the clear value it adds to any organization. The greater question is how to ensure an appropriate budget is allocated to the necessary efforts to mature an organization’s KM capabilities. KM involves an investment in the internal resources who will need to direct their efforts towards the development of a strategy, design, and/or implementation plan. These efforts could be a stand-alone, targeted KM solution, such as an enterprise taxonomy or search tool, or a more comprehensive and holistic set of KM solutions that complement one another. In addition to internal resources, you’ll want to consider how much it costs to invest in getting outside expertise from consultants who specialize in these niche fields, as well as the technology enhancements or additions necessary to help enable your KM solution(s). Having a realistic estimate of the capital and operational budget you’ll need to achieve your KM goals will help you to request the appropriate amounts so that you don’t fall short of following through on the promises you’ve made to your executive team. Make sure to get guidance from those who have either funded or led similar KM efforts.

In terms of opportunity costs, don’t forget to consider the benefits you are not gaining from failing to act more quickly in addressing your organization’s KM needs. My colleague, Lulit Tesfaye, shares critical insight into The Cost of Doing Nothing when it comes to KM. For every day that goes by that you do not begin designing and implementing KM solutions, you are wasting time and money resulting from people spending far too much time looking for information or duplicating content that already exists. Without proper KM programs, institutional knowledge easily slips out of the organization when someone leaves for another job or when they retire, which results in an inordinate amount of resources spent on training and upskilling new team members. The longer an organization waits to invest in KM efforts, the more time and resources it takes to resolve and correct the issues that have now become commonplace in your organization. Decision makers should understand that your sense of urgency is directly tied to how a lack of KM is affecting the organization’s bottom line. When presenting your business case, be sure to incorporate the opportunity cost of not taking action quickly when you present the Return on Investment (ROI) for the KM efforts you are proposing. 

Provide Expected Outcomes and Benefits

In Measuring the Success of KM Digital Transformation, I share various ways to measure the success of KM efforts. A common misstep people can make is confusing the solution itself with the outcome. Be sure to make that distinction clear. For example, successfully launching a centralized Knowledge Base is a milestone worth celebrating, but the Knowledge Base is the solution. Make sure to explore what that Knowledge Base is allowing you to do that you weren’t able to do prior to its implementation. In this example, the questions you should be asking include:

• Will people no longer need to go to multiple repositories to find the resources they need? 
• Will they be able to more quickly make decisions and provide internal and external  customers with the information they need to resolve issues and take action?
• Will they spend less time looking for information and more time being productive?

By clearly outlining what people will be able to do differently as a result of your solutions, you will be able to help shed light on why KM is worth the investment. Be prepared to discuss the specific stakeholder groups in and outside of your organization and have clear explanations of what’s in it for them. Perhaps, the business development department will have better access to past proposals to help close deals faster or your customer service team will be able to increase satisfaction ratings and decrease the time it takes to close tickets. Not only does articulating the benefits of KM help you to gain buy-in for your proposed solution(s), it also helps to define the ways in which you will measure whether or not you achieved your expected outcomes.

Define How You’ll Measure Benefits Over Time

Beyond knowing what to measure, you should also think about how you will measure success. Most KM technology, including content repositories, search engines, and collaboration tools make it easy to natively track user analytics, but you’ll want to design your proposed KM Analytics Plan to produce the necessary outputs you need to report out on your progress. Oftentimes a dashboard that centralizes analytics pulled from various sources can help to tell a compelling story of how your KM efforts are moving the needle in terms of helping your organization achieve its goals. A few things to consider:

• What will you measure?
• How often will you measure?
• Are there ways to automate analytics capture and consolidation?
• What information do you want to present in your dashboard?
• Who will be responsible for ensuring that the inputs are accurate and reliable? Who will be responsible for designing and maintaining your KM Analytics dashboard?

To supplement quantitative results, also consider measuring qualitative feedback using employee surveys conducted either electronically or via interviews and focus groups. This will give you a full picture of the impact your KM efforts are having on the Key Performance Indicators (KPIs) that matter to your organization.

Support Your Case With Strategies for Success 

It’s not enough to articulate what your proposed KM solutions are and how they will benefit the organization. You’ll want to strengthen your business case by explaining the methodologies you’ll be applying in order to increase adoption of the new KM technology or ways of working. In my Agile, Design Thinking, and Change Management course, we explore tools, methods, and techniques for avoiding the common mistakes that cause KM projects to fail. These strategies include:

• Taking an Agile approach so that you’re delivering value to the organization within weeks and months rather than years.
• Using Design Thinking principles to prioritize the challenges you focus on and design your solutions in an innovative way that takes into account the experience of those who will ultimately benefit from them.
• Implementing an Integrated Change Management Plan to decrease resistance to change and increase the likelihood that people adjust to the changes you’re introducing.

What these disciplines have in common is that they take into account input and feedback from  the various individuals who should be involved throughout the process of planning, implementing, and maintaining your KM solutions. Incorporating these methodologies for success in your business case will help you to gain the trust and confidence you need from decision makers.

Present Investment Options 

Given that there is no silver bullet for KM, it can often be quite an investment to implement the foundational elements of KM along with all of the KM solutions that build on top of them to further mature your KM capabilities. Keep in mind that there is no one technology or solution that fixes all of the challenges that can be solved with KM, so your decision makers may experience sticker shock when they see how much KM costs over time. The key point to remember is that KM doesn’t necessarily require one large investment. You can offer various options for investing in KM that allow for smaller investments over time as you begin to reap the benefits of your initial investment. For example, rather than investing in a multi-year, multi-million dollar KM initiative, you can provide options for pilot projects that introduce Minimum Viable Products (MVPs) or Proofs of Concepts (PoCs) of the various KM solutions you’re proposing. It’s always good to present options to try or sample before you buy. Alternatively, you want to take into consideration the economies of scale that can be gained from taking a holistic approach to KM and investing in comprehensive effort. Be sure to lay out all of these options and share the pros and cons of choosing one over the other.

Provide Case Studies and Benchmarks

Lastly, KM sounds great in theory, but without roots in practical applications of KM principles, methods, and tools, KM projects can fail because they are too abstract or conceptual in nature. By connecting with organizations who have had similar experiences and successes with KM projects and presenting those stories to your senior managers and executives, you are able to strengthen your case for investing in something that has been proven to work in a similar organization.  If you need some starting points, check out the Case Studies and Success Stories we have on our site. Keep in mind, however, that although there are organizations that are more successful in reaping the benefits of KM, there is no single organization that is the benchmark for all things KM-related. Be specific in presenting case studies in a targeted way so that you do not set unrealistic expectations regarding your KM solutions. Take the time to speak to KM practitioners regarding their lessons learned because more often than not, the road to these successes were paved with obstacles and unexpected circumstances that were overcome. 

Conclusion

Knowledge Management is not a trend, buzz word, or fad. Although, in its history, it may have been associated with failed attempts at harnessing institutional knowledge, it is and always will be a critical element to any organization’s success. Whether you call it “KM” or not, your organization needs a strategy and plan for connecting people to the knowledge and information they need to do their job and it’s up to you to develop a case for why. If you need help developing your business case, Contact Us at Enterprise Knowledge. We’re ready to help!

The post Developing a Business Case for Knowledge Management appeared first on Enterprise Knowledge.