EK hosts a public knowledge base of over 700 articles on KM, Semantic Layer, and AI thought leadership, produces the top-rated KM podcast, Knowledge Cast, and has published the definitive book on KM benchmarking and technologies, Making Knowledge Management Clickable. 

In addition to the Top 100 List, EK was also recently recognized by KMWorld on their list of AI Trailblazers. You can read EK VP Lulit Tesfaye’s thoughts on that recognition here. These new areas of recognition come on the heels of Honda recognizing Enterprise Knowledge as one of their suppliers of the year, and Inc. Magazine listing EK as one of the best places to work in the United States.

The post EK Again Recognized as Leading Services Provider by KMWorld 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.

The Challenge

A capital producer understood the complexity of navigating international regulatory environments. Operating across nations in numerous fields of specialization, the organization had to uphold diverse and disparate ordinances, many of which have changed over time. Dedicated to providing high-quality services to their customers, the organization sought a solution that would help them better navigate revisions to compliance requirements and ensure adherence to rigorous standards of excellence.

Like many companies, the capital producer relied on manual processes to identify, track, and communicate regulations across the organization. Unfortunately, manual approaches exposed the organization to human error, a possibility that threatened its ability to remain compliant. Since regulatory adherence depends on numerous team members throughout the organization, there were various potential points of failure, many of which were unknown. Staff had to personally determine how to best share sensitive information between groups, which created inefficiencies and risked information exposure. When these processes were performed correctly, they frequently included periods of redundancy where staff members duplicated each other’s efforts, thus diminishing organizational productivity. 

The Solution

To facilitate the organization’s compliance with regulations and standards, EK provided the capital producer with a comprehensive content management strategy rooted in knowledge management (KM) best practices. EK’s recommendations were informed by 11 separate interviews, four system demos, and 28 business unit validation workshop participants. EK spoke to executive stakeholders, content owners, system owners, and process performers throughout the organization. Based on these conversations and demonstrations of the organization’s current processes, EK developed a content strategy at the intersection of content and knowledge management. Recommendations for the organization were divided into five separate workstreams, based upon EK’s proprietary content strategy for KM evaluation framework, and broken down into the strategic and business impact of each item.

Leveraging EK’s expertise in semantics and data-driven knowledge management, EK delivered a content strategy with an emphasis on the structure, metadata, and management requirements for key organizational content types. For example, contracts exist currently as unstructured content, and in this organization’s use case can continue to be managed as such. Formulas for certain products, however, require robust security and personalization to enable regulatory compliance across multiple countries. These complex requirements necessitated a recommendation for structured formula content managed in a Product Information Management System (PIMS). 

Additionally, EK created a technology solution approach that not only identified existing pain points in the organization but also mapped each challenge to a corresponding technology solution. EK prioritized technical approaches that could easily work within the capital producer’s current technical ecosystem, minimizing the cost of integrating these solutions. At the start of the engagement, the organization was leveraging SharePoint for all content management needs. EK’s technology recommendations included strategies to optimize the use of SharePoint for appropriate use cases as well as recommendations for specialized contract management systems for product lifecycle management and contract management.

Implementing technological and procedural changes within the capital producer will allow the organization to continue to grow globally while providing compliant and high-quality products for its consumers. EK’s proposed content management approach will enable staff to better create, protect, share, and utilize compliance content to ensure the seamless continuity of operations, establish secure intellectual property, and achieve operational efficiencies. 

The EK Difference

Our team worked closely with the organization’s stakeholders to produce a content management strategy that would help them achieve larger knowledge objectives. Establishing processes and avenues for information sharing will enable the organization to not only uphold international standards of compliance but also increase productivity over time by efficiently sharing information and preserving tacit knowledge.

This engagement operated within the intersection of content management and KM. EK leveraged its KM background to guide this content strategy approach and used KM best practices to conduct knowledge-gathering activities, including document review, stakeholder interviews, stakeholder workshops, and system demos. After reviewing this information, EK was able to use its proprietary current state and target state framework to conduct a content management analysis at the organization. 

EK additionally utilized an ontological data modeling approach to guide its advanced content management strategy. The capital producer was exclusively a document-based organization at the beginning of the engagement; with EK’s support, they identified a future-ready content strategy for prioritized content use cases. There are a variety of content management approaches that can be used to provide structure to digital materials. These methods can be viewed on a continuum from file-level management to semantically enriched component management. However, not all approaches are the right fit for every client. Our content strategy and operations experts were able to ascertain the right level of content management for various use cases at the organization and ultimately provide them with a detailed technical plan for how to implement the right content management strategy. 

The Results

At the end of the engagement, EK provided the organization with a clear roadmap for the adoption of a transformational content management strategy. Stakeholders from over ten different business units aligned on an approach that addressed their various needs and pain points, as well as an understanding of the investment required to achieve the target state content strategy. 

EK provided the organization’s stakeholders with the roadmap for a long-term vision and the tools for a quick return on investment. This came in five key accelerators, allowing the organization to deploy strategies, frameworks, and management approaches tailored to the organization’s unique needs. Each accelerator included a description of the recommendation, a path to implement the task successfully, success indicators to track, and the corresponding pain points it addressed. 

By implementing a more robust content management strategy, the capital producer will maintain compliance with regulations and standards, ensure content is secure and only accessible to those who need it, and improve overall efficiency of content operations. 

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Sustainable organizations are also more internally efficient. In a UCLA study of over 5,000 companies, environmental economists found that organizations that voluntarily adopt international “green” practices and standards have employees who are, on average, 16% more productive. And technological solutions, when paired with knowledge management (KM) practices, can create new pathways to grow sustainability at your organization.

KM can be used to achieve sustainability while simultaneously developing an organization’s technical capabilities. Our clients have used a variety of KM practices to become more sustainable, including:

• Pruning back the scale of content;
• Rooting sustainable practices in user behavior by promoting environmentally-conscious practices;
• Sprouting a green system by optimizing computational pipelines; and
• Leveraging semantics so they don’t miss the forest for the trees.

In this blog, I will discuss how EK has used these techniques to help three different organizations achieve their sustainability goals. 

Technique 1: Prune Back the Scale of Content

Unsure about how content is affecting your organization’s carbon footprint? Consider the scale of your content. Content increases carbon emissions because of the energy required to create, store, and access the materials. A higher volume of content necessitates more energy—thus generating more carbon emissions—to utilize. 

Removing duplicate content can also benefit organizations financially. Since organizations experience per-gigabyte (GB) data expenses, duplicate content can become costly, especially considering their deteriorative effect on overall data quality. If a file exists in multiple versions with different editors, it is nearly impossible to discern which file is the most up-to-date. An organization could easily rely on the wrong file for information, reducing accuracy and efficiency. 

Pruning back the scale of content benefits organizations from both a financial and environmental lens. Investigating the scale of asset libraries is a first step in this process, which can be performed by using semantic applications. 

Technique 2: Root Sustainable Practices in User Behavior

Users at an organization can inadvertently perpetuate an unsustainable content culture. Duplicate content most commonly emerges when employees save multiple copies of the same file to their devices, construct new files to replace forgotten ones, or use separate files for different document versions. Users may not consciously perpetuate these practices; however, the effects are damaging. Saving and storing unnecessary content has significant environmental impacts as it collects over time, where storing hundreds of GBs of data can equate to large amounts of carbon dioxide released.

Adjusting user behavior can prevent the unnecessary collection of data. When organizations provide visibility to their employees on their individual carbon footprint, users become accountable for their own environmental efforts and naturally decrease any unsustainable behavior.

Technique 3: Sprout a Green System Through the Optimization of Computational Pipelines

AI produces substantial environmental costs, through the electricity required to run data centers and the water necessary to cool them. Many organizations face a conflict between their desire to technologically mature by implementing AI, and their concern about the environmental impacts of doing so. 

Organizations can mitigate the environmental impact of their AI use and corresponding carbon emissions by reducing the computing power used in AI. This can be accomplished by making thoughtful decisions when designing AI architecture. How data is stored and transferred, and where servers are located, can have small but meaningful impacts on the sustainability of an AI solution. Your choice of cloud architecture also matters: powering AI models with renewable energy can promote sustainability.

Technique 4: Leverage Existing Data So You Don’t Miss the Forest for the Trees

Some organizations may have already launched sustainability activities. However, these solutions may not be optimized to maximize environmental outputs. When information is inconsistently tagged, siloed, or forgotten, it becomes lost to the organization, hurting otherwise well-planned ecological efforts. 

Organizations can maximize the effectiveness of their environmental campaigns by introducing technological solutions that promote visibility, insights, and accuracy about the ecological impact of their initiatives. Solutions such as a semantic layer can be leveraged to enhance organizational efficiency towards promoting sustainability. 

Conclusion

Sustainability has several benefits for an organization: it promotes productivity in the workforce, attracts high-quality employees, raises employee satisfaction overall, and increases the marketability of a company’s services. KM practices can help your organization achieve these outcomes while simultaneously reducing storage costs and streamlining client data. 

Enterprise Knowledge possesses expertise in making technical solutions that are both mature and sustainable. Our use of semantic applications and green data practices has supported environmental initiatives for various clients, and can help your organization to reach ambitious sustainability goals as well. Contact us today if you would like assistance in using KM to become environmentally friendly as an organization.

The post How Sustainability Grows from KM appeared first on Enterprise Knowledge.

The Challenge

Enterprise Knowledge (EK) recently worked with a Federally Funded Research and Development Center (FFRDC) that was having difficulty retrieving relevant content in a large volume of archival scientific papers. Researchers were burdened with excessive search times and the potential for knowledge loss when target documents could not be found at all. To learn more about the client’s use case and EK’s initial strategy, please see the first blog in the Optimizing Historical Knowledge Retrieval series: Standardizing Metadata for Enhanced Research Access.

To make these research papers more discoverable, part of EK’s solution was to add “about-ness” tags to the document metadata through a classification process. Many of the files in this document management system (DMS) were lower quality PDF scans of older documents, such as typewritten papers and pre-digital technical reports that often included handwritten annotations. To begin classifying the content, the team first needed to transform the scanned PDFs into machine-readable text. EK utilized an Optical Character Recognition (OCR) tool, which can “read” non-text file formats for recognizable language and convert it into digital text. When processing the archival documents, even the most advanced OCR tools still introduced a significant amount of noise in the extracted text. This frequently manifested as:

• A table, figure, or handwriting in the document being read in as random symbols and white space.
• Inserting random punctuation where a spot or pen mark may have been on the file, breaking up words and sentences.
• Excessive or misplaced line breaks separating related content.
• Other miscellaneous irregularities in the text that make the document less comprehensible.

The first round of text extraction using out-of-the-box OCR capabilities resulted in many of the above issues across the output text files. This starter batch of text extracts was sent to the classification model to be tagged. The results were assessed by examining the classifier’s evidence within the document for tagging (or failing to tag) a concept. Through this inspection, the team found that there was enough clutter or inconsistency within the text extracts that some irrelevant concepts were misapplied and other, applicable concepts were being missed entirely. It was clear from the negative impact on classification performance that document comprehension needed to be enhanced.

Auto-Classification Auto-Classification (also referred to as auto-tagging) is an advanced process that automatically applies relevant terms or labels (tags) from a defined information model (such as a taxonomy) to your data. Read more about Enterprise Knowledge’s auto-tagging solutions here: 4 Steps to Content Auto-Classification with High Accuracy Expert Analysis: When should my organization use auto-tagging? Part One Knowledge AI: Content Recommender and Chatbot Powered by Auto-Tagging and an Enterprise Knowledge Graph A Guide to Selecting the Right Auto-Tagging Approach

The Solution

To address this challenge, the team explored several potential solutions for cleaning up the text extracts. However, there was concern that direct text manipulation might lead to the loss of critical information if blanket applied to the entire corpus. Rather than modifying the raw text directly, the team decided to leverage a client-side Large Language Model (LLM) to generate additional text based on the extracts. The idea was that the LLM could potentially better interpret the noise from OCR processing as irrelevant and produce a refined summary of the text that could be used to improve classification.

The team tested various summarization strategies via careful prompt engineering to generate different kinds of summaries (such as abstractive vs. extractive) of varying lengths and levels of detail. The team performed a human-in-the-loop grading process to manually assess the effectiveness of these different approaches. To determine the prompt to be used in the application, graders evaluated the quality of summaries generated per trial prompt over a sample set of documents with particularly low-quality source PDFs. Evaluation metrics included the complexity of the prompt, summary generation time, human readability, errors, hallucinations, and of course – precision of  auto-classification results.

The EK Difference

Through this iterative process, the team determined that the most effective summaries for this use case were abstractive summaries (summaries that paraphrase content) of around four complete sentences in length. The selected prompt generated summaries with a sufficient level of detail (for both human readers and the classifier) while maintaining brevity. To improve classification, the LLM-generated summaries are meant to supplement the full text extract, not to replace it. The team incorporated the new summaries into the classification pipeline by creating a new metadata field for the source document. The new ‘summary’ metadata field was added to the auto-classification submission along with the full text extracts to provide additional clarity and context. This required adjusting classification model configurations, such as the weights (or priority) for the new and existing fields.

Large Language Models (LLMs) A Large Language Model is an advanced AI model designed to perform Natural Language Processing (NLP) tasks, including interpreting, translating, predicting, and generating coherent, contextually relevant text. Read more about how Enterprise Knowledge is leveraging LLMs in client solutions here: What is a Large Language Model (LLM)? Choosing the Right Approach: LLMs vs. Traditional Machine Learning for Text Summarization The Role of Semantic Layers with LLMs

The Results

By including the LLM-generated summaries in the classification request, the team was able to provide more context and structure to the existing text. This additional information filled in previous gaps and allowed the classifier to better interpret the content, leading to more precise subject tags compared to using the original OCR text alone. As a bonus, the LLM-generated summaries were also added to the document metadata in the DMS, further improving the discoverability of the archived documents.

By leveraging the power of LLMs, the team was able to clean up noisy OCR output to improve auto-tagging capabilities as well as further enriching document metadata with content descriptions. If your organization is facing similar challenges managing and archiving older or difficult to parse documents, consider how Enterprise Knowledge can assist in optimizing your content findability with advanced AI techniques.

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The Challenge

One of the top global leaders in automotive manufacturing faced significant challenges in managing and accessing critical knowledge across its diverse teams. The company’s employees were working in silos and struggling with fragmented data spread across various departments and business units. Employees relied heavily on personal networks to find information, overburdening subject matter experts and creating bottlenecks during pivotal innovation phases. They consistently spent significant time searching for technical specifications, design documents, and previous project insights. Further, employees did not trust the integrity of the information available to them, limiting their ability to reuse past information efficiently. The company engaged Enterprise Knowledge (EK) to conduct a Knowledge Management (KM) Strategy and solution implementation project plan after the failure of multiple KM initiatives. The engagement’s long-term goal is to establish a shared Knowledge Management System (KMS) to streamline access to crucial information, better leverage experts’ institutional knowledge and experience, and decrease new employees’ time to proficiency.

The Solution

While the company originally sought a single platform to solve all KM challenges, EK’s assessment and collaboration identified a more integrated approach, leveraging existing systems, KM best practices, and semantic foundations. This initial 9-month engagement covered KM organizational design and governance, taxonomy and ontology development, as part of a scalable semantic layer technology architecture, UI/UX design, and a knowledge graph to drive long-term KMS adoption and sustainability. 

Evolving into a multi-year KM transformation, EK focused on creating a strategic and technical framework to drive sustainable KM practices. This phase centered on developing a clear roadmap for KM improvement, designing a KMS proof of concept (PoC), and ensuring the solutions aligned with the company’s evolving needs. The envisioned KMS would serve as a centralized knowledge portal, aggregating multiple applications and platforms to offer a single point of access to a holistic, connected view of the information that employees need to effectively perform their work. The following activities laid the foundation for a fully integrated system and long-term success:

Understanding Business Needs and Requirements
The first phase of the project focused on understanding the company’s business, technical, and functional requirements for the KMS. In a matter of weeks, EK engaged with 100+ select employees (representative of their 24,000-person workforce) and evaluated more than 10 business-critical systems using a hybrid approach that combined top-down (focus groups, interviews, and technical demos) and bottom-up (content and data analysis) research methods.

Insights to Strategy and Roadmap
These inputs informed a comprehensive KM strategy assessment that evaluated the company’s current KM maturity. Combined with inputs from their leadership, EK developed a tailored three-year roadmap for improvement with a focus on three key areas: content governance, user engagement, and knowledge sharing.

Establishing a Sustainable Operating Model
To support long-term sustainability, EK designed a KM operating model that provided a detailed framework to operationalize a KM Center of Excellence (CoE). The KM Org Function & Operating Model included dedicated KM roles and business unit representatives tasked with driving adoption and embedding KM practices across the organization.

Developing the Knowledge Portal PoC
In parallel, EK designed and deployed a Knowledge Portal PoC hosted in the company’s AWS environment. The portal was powered by a knowledge graph and a taxonomy and ontology management solution, consolidating information from multiple systems into a single landing page. The interface was developed using design thinking principles to ensure intuitive navigation and ease of use.

User-Centered Design and Testing
EK facilitated extensive discovery sessions with a variety of stakeholders to define user personas and journey maps. The team tested a clickable prototype and continuously refined the PoC based on stakeholder feedback, ensuring the solution reflected real-world user needs.

Scalable Technical Architecture
To support future growth, EK also provided technical architecture recommendations designed to scale with the company’s data demands. These recommendations were anchored in the semantic layer and security standards to ensure the solution can integrate seamlessly with existing systems, deliver reliable performance, and accommodate advanced AI capabilities over time.

The EK Difference

EK prioritizes iterative, user-driven, sustainable solutions while demonstrating dynamic responsiveness to client needs. Engaging a diverse cross-section of employees, EK leveraged expertise in KM and design thinking to facilitate virtual and onsite sessions, bringing together over 100 employees from various business units efficiently to capture diverse user perspectives. To minimize the level of effort and time from employees, EK employed a variety of validation activities to collect user feedback to refine deliverables and align them with company milestones and leadership briefings. Leveraging an Agile framework, EK held regular reviews, providing visual updates and executive briefings for incremental, efficient processes aligned with strategic goals. 

To promote long-term adoption and cultural change, EK embedded knowledge transfer into every project phase, conducting ongoing working sessions to upskill employees in KM principles, practical system use, and day-to-day maintenance. These sessions were designed to build immediate capability and empower employees to integrate KM practices into how they work moving forward. EK also equipped stakeholders with tailored educational materials and actionable training recommendations to support continuous KM growth. These efforts fostered stronger user ownership and helped lay the foundation for a sustainable, self-sufficient KM culture beyond the project’s completion. 

To meet the complexity of the company’s KM goals, EK assembled a multidisciplinary team capable of bridging business, technical, and functional perspectives. The team included software engineers, KM specialists, taxonomy and ontology experts, and UI/UX designers, each bringing unique expertise to translate complex requirements into actionable components. This cross-functional structure enabled EK to offer integrated recommendations that addressed both technical implementation and non-technical KM strategies. Working across simultaneous workstreams, the team maintained steady progress while ensuring alignment between business needs and system design. As priorities evolved, EK expanded its team to include a dedicated UI/UX design group, focused on crafting an interface tailored to the company’s specific context. The iterative design approach allowed for ongoing refinement, ensuring the KMS fit seamlessly within the company’s environment and supported long-term adoption.

The Results

By the end of the first phase, EK positioned the company to take decisive steps toward long-term KM maturity. The comprehensive three-year KM Strategy Roadmap clarified and prioritized the company’s most pressing KM challenges, offering a phased path forward grounded in its unique business context. The KM Org Function & Operating Model and Governance Plan define the resourcing, roles, and decision-making structures needed to embed KM into the company, ultimately helping leadership identify where to upskill, hire, or realign talent to support KM goals within their current structure.

To accelerate adoption and ensure stakeholder alignment, EK also deployed a Knowledge Portal PoC in the company’s Cloud environment. This allowed staff to experience core portal functionalities, such as integrated project views and intuitive search, and provide input on usability, informing future enhancements. Behind the scenes, EK’s semantic layer framework (taxonomy/ontology and knowledge graph models) laid the groundwork for smarter data connections, improving content findability and relevance in ways that resonate with end users.

The company leadership acknowledged the approach and priority—funding the implementation and next phases of the program and engagement. With the project extended over three years, EK continues to  partner with the company to help transition the PoC into a fully operational production system, providing employees with a reliable “single view of truth.” EK will support onboarding and training for the KM CoE, equipping its members to lead and champion KM efforts company-wide. Additionally, KM best practices and governance will be scaled across the broader organization, strengthening consistency and sustainability.

Looking ahead, EK will introduce advanced KMS capabilities such as natural language processing, AI-powered chatbot support, and personalized content recommendations. These capabilities will transform how employees access and apply knowledge and position the company and its employees for greater agility and innovation as a leader in the automotive industry.

Interested in maturing your organization’s knowledge management? Contact us today!

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The post Establishing a Scalable Knowledge Management Strategy and Solution Framework for a Leading Automotive Manufacturing Company: A Case Study appeared first on Enterprise Knowledge.

As part of our work, we’ve endeavored to help our clients understand the expansive nature of their knowledge, content, and data. The complete range of these materials can be considered based on several different spectra. They can range from tacit to explicit, knowledge to information, structured to unstructured, digital to analog, internal to external, and originated to generated. Before we go deeper into the definition of knowledge assets, let’s first explore each of these variables to understand how vast the full collection of knowledge assets can be for an organization.

• Tacit and Explicit – Tacit content is held in people’s heads. It is inferred instead of explicitly encoded in systems, and does not exist in a shareable or repeatable format. Explicit content is that which has been captured in an independent form, typically as a digital file or entry. Historically, organizations have been focused on converting tacit knowledge to explicit so that the organization could better maintain and reuse it. However, we’ll explain below how the complete definition of a knowledge asset shifts that thinking somewhat.
• Knowledge and Information – Knowledge is the expertise and experience people acquire, making it extremely valuable but hard to convert from tacit to explicit. Information is just facts, lacking expert context. Organizations have both, and documents often mix them.
• Structured and Unstructured – Structured information is machine-readable and system-friendly and unstructured information is human-readable and context-rich. Structured data, like database entries, is easy for systems but hard for humans to understand without tools. Unstructured data, designed for humans, is easier to grasp but historically challenging for machines to process. 
• Digital to Analog – Digital information exists in an electronic format, whereas analog information exists in a physical format. Many global organizations are sitting on mountains of knowledge and information that isn’t accessible (or perhaps even known) to most people in the organization. Making things more complex, there’s also formerly analog information, the many old documents that have been digitized but exist in a middle state where they’re not particularly machine-readable, but are electronic.
• Internal to External – Internal content targets employees, while external content targets customers, partners, or the public, with differing tones and styles, and often greater governance and overall rigor for external content. Both types should align, but are treated differently. You can also consider the content created by your organization versus external content purchased, acquired, or accessed from external sources. From this perspective, you have much greater control over your organization’s own content than that which was created or is owned externally.
• Originated and Generated – Originated content already exists within the organization as discrete items within a repository or repositories, authored by humans. Explicit content, for example, is originated. It was created by a person or people, it is managed, and identified as a unique item. Any file you’ve created before the AI era falls into this category. With Generative AI becoming pervasive, however, we must also consider generated information, derived from AI. These generated assets (synthetic assets) are automatically created based on an organization’s existing (originated) information, forming new content that may not possess the same level of rigor or governance.

If we were to go no further than the above, most organizations would already be dealing with petabytes of information and tons of paper encompassing years and years. However, by thinking about information based on its state (i.e. structured or unstructured, digital or analog, etc), or by its use (i.e. internal or external), organizations are creating artificial barriers and silos to knowledge, as well as duplicating or triplicating work that should be done at the enterprise level. Unfortunately, for most organizations, the data management group defines and oversees data governance for their data, while the content management group defines and oversees content governance for their content. This goes beyond inefficiency or redundancy, creating cost and confusion for the organization and misaligning how information is managed, shared, and evolved. Addressing this issue, in itself, is already a worthy challenge, but it doesn’t yet fully define a knowledge asset or how thinking in terms of knowledge assets can deliver new value and insights to an organization.

If you go beyond traditional digital content and begin to consider how people actually want to obtain answers, as well as how artificial intelligence solutions work, we can begin to think of the knowledge an organization possesses more broadly. Rather than just looking at digital content, we can recognize all the other places, things, and people that can act as resources for an organization. For instance, people and the knowledge and information they possess are, in fact, an asset themselves. The field of KM has long been focused on extracting that knowledge, with at best mixed results. However, in the modern ecosystem of KM, semantics, and AI, we can instead consider people themselves as the asset that can be connected to the network. We may still choose to capture their knowledge in a digital form, but we can also add them to the network, creating avenues for people to find them, learn from them, and collaborate with them while mapping them to other assets.

In the same way, products, equipment, processes, and facilities can all be considered knowledge assets. By considering all of your organizational components not as “things,” but as containers of knowledge, you move from a world of silos to a connected and contextualized network that is traversable by a human and understandable by a machine. We coined the term knowledge assets to express this concept. The key to a knowledge asset is that it can be connected with other knowledge assets via metadata, meaning it can be put into the organization’s context. Anything that can hold metadata and be connected to other knowledge assets can be an asset.

Another set of knowledge assets that are quickly becoming critical for mature organizations are components of AI orchestration. As organizations build increasingly complex systems of agents, models, tools, and workflows, the logic that governs how these components interact becomes a form of operational knowledge in its own right. These orchestration components encode decisions, institutional context, and domain expertise, meaning they are worthy of being treated as first-class knowledge assets. To fully harness the value of AI, orchestration components should be clearly defined, governed, and meaningfully connected to the broader knowledge ecosystem.

Put into practice, a mature organization could create a true web of knowledge assets to serve virtually any use case. Rather than a simple search, a user might instead query their system to learn about a process. Instead of getting a link to the process documentation, they get a view of options, allowing them to read the documentation, speak to an expert on the topic, attend training on the process, join a community of practice working on it, or visit an application supporting it. 

A new joiner to your organization might be given a task to complete. Currently, they may hunt around your network for guidance, or wait for a message back from their mentor, but if they instead had a traversable network of all your organization’s knowledge assets, they could begin with a simple search on the topic of the task, find a past deliverable from a related task, which would lead them to the author of that task from whom they could seek guidance, or instead to an internal meetup of professionals deemed to have expertise in that task.

If we break these silos down, add context and meaning via metadata, and begin to treat our knowledge assets holistically, we’re also creating the necessary foundations for any AI solutions to better understand our enterprise and deliver complete answers. This means that we’re building the better answer for our organization immediately, while also enabling our organization to leverage AI capabilities faster, more consistently, and more reliably than others.

The idea of knowledge assets will be a shift both in mindset and strategies, with impacts potentially rippling deeply through your org chart, technologies, and culture. However, the organizations that embrace this concept will achieve an enterprise most closely resembling how humans naturally think and learn and how AI is best equipped to deliver.

If you’re ready to take the next big step in organizational knowledge and maturity, contact us, and we will bring all of our knowledge assets to bear in support. 

The post What is a Knowledge Asset? appeared first on Enterprise Knowledge.

In this presentation, DeMay and Anna explored the World Wildlife Fund’s (WWF) approach to developing its knowledge management strategy from the ground up. They focused on the organization’s initial challenges, such as disparate systems and siloed information. They highlighted WWF’s strategy for overcoming these obstacles, emphasizing the integration of people, processes, and technology to craft a roadmap aligned with WWF’s organizational goals.

They discussed WWF’s proactive efforts to foster a knowledge-sharing culture, define clear roles, and implement a governance structure that enhances content management across a distributed team of over 1,900 employees. They also addressed the vital role of change management, sharing techniques for navigating resistance and securing buy-in through executive sponsorship and grassroots advocacy.

Participants in this session gained insights into:

• Key challenges and strategies for building a KM program from scratch;
• The importance of aligning KM initiatives with organizational goals;
• Techniques for fostering a knowledge-sharing culture and managing content; and
• How to drive sustainable change with effective communication, training, and support.

The post Nurturing Knowledge – A Journey in Building a KM Program from Scratch: A Case Study appeared first on Enterprise Knowledge.

The field of Knowledge Management continues to experience a period of rapid evolution, and with it, growing opportunity to redefine value and reorient decision-makers and stakeholders toward the business value the field offers. With the nature of work continuing to evolve in a post-Covid world, the “AI Revolution” dominating conversations and instances of Generative AI seemingly everywhere, and the field of Knowledge, Information, Data, and Content Management continuing to connect in new ways, Knowledge Management continues to evolve. 

As in years past, my annual report on Top Knowledge Management Trends for 2025 is based on an array of factors and inputs. As the largest global KM consultancy, EK is in a unique position to identify where KM is and where it is heading. Along with my colleagues, I interview clients and map their priorities, concerns, and roadmaps. We also sample the broad array of requests and inquiries we receive from potential clients and analyze various requests for proposal and information (RFPs and RFIs). In addition, we attend conferences not just for KM, both more broadly across industries and related fields to understand where the “buzz” is. I then supplement these and other inputs with interviews from leaders in the field and inputs from EK’s Expert Advisory Board (EAB). From that, I identify what I see as the top trends in KM.

You can review each of these annual blogs for 2024, 2023, 2022, 2021, 2020, and 2019 to get a sense of how the world of KM has rapidly progressed and to test my own track record. Now, here’s the list of the Top Knowledge Management trends for 2025.

1) AI-KM Symbiosis – Everyone is talking about AI and we’re seeing massive budgets allocated to make it a reality for organizations, rather than simply something that demonstrates well but generates too many errors to be trusted. Meanwhile, many KM practitioners have been asking what their role in the world of AI will be. In last year’s KM Trends blog I established the simple idea that AI can be used to automate and simplify otherwise difficult and time-consuming aspects of KM programs, and equally, KM design and governance practices can play a major role in making AI “work” within organizations. I doubled down on this idea during my keynote at last year’s Knowledge Summit Dublin, where I presented the two sides of the coin, KM for AI, and AI for KM, and more recently detailed this in a blog while introducing the term Knowledge Intelligence (KI).

In total, this can be considered as the mutually beneficial relationship between Artificial Intelligence and Knowledge Management, which all KM professionals should be seizing upon to help organizations understand and maximize their value, and for which the broader community is quickly becoming aware. Core KM practices and design frameworks address many of the reliability, completeness, and accuracy issues organizations are reporting with AI – for instance, taxonomy and ontology to enable context and categorization for AI, tacit knowledge capture and expert identification to deliver rich knowledge assets for AI to leverage, and governance to ensure the answers are correct and current. 

AI, on the other hand, delivers inference, assembly, delivery, and machine learning to speed up and automate otherwise time intensive human-based tasks that were rife with inconsistencies. AI can help to deliver the right knowledge to the right people at the moment of need through automation and inference, it can automate tasks like tagging, and even improve tacit knowledge capture, which I cover below in greater detail as a unique trend.

2) AI-Ready Content – Zeroing in on one of the greatest gaps in high-performing AI systems, a key role for KM professionals this year will be to establish and guide the processes and organizational structures necessary to ensure content ingested by an organization’s AI systems is connectable and understandable, accurate, up-to-date, reliable, and eminently trusted. There are several layers to this, in all of which Knowledge Management professionals should play a central role. First is the accuracy and alignment of the content itself. Whether we’re talking structured or unstructured, one of the greatest challenges organizations face is the maintenance of their content. This has been a problem long before AI, but it is now compounded by the fact that an AI system can connect with a great deal of content and repackage it in a way that potentially looks new and more official than the source content. What happens when an AI system is answering questions based on an old directive, outdated regulation, or even completely wrong content? What does it do if it finds multiple conflicting pieces of information? This is where “hallucinations” start appearing, with people quickly losing trust in AI solutions.

In addition to the issues of quality and reliability, there are also content issues related to structure and state. AI solutions perform better when content in all forms has been tagged consistently with metadata and certain systems and use cases benefit from consistent structure and state of content as well. For organizations that have previously invested in their information and data practices, leveraging taxonomies, ontologies, and other information definition and categorization solutions, trusted AI solutions will be a closer reality. For the many others, this must be an area of focus.

Notably, we’ve even seen a growing number of data management experts making a call for greater Knowledge Management practices and principles in their own discipline. The world is waking up to the value of KM. In 2025, there will be a growing priority on this age-old problem of getting an organization’s content, and content governance, in order so that those materials surfaced through AI will be consistently trusted and actionable.

3) Filling Knowledge Gaps – All systems, AI-driven or otherwise, are only as smart as the knowledge they can ingest. As systems leverage AI more and transcend individual silos to operate for the entire enterprise, there’s a great opportunity to better understand what people are asking for. This goes beyond analytics, though that is a part of it, but rather focuses on an understanding of what was asked that couldn’t be answered. Once enterprise-level knowledge assets are united, these AI and Semantic Layer solutions have the ability to identify knowledge gaps. 

This creates a massive opportunity for Knowledge Management professionals. A key role of KM professionals has always been to proactively fill these knowledge gaps, but in so many organizations, simply knowing what you don’t know is a massive feat in itself. As systems converge and connect, however, organizations will suddenly have an ability to spot their knowledge gaps as well as their potential “single points of failure,” where only a handful of experts possess critical knowledge within the organization. This new map of knowledge flows and gaps can be a tool for KM professionals to prioritize filling the most critical gaps and track their progress for the organization. This in turn can create an important new ability for KM professionals to demonstrate their value and impact for organizations, showing how previously unanswerable questions are now addressed and how past single points of failure no longer exist. 

To paint the picture of how this works, imagine a united organization that could receive regular, automated reports on the topics for which people were seeking answers but the system was unable to provide. The organization could then prioritize capturing tacit knowledge, fostering new communities of practice, generating new documentation, and building new training around those topics. For instance, if a manufacturing company had a notable spike in user queries about a particular piece of equipment, the system would be able to notify the KM professionals, allowing them to assess why this was occurring and begin creating or curating knowledge to better address those queries. The most intelligent systems would be able to go beyond content and even recognize when an organization’s experts on a particular topic were dwindling to the point that a future knowledge gap might exist, alerting the organization to enhance knowledge capture, hiring, or training. 

4) AI-Assisted Tacit Knowledge Capture – Since the late 1990’s, I’ve seen people in the KM field seek to automate the process of tacit knowledge capture. Despite many demos and good ideas over the decades, I’ve never found a technical solution that approximates a human-driven knowledge capture approach. I believe that will change in the coming years, but for now the trend isn’t automated knowledge capture, it is AI-assisted knowledge capture. There’s a role for both KM professionals and AI solutions to play in this approach. The human’s responsibilities are to identify high value moments of knowledge capture, understand who holds that knowledge and what specifically we want to be able to answer (and for whom), and then facilitate the conversations and connect to have that knowledge transferred to others. 

That’s not new, but it is now scalable and easier to digitize when AI and automation are brought into the processes. The role of the AI solution is to record and transcribe the capture and transfer of knowledge, automatically ingesting the new assets into digital form, and then leveraging it as part of the new AI body of knowledge to serve up to others at the point of need. By again considering the partnership between Knowledge Management professionals and the new AI tools that exist, practices and concepts that were once highly limited to human interactions can be multiplied and scaled to the enterprise, allowing the KM professional to do more that leverages their expertise, and automating the drudgery and low-impact tasks.

5) Enterprise Semantic Layers – Last year in this KM Trends blog, I introduced the concept of the Semantic Layer. I identified it as the next step for organizations seeking enterprise knowledge capabilities beyond the maturity of knowledge graphs, as a foundational framework that can make AI a reality for your organization. Over the last year we saw that term enter firmly into the conversation and begin to move into production for many large organizations. That trend is already continuing and growing in 2025. In 2025, organizations will move from prototyping and piloting semantic layers to putting them into production. The most mature organizations will leverage their semantic layers for multiple different front-end solutions, including AI-assisted search, intelligent chatbots, recommendation engines, and more.

6) Access and Entitlements – So what happens when, through a combination of semantic layers, enterprise AI, and improved knowledge management practices an organization actually achieves what they’ve been seeking and connects knowledge assets of all different types, spread across the enterprise in different systems, and representing different eras of the organization? The potential is phenomenal, but there is also a major risk. Many organizations struggle mightily with the appropriate access and entitlements to their knowledge assets. Legacy file drives and older systems possess dark content and data that should be secured but isn’t. This largely goes unnoticed when those materials are “hidden” by poor findability and confused information architectures. All of a sudden, as those issues melt away thanks to AI and semantic layers, knowledge assets that should be secured will be exposed. Though not specifically a knowledge management problem, the work of knowledge managers and others within organizations to break down silos, connect content in context, and improve enterprise findability and discoverability will surface this security and access issue. It will need to be addressed proactively lest organizations find themselves exposing materials they shouldn’t. 

I anticipate this will be a hard lesson learned for many organizations in 2025. As they succeed in the initial phases of production AI and semantic layer efforts, there will be unfortunate exposures. Rather than delivering the right knowledge to the right people, the wrong knowledge will be delivered to the wrong people. The potential risk and impact for this is profound. It will require KM professionals to help identify this risk, not solve it independently, but partner with others in an organization to recognize it and plan to avoid it.

7) More Specific Use Cases (and Harder ROI) – In 2024, we heard a lot of organizations saying “we want AI,” “we need a semantic layer,” or “we want to automate our information processes.” As these solutions become more real and organizations become more educated about the “how” and “why,” we’ll see growing maturity around these requests. Rather than broad statements about technology and associated frameworks, we’ll see more organizations formulating cohesive use cases and speaking more in terms of outcomes and value. This will help to move these initiatives from interesting nice-to-have experiments to recession-proof, business critical solutions. The knowledge management professionals’ responsibility is to guide these conversations. Zero your organization in on the “why?” and ensure you can connect the solution and framework to the specific business problems they will solve, and then to the measurable value they will deliver for the organization.

Knowledge Management professionals are poised to play a major role in these new KM Trends. Many of them, as you read above, pull on long-standing KM responsibilities and skills, ranging from tacit knowledge capture, to taxonomy and ontology design, as well as governance and organizational design. The most successful KM’ers in 2025 will be those that merge these traditional skillsets with a deeper understanding of semantics and their associated technologies, continuing to connect the fields of Knowledge, Content, Information, and Data Management as the connectors and silo busters for organizations.

Where does your organization currently stand with each of these trends? Are you in a position to ensure you’re at the center of these solutions for your organization, leading the way and ensuring knowledge assets are connected and delivered with high-value and high-reliability context? Contact us to learn more and get started.

The post Top Knowledge Management Trends – 2025 appeared first on Enterprise Knowledge.