We’re excited to continue to release these discussions for viewing: next up, a panel moderated by Barry Byrne of Novartis, featuring Kurt Kragh Sørensen (Novartis), Daan Hannessen (Shell), and Sara Mae O’Brien-Scott (Enterprise Knowledge). And check out Daan’s pre-SLS Knowledge Cast episode!

Panel – Knowledge Graphs: The Rising Star of the Knowledge Management Toolkit

Panel Moderator: Barry Byrne (Novartis)
Panelists: Kurt Kragh Sørensen (Novartis) & Daan Hannessen (Shell) & Sara Mae O’Brien-Scott (Enterprise Knowledge)

Leading organizations are increasingly turning to knowledge graphs to connect information, enable intelligent discovery, and unlock new business value. In this panel, world-class practitioners share real stories of how they have implemented knowledge graphs as part of their knowledge management strategies. Expect practical lessons, proven approaches, and insights into why graphs are quickly becoming an essential part of the enterprise toolkit.

The post Semantic Layer Symposium 2025: Knowledge Graphs Panel — The Rising Star of the Knowledge Management Toolkit appeared first on Enterprise Knowledge.

We’re excited to release these discussions for viewing, starting with Ben Clinch of Ortecha (who we also got the chance to speak with ahead of the event on Knowledge Cast).

Using Semantics to Reduce Hallucinations and Overcome Agentic Limits – Neuro-Symbolic AI and the Promise of Agentic AI

Speaker: Ben Clinch (Ortecha)

With the pace of change of AI being experienced across the industry and the constant bombardment of contradictory advice it is easy to become overwhelmed and not know where to start. The promise of LLMs have been undermined by vendor and journalistic hype and an inability to rely on quantitative answers being accurate. After all, what good would a colleague be (artificial or not) if you already need to know the answer to validate any question that you ask of them? This is further compounded by the exciting promise of Agentic AI but the relative immaturity of frameworks such as MCP. The promise of neuro-symbolic AI that combines two well established technologies (semantic knowledge graphs with machine learning) enable you to get more accurate LLM powered analytics and most importantly faster time to greater data value and when leveraged alongside solid data management foundations can mitigate and empower AI Agents while limiting the inherent risks in using them.

In this practical, engaging, and fun talk, Ben equips participants with the principles and fundamentals that never change but often go under-utilized to help you lay a solid foundation for the new age of agentic AI.

The post Semantic Layer Symposium 2025: Using Semantics to Reduce Hallucinations and Overcome Agentic Limits – Neuro-Symbolic AI and the Promise of Agentic AI appeared first on Enterprise Knowledge.

The Anatomy of AI

Here is a conceptual analogy to help illustrate how taxonomies and ontologies support AI. While inspired by the human musculoskeletal system, this analogy is not intended to represent anatomical accuracy, but rather to illustrate how taxonomies provide foundational structure and ontologies enable flexible, contextual connections of knowledge assets within AI systems.

Just like the musculoskeletal system gives structure, support, and coherence to the human body, taxonomies and ontologies provide the structural framework that organizes and contextualizes knowledge assets for AI. Here is the analogy: the spine and the bones represent the taxonomies, in other words, the hierarchical, backbone structure for categorizing and organizing concepts that describe an organization’s core knowledge assets. Similarly, the joints, ligaments, and muscles represent the ontologies that provide the flexibility to connect related concepts across assets in an organization’s knowledge domain. 

Depending on the organization’s domain or industry, certain types of knowledge assets become more relevant or strategically important. In the case of a healthcare organization, key knowledge assets may include content such as patients’ electronic health records, clinical guidelines and protocols, multidisciplinary case reviews, and research publications, as well as data such as diagnostic data and clinical trial data. Taxonomies that capture and group together key concepts, such as illnesses, symptoms, treatments, outcomes, medicines, clinical specialties can be used to tag and structure these assets. Continuing with the same scenario, an ontology in a healthcare organization can incorporate those key concepts (entities) from the taxonomy, along with their properties and relationships, to enable alignment and consistent interpretation of knowledge assets across systems. Both taxonomies and ontologies in healthcare organizations make it possible to connect, for instance, a patient’s health record with diagnostic data and previous case reviews for other patients based on the same (or similar) conditions, including illnesses, symptoms, treatments, and medicines. As a result, healthcare professionals can quickly access the information they need to make well-informed decisions about a patient’s care.

Where AI is Failing

On multiple occasions, AI has repeatedly failed to provide reliable information to employees, customers, and patients, undermining their confidence in the AI supported system and sometimes leading to serious organizational consequences. You may be familiar with the case in which a chatbot of a medical association was unintentionally giving harmful advice to people with eating disorders. Or maybe you heard in the news about the bank with a faulty AI system that misclassified thousands of transactions as fraudulent due to a programming error, resulting in significant customer dissatisfaction and harming the organization’s reputation. There was also a case in which an AI-powered translation system failed to accurately assess asylum seekers’ applications, raising serious concerns about its fairness and accuracy, and potentially affecting critical life decisions for those applicants. In each of these cases, had the corresponding AI systems effectively aggregated both unstructured and structured knowledge assets, and reliably linked them to encoded expert knowledge and relevant business context, these cases would have produced very different and positive outcomes. By leveraging taxonomies and ontologies to aggregate key knowledge assets, the result of these cases would have been much more closely aligned with intended objectives, ultimately, benefiting the end users as it was initially intended. 

How Taxonomies And Ontologies Enable Explainable AI

When knowledge assets are consistently tagged with taxonomies and related via ontologies, AI systems can trace how a decision was made. This means that end users can understand the reasoning path, supported by defined relationships. This also means that bias and hallucinations can be more easily detected by auditing the semantic structure behind the results.

As illustrated in the healthcare organization example, diagnoses can be tagged with medical industry taxonomies, while ontologies can help create relationships among symptoms, treatments, and outcomes. This can help physicians tailor treatments to individual patient needs by leveraging past patient cases and the collective expertise from other physicians. Similarly, a retail organization can enhance its customer service by implementing a chatbot that is linked to structured product taxonomies and ontologies to help deliver consistent and explainable answers about products to customers. More consistent and trustworthy customer interactions result in streamlining end user support and strengthening brand confidence.

Do We Really Need Taxonomies and Ontologies to be Successful With AI?

The examples above illustrate that explainability in AI really matters. Whether end users are patients, bank customers, or any individuals requesting specific products or services, they all want more transparent, trustworthy, and human-centered AI experiences. Taxonomies and ontologies help provide structure and connectedness to content, documents, data, expert knowledge and overall business context, so that they all are machine readable and findable by AI systems at the moment of need, ultimately creating meaningful interactions for end users.  

Conclusion

Just like bones, joints, ligaments, and muscles in the human body, taxonomies and ontologies provide the essential structure and connection that allow AI systems to stand up to testing, be reliable, and perform with clarity. At EK we have extensive experience identifying key knowledge assets as well as designing and implementing taxonomies and ontologies to successfully support AI initiatives. If you want to improve the Knowledge Intelligence (KI) of your existing or future AI applications and need help with your taxonomy and ontology efforts, don’t hesitate to get in touch with us. 

The post How Taxonomies and Ontologies Enable Explainable AI appeared first on Enterprise Knowledge.

One key step in preparing content is the application of metadata and organizational context to pieces of content through tagging. There are several tagging approaches an organization can take to enrich pre-existing content with metadata and organizational context, including manual tagging, automated tagging capabilities from a taxonomy and ontology management system (TOMS), using apps and features directly from a content management solution, and various hybrid approaches. While many of these approaches, in particular acquiring a TOMS, are recommended as a long-term auto-tagging solution, EK has recommended and implemented Large Language Model (LLM)-based auto-tagging capabilities across several recent engagements. Due to LLM-based tagging’s lower initial investment compared to a TOMS and its greater efficiency than manual tagging, these auto-tagging solutions have been able to provide immediate value and jumpstart the process of re-tagging existing content. This blog will dive deeper into how LLM tagging works, the value of semantics, technical considerations, and next steps for implementing an LLM-based tagging solution.

Overview of LLM-Based Auto-Tagging Process

Similar to existing auto-tagging approaches, the LLM suggests a tag by parsing through a piece of content, processing and identifying key phrases, terms, or structure that gives the document context. Through prompt engineering, the LLM is then asked to compare the similarity of key semantic components (e.g., named entities, key phrases) with various term lists, returning a set of terms that could be used to categorize the piece of content. These responses can be adjusted in the tagging workflow to only return terms meeting a specific similarity score. These tagging results are then exported to a data store and applied to the content source. Many factors, including the particular LLM used, the knowledge an LLM is working with, and the source location of content, can greatly impact the tagging effectiveness and accuracy. In addition, adjusting parameters, taxonomies/term lists, and/or prompts to improve precision and recall can ensure tagging results align with an organization’s needs. The final step is the auto-tagging itself and the application of the tags in the source system. This could look like a script or workflow that applies the stored tags to pieces of content.

Figure 1: High-level steps for LLM content enrichment

EK has put these steps into practice, for example, when engaging with a trade association on a content modernization project to migrate and auto-tag content into a new content management system (CMS). The organization had been struggling with content findability, standardization, and governance, in particular, the language used to describe the diverse areas of work the trade association covers. As part of this engagement, EK first worked with the organization’s subject matter experts (SMEs) to develop new enterprise-wide taxonomies and controlled vocabularies integrated across multiple platforms to be utilized by both external and internal end-users. To operationalize and apply these common vocabularies, EK developed an LLM-based auto-tagging workflow utilizing the four high-level steps above to auto-tag metadata fields and identify content types. This content modernization effort set up the organization for document workflows, search solutions, and generative AI projects, all of which are able to leverage the added metadata on documents. 

Value of Semantics with LLM-Based Auto-Tagging

Semantic models such as taxonomies, metadata models, ontologies, and content types can all be valuable inputs to guide an LLM on how to effectively categorize a piece of content. When considering how an LLM is trained for auto-tagging content, a greater emphasis needs to be put on organization-specific context. If using a taxonomy as a training input, organizational context can be added through weighting specific terms, increasing the number of synonyms/alternative labels, and providing organization-specific definitions. For example, by providing organizational context through a taxonomy or business glossary that the term “Green Account” refers to accounts that have met a specific environmental standard, the LLM would not accidentally tag content related to the color green or an account that is financially successful.

Another benefit of an LLM-based approach is the ability to evolve both the semantic model and LLM as tagging results are received. As sets of tags are generated for an initial set of content, the taxonomies and content models being used to train the LLM can be refined to better fit the specific organizational context. This could look like adding additional alternative labels, adjusting the definition of terms, or adjusting the taxonomy hierarchy. Similarly, additional tools and techniques, such as weighting and prompt engineering, can tune the results provided by the LLM and evolve the results generated to achieve a higher recall (rate the LLM is including the correct term) and precision (rate the LLM is selecting only the correct term) when recommending terms. One example of this is  adding weighting from 0 to 10 for all taxonomy terms and assigning a higher score for terms the organization prefers to use. The workflow developed alongside the LLM can use this context to include or exclude a particular term.

Implementation Considerations for LLM-Based Auto-Tagging 

Several factors, such as the timeframe, volume of information, necessary accuracy, types of content management systems, and desired capabilities, inform the complexity and resources needed for LLM-based content enrichment. The following considerations expand upon the factors an organization must consider for effective LLM content enrichment. 

Tagging Accuracy

The accuracy of tags from an LLM directly impacts end-users and systems (e.g., search instances or dashboards) that are utilizing the tags. Safeguards need to be implemented to ensure end-users can trust the accuracy of the tagged content they are using. These help ensure that a user is not mistakenly accessing or using a particular document, or that they are frustrated by the results they get. To mitigate both of these concerns, a high recall and precision score with the LLM tagging improves the overall accuracy and lowers the chance for miscategorization. This can be done by investing further into human test-tagging and input from SMEs to create a gold-standard set of tagged content as training data for the LLM. The gold-standard set can then be used to adjust how the LLM weights or prioritizes terms, based on the organizational context in the gold-standard set. These practices will help to avoid hallucinations (factually incorrect or misleading content) that could appear in applications utilizing the auto-tagged set of content.

Content Repositories

One factor that greatly adds technical complexity is accessing the various types of content repositories that an LLM solution, or any auto-tagging solution, needs to read from. The best content management practice for auto-tagging is to read content in its source location, limiting the risk of duplication and the effort needed to download and then read content. When developing a custom solution, each content repository often needs a distinctive approach to read and apply tags. A content or document repository like SharePoint, for example, has a robust API for reading content and seamlessly applying tags, while a less widely adopted platform may not have the same level of support. It is important to account for the unique needs of each system in order to limit the disruption end-users may experience when embarking on a tagging effort.

Knowledge Assets

When considering the scalability of the auto-tagging effort, it is also important to evaluate the breadth of knowledge asset types being analyzed. While the ability of LLMs to process several types of knowledge assets has been growing, each step of additional complexity, particularly evaluating multiple types, can result in additional resources and time needed to read and tag documents. A PDF document with 2-3 pages of content will take far fewer tokens and resources for an LLM to read its content than a long visual or audio asset. Going from a tagging workflow of structured knowledge assets to tagging unstructured content will increase the overall time, resources, and custom development needed to run a tagging workflow. 

Data Security & Entitlements

When utilizing an LLM, it is recommended that an organization invest in a private or an in-house LLM to complete analysis, rather than leveraging a publicly available model. In particular, an LLM does not need to be ‘on-premises’, as several providers have options for LLMs in your company’s own environment. This ensures a higher level of document security and additional features for customization. Particularly when tackling use cases with higher levels of personal information and access controls, a robust mapping of content and an understanding of what needs to be tagged is imperative. As an example, if a publicly facing LLM was reading confidential documents on how to develop a company-specific product, this information could then be leveraged in other public queries and has a higher likelihood of being accessed outside of the organization. In an enterprise data ecosystem, running an LLM-based auto-tagging solution can raise red flags around data access, controls, and compliance. These challenges can be addressed through a Unified Entitlements System (UES) that creates a centralized policy management system for both end users and LLM solutions being deployed.

Next Steps:

One major consideration with an LLM tagging solution is maintenance and governance over time. For some organizations, after completing an initial enrichment of content by the LLM, a combination of manual tagging and forms within each CMS helps them maintain tagging standards over time. However, a more mature organization that is dealing with several content repositories and systems may want to either operationalize the content enrichment solution for continued use or invest in a TOMS. With either approach, completing an initial LLM enrichment of content is a key method to prove the value of semantics and metadata to decision-makers in an organization. 
Many technical solutions and initiatives that excite both technical and business stakeholders can be actualized by an LLM content enrichment effort. By having content that is tagged and adhering to semantic standards, solutions like knowledge graphs, knowledge portals, and semantic search engines, or even an enterprise-wide LLM Solution, are upgraded even further to show organizational value.

If your organization is interested in upgrading your content and developing new KM solutions, contact us!

The post How to Leverage LLMs for Auto-tagging & Content Enrichment appeared first on Enterprise Knowledge.

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, of course, is an ongoing challenge, rather than a moment in time initiative. To ensure the important work you’ve done to get your content, data, and other assets AI-ready is not lost, you need governance as well as an operating model to guide it. Indeed, well before any AI readiness initiative, governance and the organization must be top of mind. 

Governance is not a new term within the field. Historically, we’ve identified four core components to governance in the context of content or data:

• Business Case and Measurable Success Criteria: Defining the value of the solution and the governance model itself, as well as what success looks like for both.
• Roles and Responsibilities: Defining the individuals and groups necessary for governance, as well as the specific authorities and expectations of their roles.
• Policies and Procedures: Detailing the timelines, steps, definitions, and actions for the associated roles to play.
• Communications and Training: Laying out the approach to two-way communications between the associated governance roles/groups and the various stakeholders.

These traditional components of governance all have held up, tried and true, over the quarter-century since we first defined them. In the context of AI, however, it is important to go deeper and consider the unique aspects that artificial intelligence brings into the conversation. Virtually every expert in the field agrees that AI governance should be a priority for any organization, but that must be detailed further in order to be meaningful.

In the context of AI readiness for knowledge assets, we focus AI governance, and more broadly its supporting operating model, on five key elements for success:

• Coordination and Enablement Over Execution
• Connection Instead of Migration
• Filling Gaps to Address the Unanswerable Questions
• Acting on “Hallucinations”
• Embedding Automation (Where It Makes Sense)

There is, of course, more to AI governance than these five elements, but in the context of AI readiness for knowledge assets, our experience shows that these are the areas where organizations should be focusing and shifting away from traditional models. 

1) Coordination and Enablement Over Execution

In traditional governance models (i.e. content governance, data governance, etc.), most of the work was done in the context of a single system. Content would be in a content management system and have a content governance model. Data would be in a data management solution and have a data governance model. The shift is that today’s AI governance solutions shouldn’t care what types of assets you have or where they are housed. This presents an amazing opportunity to remove artificial silos within an organization, but brings a marked challenge. 

If you were previously defining a content governance model, you most likely possessed some level of control or ownership over your content and document management systems. Likewise, if you were in charge of data governance, you likely “own” some or all of the major data solutions like master data management or a data warehouse within your organization. With AI, however, an enormous benefit of a correctly architected enterprise AI solution that leverages a semantic layer is that you likely don’t own these source systems. The system housing the content, data, and other knowledge assets is likely, at least partly, managed by other parts of your organization. In other words, in an AI world, you have less control over the sources of the knowledge assets, and thereby over the knowledge assets themselves. This may well change as organizations evolve in the “Age of AI,” but for now, the role and responsibility for AI governance becomes more about coordination and less about execution or enforcement.

In practice, this means an AI Governance for Knowledge Asset Readiness group must coordinate with the owners of the various source systems for knowledge assets, providing additive guidance to define what it means to have AI-ready assets as well as training and communications to enable and engage system and asset owners to understand what they must do to have their content, data, and other assets included within the AI models. The word “must” in the previous sentence is purposeful. You alone may not possess the authority of an information system owner to define standards for their assets, but you should have the authority to choose not to include those assets within the enterprise AI solution set.

How do you apply that authority? As the lines continue to blur between the purview of KM, Data, and AI teams, this AI Governance for Knowledge Asset Readiness group should comprise representatives from each of these once siloed teams to co-own outcomes as new AI use cases, features, and capabilities are developed. The AI governance group should be responsible for delineating key interaction points and expected outcomes across teams and business functions to build alignment, facilitate planning and coordination, and establish expectations for business and technical stakeholders alike as AI solutions evolve. Further, this group should define what it means (and what is required) for an asset to be AI-ready. We cover this in detail in previous articles, but in short, this boils down to semantic structure, quality, and entitlements as the three core pillars to AI readiness for knowledge assets. 

2) Connection Instead of Migration

The idea of connections over migration aligns with the previous point. Past monolithic efforts in your organization would commonly have included massive migrations and consolidations of systems and solutions. The roadmaps of past MDMs, data warehouses, and enterprise content management initiatives are littered with failed migrations. Again, part of the value of an enterprise AI initiative that leverages a semantic layer, or at least a knowledge graph, is that you don’t need to absorb the cost, complexity, and probable failure of a massive migration. 

Instead, the role of the AI Governance for Knowledge Asset Readiness group is one of connections. Once the group has set the expectation for AI-ready knowledge assets, the next step is to ensure the systems that house those assets are connected and available, ready for the enterprise AI solutions to be ingested and understood. This can be a highly iterative process, not to be rushed, as the sanctity of the assets ingested by AI is more important than their depth. Said differently, you have few chances to deliver wrong answers—your end users will lose trust quickly in a solution that delivers inaccurate information that they know is unmistakably incorrect; but if they receive an incomplete answer instead, they will be more likely to raise this and continue to engage. The role of this AI governance group is to ensure the right systems and their assets are reliably available for the AI solution(s) at the right time, after your knowledge assets have passed through the appropriate requirements.

3) Filling Gaps to Address the Unanswerable Questions

As the AI solutions are deployed, the shift for AI governance moves from being proactive to reactive. There is a great opportunity associated with this that bears a particular focus. In the history of knowledge management, and more broadly the fields of content management, data management, and information management, there’s always been a creeping concern that an organization “doesn’t know what it doesn’t know.” What are the gaps in knowledge? What are the organizational blind spots? These questions have been nearly impossible to answer at the enterprise level. However, with enterprise-level AI solutions implemented, the ability to have this awareness is suddenly a possibility.

Even before deploying AI solutions, a well-designed semantic layer can help pinpoint organizational gaps in knowledge by finding taxonomy elements lacking in applied knowledge assets. However, this potential is magnified once the AI solution is fully defined. Today’s mature AI solutions are “smart” enough to know when they can’t answer a question and highlight that unanswerable question to the AI governance group. Imagine possessing the organizational intelligence to know what your colleagues are seeking to understand, having insights into that which they are trying to learn or answer, but are currently unable to. 

In this way, once an AI solution is deployed, the primary role of the AI governance group should be to diagnose and then respond to these automatically identified knowledge gaps, using their standards to fill them. It may be that the information does, in fact, exist within the enterprise, but that the AI solution wasn’t connected to those knowledge assets. Alternatively, it may be that the right semantic structure wasn’t placed on the assets, resulting in a missed connection and a false gap from the AI. However, it may also be that the answer to the “unanswerable” question only exists as tacit knowledge in the heads of the organization’s experts, or doesn’t exist at all. This is the most core and true value of the field of knowledge management, and has never been so possible.

4) Acting on “Hallucinations”

Aligned with the idea of filling gaps, a similar role for the AI governance group should be to address hallucinations or failures for AI to deliver an accurate, consistent, and complete “answer.” For organizations attempting to implement enterprise AI, a hallucination is little more than a cute word for an error, and should be treated as such by the AI governance group. There are many reasons for these errors, ranging from poor quality (i.e., wrong, outdated, near-duplicate, or conflicting) knowledge assets, insufficient semantic structure (e.g., taxonomy, ontology, or a business glossary), or poor logic built into the model itself. Any of these issues should be treated with immediate action. Your organization’s end users will quickly lose trust in an AI solution that delivers inaccurate results. Your governance model and associated organizational structure must be equipped to act quickly, first to leverage communications and feedback channels to ensure your end users are telling you when they believe something is inaccurate or incomplete, and moreover, to diagnose and address it.

As a note, for the most mature organizations, this action won’t be entirely reactive. For the most mature, organizational subject matter experts will be involved in perpetuity, especially right before and after enterprise AI deployment, to hunt for errors in these systems. Commonly, you can consider this governance function as the “Hallucination Killers” within your organization, likely to be one of the most critical actions as AI continues to expand.

5) Embedding Automation (Where It Makes Sense)

Finally, one of the most important roles of an AI governance group will be to use AI to make AI better. Almost everything we’ve described above can be automated. AI can and should be used to automate identification of knowledge gaps as well as solve the issue of those knowledge gaps by pinpointing organizational subject matter experts and targeting them to deliver their learning and experience at the right moments. It can also play a major role in helping to apply the appropriate semantic structure to knowledge, through tagging of taxonomy terms as metadata or identification of potential terms for inclusion in a business glossary. Central to all of this automation, however, is to ensure the ‘human is in the loop’, or rather, the AI governance group plays an advisory and oversight role throughout these automations, to ensure the design doesn’t fall out of alignment. This element further facilitates AI governance coordination across the organization by supporting stakeholders and knowledge asset stewards through technical enablement.

All of this presents a world of possibility. Governance was historically one of the drier and more esoteric concepts within the field, often where good projects went bad. We have the opportunity to do governance better by leveraging AI in the areas where humans historically fell short, while maintaining the important role of human experts with the right authority to ensure organizational alignment and value.

If your AI efforts aren’t yet yielding the results you expected, or you’re seeking to get things started right from the beginning, contact EK to help you.

The post Defining Governance and Operating Models for AI Readiness of Knowledge Assets appeared first on Enterprise Knowledge.

Enterprise Knowledge regularly hears from our clients that: 

• “We don’t have time for documentation with everything going on.”

• “We’re not sure how to capture knowledge in a way that is useful to others.”

• “People don’t know what they can or can’t share.”

These are real barriers, and this blog and accompanying infographic address them directly. It is not about doing more. It is about working smarter by embedding lightweight, effective knowledge-sharing habits into what you are already doing. Over time, these habits create durable knowledge assets that strengthen organizational memory and prepare your content and data for AI-readiness.

Integrate Knowledge Capture Into the Flow of Work

Small changes can make a big impact, especially when they reduce friction and feel like a natural part of the workday. Start by using familiar tools to ensure employees can document and share knowledge within the platforms they already use. This lowers barriers to participation and makes it easier to integrate knowledge sharing into the flow of work.

Standardized templates offer a simple, structured way to capture lessons learned, best practices, and key insights. The templates themselves serve as a guide, prompting employees on what details to capture and where those details belong. This reduces the cognitive load and guesswork that often gets in the way of documenting knowledge.

To reinforce the habit, build knowledge capture tasks into process and project checklists, or use workflow triggers that remind employees when it is time to reflect and share. Until knowledge-sharing practices are fully embedded, timely prompts help ensure action happens at the right moment.

Some moments naturally lend themselves to knowledge capture, such as project closeouts, after client interactions, during onboarding, or following major decisions. These are high-value opportunities where small, structured contributions can have an outsized impact. Our blog on High Value Moments of Content Capture expands on this by showing how to identify the right moments and implement simple practices to capture knowledge effectively when it matters most.

Automate Where You Can

Leverage automated and AI-powered processes to further enhance knowledge capture by minimizing manual effort and making information more accessible with low-effort, intelligent solutions such as:

• Automated meeting transcription and indexing capture discussions with minimal effort, converting conversations into structured content that is searchable and readily available for reference.
• AI-powered recommendations proactively surface relevant documentation within collaboration tools, reducing the need for employees to search for critical information manually.
• Auto-classification of content streamlines knowledge organization by automatically tagging and categorizing information, ensuring documents and insights are consistently structured and easy to retrieve.
• AI-driven named entity recognition (NER) automatically extracts and tags key information in real-time, transforming unstructured content into easily searchable and actionable knowledge.

Closing Thoughts

When knowledge capture is built into existing workflows, rather than treated as a separate activity, staff do not have to choose between sharing what they know and doing their job. The goal is not perfection; it is progress through building consistent, low-effort habits.

Whether your organization is just starting to explore knowledge capture or is ready to scale existing practices with automation, EK can help. Our approach is practical and tailored–we will meet you where you are and co-design right-sized solutions that fit your current capacity and goals. Contact us to learn more.

The post Capture as You Work: Embedding Knowledge Capture in Daily Work appeared first on Enterprise Knowledge.

In a recent blog, “Top Ways to Get Your Content and Data Ready for AI,” Sara Mae O’Brien-Scott and Zach Wahl gave an approach for ensuring your organization is ready to undertake an AI Initiative. While the previous blog provided a broad view of AI-readiness for all types of Knowledge Assets collectively, this blog will leverage the same approach, zeroing in on actionable steps to ensure your content is ready for AI. Content, also known as unstructured information, is pervasive in every organization. In fact, for many organizations it comprises 80% to 90% of the total information held within the organization. Within that corpus of content, there is a massive amount of value, but there also tends to be chaos. We’ve found that most organizations should only be actively maintaining 15-20% of their unstructured information, with the rest being duplicate, near-duplicate, outdated, or completely incorrect. Without taking steps to clean it up, contextualize it, and ensure it is properly accessible to the right people, your AI initiatives will flounder. The steps we detail below will enable you to implement Enterprise AI at your organization, minimizing the pitfalls and struggles many organizations have encountered while trying to implement AI.

1) Understand What You Mean by “Content” (Knowledge Asset Definition) 

In a previous blog, we discussed the many types of knowledge assets organizations possess, how they can be connected, and the collective value they offer. Identifying content, or unstructured information, as one of the types of knowledge assets to be included in your organization’s AI solutions will be a foregone conclusion for most. However, that alone is insufficient to manage scope and understand what needs to be done to ensure your content is AI-ready. There are many types of content, held in varied repositories, with much likely sprawling on existing file drives and old document management systems. 

Before embarking on an AI initiative, it is essential to focus on the content that addresses your highest priority use cases and will yield the greatest value, recognizing that more layers can be added iteratively over time. To maximize AI effectiveness, it is critical to ensure the content feeding AI models aligns with real user needs and AI use cases. Misaligned content can lead to hallucinations, inaccurate responses, or poor user experiences. The following actions help define content and prepare it for AI:

• Identify the types of content that are critical for priority AI use cases.
• Work with Content Governance Groups to identify content owners for future inclusion in AI testing. 
• Map end-to-end user journeys to determine where AI interacts with users and the content touchpoints that need to be referenced by AI applications.
• Inventory priority content across enterprise-wide source systems, breaking knowledge asset silos and system silos.
• Flag where different assets serve the same intent to flag potential overlap or duplication, helping AI applications ingest only relevant content and minimize noise during AI model training.

What content means can vary significantly across organizations. For example, in a manufacturing company, content can take the form of operational procedures and inventory reports, while in a healthcare organization, it can include clinical case documentation and electronic health records. Understanding what content truly represents in an organization and identifying where it resides, often across siloed repositories, are the first steps toward enabling AI solutions to deliver complete and context-rich information to end users.

2) Ensure Quality (Content Cleanup)

Your AI Model is only as good as what’s going into it. ‘Garbage in, garbage out’, ‘steady foundation’, ‘steady house’, there are any number of ways to describe that if the content going into an AI model lacks quality, the outputs will too. Strong AI starts with strong content. Below, we have detailed both manual and automated actions that can be taken to improve the quality of your content, thereby improving your AI outcomes. 

Content Quality

Content created without regard for quality is common in the everyday workflow. While this content might serve business-as-usual processes, it can be detrimental to AI initiatives. Therefore, it’s crucial to address content quality issues within your repositories. Steps you can take to improve content quality and accelerate content AI readiness include:

• Automate content cleanup processes by leveraging a combination of human-led and system-driven approaches, such as auto-tagging content for update, archival, or removal.
• Scan and index content using automated processes to detect potential duplication by comparing titles, file size, metadata, and semantic similarity.
• Apply similarity analysis to define business rules for deleting, archiving or modifying duplicate or near-duplicate content.
• Flag content that has low-use or no-use, using analytics.
• Combine analytics and content age to determine a retention cut-off (such as removing any content older than 2 years).
• Leverage semantic tools like Named Entity Recognition (NER) and Natural Language Processing (NLP) to apply expert knowledge and determine the accuracy of content.
• Use NLP to detect overly complex sentence structure and enterprise specific jargon that may reduce clarity or discoverability.

Content Restructuring

In the blog, “Improve Enterprise AI with Semantic Content Management” we note that content in an organization exists on a continuum of structure depending on many factors. The same is true for the amount of content restructuring that may or may not need to happen to enable your AI use case. We recently saw with a client that introducing even just basic structure to a document improved AI outcomes by almost 200%. However, this step requires clear goals and prioritization. Oftentimes this part of ensuring your content is AI-ready happens iteratively as the model is applied and you can determine what level of restructuring needs to occur to best improve AI outcomes. Restructuring content to prepare it for AI involves activities such as:

• Apply tags, such as heading structures, to unstructured content to improve AI outcomes and enhance the end-user experience.
• Use an AI-assisted check to validate that heading structures and tags are being used appropriately and are machine readable, so that content can be ingested smoothly by AI systems.
• Simplify and restructure content that has been identified as overly complex and could result in hallucinations or unsatisfactory responses generated by the AI model.
• Focus on reformatting longer, text-heavy content to achieve a more linear, time-based, or topic-based flow and improve AI effectiveness. 
• Develop repeatable structures that can be applied automatically to content during creation or retroactively to provide AI with relevant content in a consumable format. 

In brief, cleaning up and restructuring content assets improves machine readability of content and therefore allows the AI model to generate stronger and more accurate outputs. To prioritize assets that need cleanup and restructuring, focus on activities and resources that will yield the highest return on investment for your AI solution. However, it is important to recognize that this may vary significantly across organizations, industries, and AI use cases. For example, an organization with a truly cross-functional use case, such as enterprise search, may prioritize deduplication of content to ensure information from different business areas doesn’t conflict when providing AI-generated responses. On the other hand, an organization with a more function-specific use case, such as streamlining legal contract review, may prioritize more hands-on content restructuring to improve AI comprehension.

3) Fill Gaps (Tacit Knowledge Capture)

Even with high-quality content, knowledge gaps that exist in your full enterprise ecosystem can cause AI errors and introduce the risk of unreliable outcomes. Considering your AI use case, the questions you want to answer, the discovery you’ve completed in previous steps, and the actions detailed below you can start to identify and fill gaps that may exist. 

Content Coverage 

Even with the best content strategy, it is not uncommon for different types of content to “fall through the cracks” and be unavailable or inaccessible for any number of reasons. Many organizations “don’t know what they don’t know”, so it can be difficult to begin this process. However, it is crucial to be aware of these content gaps, particularly when using LLMs to avoid hallucinations. Actions you may take to ensure content coverage and accelerate your journey toward content AI readiness include: 

• Leverage systems analytics to assess user search behavior and uncover content gaps. This may include unused content areas of a repository, abandoned search queries, or searches that returned no results. 
• Identify content gaps by using taxonomy analytics to identify missing categories or underrepresented terms and as a result, determine what content should be included.
• Leverage SMEs and other end users during AI testing to evaluate AI-generated responses and identify areas where content may be missing. 
• Use AI governance to ensure the model is transparent and can communicate with the user when it is not able to find a satisfactory answer.

Fill the Gap

Once missing content has been identified from information sources feeding the AI model, the real challenge is to fill in those gaps to prevent “hallucinations” and avoid user frustration that may be generated by incomplete or inaccurate answers. This may include creating new assets, locating assets, or other techniques identified which together can move the organization from AI to Knowledge Intelligence. Steps you may take to remediate the gaps and help your organization’s content be AI ready include:

• Use link detection to uncover relationships across the content, identify knowledge that may exist elsewhere, and increase the likelihood of surfacing the right content. This can also inform later semantic tagging activities.
• Identify, by analyzing content repositories, sources where content identified as “missing” could possibly exist.
• Apply content transformation practices to “missing” content identified during the content repository analysis to ensure machine readability.
• Conduct knowledge capture and transfer activities such as SME interviews, communities of practice, and collaborative tools to document tacit knowledge in the form of guides, processes, or playbooks. 
• Institutionalize content that exists in private spaces that aren’t currently included in the repositories accessed by AI.
• Create draft content using generative AI, making sure to include a human-in-the-loop step for accuracy. 
• Acquire external content when gaps aren’t organization specific. Consider purchasing or licensing third-party content, such as research reports, marketing intelligence, and stock images.

By evaluating the content coverage for a particular use case, you can start to predict how well (or poorly) your AI model may perform. When critical content mostly exists in people’s heads, rather than in documented, accessible format, the organization is exposed to significant risk. For example, an organization deploying a customer-facing AI chatbot to help with case deflection in customer service centers, gaps in content can lead to potentially false or misleading responses. If the chatbot tries to answer questions it wasn’t trained for, it could result in out-of-policy exceptions, financial loss, decrease in customer trust, or lower retention due to inaccurate, outdated, or non-existent information. This example highlights why it is so important to identify and fill knowledge gaps to ensure your content is ready for AI. 

4) Add Structure and Context (Semantic Components)

Once you have identified the relevant content for an AI solution, ensured its quality for AI, and addressed major content gaps for your AI use cases, the next step in getting content ready for AI involves adding structure and context to content by leveraging semantic components. Taxonomy and metadata models provide the foundational structure needed to categorize unstructured content and provide meaningful context. Business glossaries ensure alignment by defining terms for shared understanding, while ontology models provide contextual connections needed for AI systems to process content. The semantic maturity of all of these models is critical to achieve successful AI applications. 

Semantic Maturity of Taxonomy and Business Glossaries

Some organizations struggle with the state of their taxonomies when starting AI-driven projects. Organizations must actively design and manage taxonomies and business glossaries to properly support AI-driven applications and use cases. This is not only essential for short-term implementation of the AI solution, but most importantly for long-term success. Standardization and centralization of these models help balance organization-wide needs and domain-specific needs. Properly structured and annotated taxonomies are instrumental in preparing content for AI. Taking the following actions will ensure that you have the Semantic Maturity of Taxonomies and Business Glossaries needed to achieve AI ready content:

• Balance taxonomies across business areas to ensure organization-wide standardization, enabling smooth implementation of AI use cases and seamless integration of AI applications. 
• Design hierarchical taxonomy structures with the depth and breadth needed to support AI use cases.
• Refine concepts and alternative terms (synonyms and acronyms) in the taxonomy to more adequately describe and apply to priority AI content.
• Align taxonomies with usability standards, such as ANSI/NISO Z39.19, and interoperability/machine readability standards, such as SKOS, so that taxonomies are both human and machine readable.
• Incorporate definitions and usage notes from an organizational business glossary into the taxonomy to enrich meaning and improve semantic clarity.
• Store and manage taxonomies in a centralized Taxonomy Management System (TMS) to support scalable AI readiness.

Semantic Maturity of Metadata 

Before content can effectively support AI-driven applications, organizations must also establish metadata practices to ensure that content has been sufficiently described and annotated. This involves not only establishing shared or enterprise-wide coordinated metadata models, but more importantly, applying complete and consistent metadata to that content. The following actions will ensure that the Semantic Maturity of your Metadata model meets the standards required for content to be AI ready:

• Structure metadata models to meet the requirements of AI use cases, helping derive meaningful insights from tagged content.
• Design metadata models that accurately represent different knowledge asset types (types of content) associated with priority AI use cases.
• Apply metadata models consistently across all content source systems to enhance findability and discoverability of content in AI applications. 
• Document and regularly update metadata models.
• Store and manage metadata models in a centralized semantic repository to ensure interoperability and scalable reuse across AI solutions.

Semantic Maturity of Ontology

Just as with taxonomies, metadata, and business glossaries, developing semantically rich and precise ontologies is essential to achieve successful AI applications and to enable Knowledge Intelligence (KI) or explainable AI. Ontologies must be sufficiently expressive to support semantic enrichment, traceability, and AI-driven reasoning. They must be designed to accurately represent key entities, their properties, and relationships in ways that enable consistent tagging, retrieval, and interpretation across systems and AI use cases. By taking the following actions, your ontology model will achieve the level of semantic maturity needed for content to be AI ready:

• Ensure ontologies accurately describe the knowledge domain for the in-scope content.
• Define key entities, their attributes, and relationships in a way that supports AI-driven classification, recommendation, and reasoning.
• Design modular and extensible ontologies for reuse across domains, applications, and future AI use cases.
• Align ontologies with organizational taxonomies to support semantic interoperability across business areas and content source systems.
• Annotate ontologies with rich metadata for human and machine readability.
• Adhere to ontology standards such as OWL, RDF, or SHACL for interoperability with AI tools.
• Store ontologies in a central ontology management system for machine readability and interoperability with other semantic models.

Preparing content for AI is not just about organizing information, it’s about making it discoverable, valuable, and usable. Investing in semantic models and ensuring a consistent content structure lays the foundation for AI to generate meaningful insights. For example, if an organization wants to deliver highly personalized recommendations that connect users to specific content, building customized taxonomies, metadata models, business glossaries, and ontologies not only maximizes the impact of current AI initiatives, but also future-proofs content for emerging AI-driven use cases.

5) Semantic Model Application (Content Tagging)

Designing structured semantic models is just one part of preparing content for AI. Equally important is the consistent application of complete, high-quality metadata to organization-wide content. Metadata enrichment of unstructured content, especially across silo repositories, is critical for enabling AI-powered systems to reliably discover, interpret, and utilize that content. The following actions to enhance the application of content tags will help you achieve content AI readiness:

• Tag unstructured content with high-quality metadata to enhance interpretability in AI systems.
• Ensure each piece of relevant content for the AI solution is sufficiently annotated, or in other words, it is labeled with enough metadata to describe its meaning and context. 
• Promote consistent annotation of content across business areas and systems using tags derived from a centralized and standardized taxonomy. 
• Leverage mechanisms, like auto-tagging, to enhance the speed and coverage of content tagging. 
• Include a human-in-the-loop step in the auto-tagging process to improve accuracy of content tagging.

Consistent content tagging provides an added layer of meaning and context that AI can use to deliver more complete and accurate answers. For example, an organization managing thousands of unstructured content assets across disparate repositories and aiming to deliver personalized content experiences to end users, can more effectively tag content by leveraging a centralized taxonomy and an auto-tagging approach. As a result, AI systems can more reliably surface relevant content, extract meaningful insights, and generate personalized recommendations.

6) Address Access and Security (Unified Entitlements)

As Joe Hilger mentioned in his blog about unified entitlements, “successful semantic solutions and knowledge management initiatives help the right people see the right information at the right time.” But to achieve this, access permissions must be in place so that only authorized individuals have visibility into the appropriate content. Unfortunately, many organizations still maintain content in old repositories that don’t have the right features or processes to secure it, creating a significant risk for organizations pursuing AI initiatives. Therefore, now more than ever, it is important to properly secure content by defining and applying entitlements, preventing access to highly sensitive content by unauthorized people and as a result, maintaining trust across the organization. The actions outlined below to enhance Unified Entitlements will accelerate your journey toward content AI readiness:

• Define an enterprise-wide entitlement framework to apply security rules consistently across content assets, regardless of the source system.
• Automate security by enforcing privileges across all systems and types of content assets using a unified entitlements solution.
• Leverage AI governance processes to ensure that content creators, managers, and owners are aware of entitlements for content they handle and needs to be consumed by AI applications.

Entitlements are important because they ensure that content remains consistent, trustworthy, and reusable for AI systems. For example, if an organization developing a Generative AI solution stores documents and web content about products and clients across multiple SharePoint sites, content management systems, and webpages, inconsistent application of entitlements may represent a legal or compliance risk, potentially exposing outdated, or even worse, highly sensitive content to the wrong people. On the other hand, the correct definition and application of access permissions through a unified entitlements solution plays a key role in mitigating that risk, enabling operational integrity and scalability, not only for the intended Generative AI solution, but also for future AI initiatives.

7) Maintain Quality While Iteratively Improving (Governance)

Effective governance for AI solutions can be very complex because it requires coordination across systems and groups, not just within them, especially among content governance, semantic governance, and AI governance groups. This coordination is essential to ensure content remains up to date and accessible for users and AI solutions, and that semantic models are current and centrally accessible. 

AI Governance for Content Readiness 

Content Governance 

Not all organizations have supporting organizational structures with defined roles and processes to create, manage, and govern content that is aligned with cross-organizational AI initiatives. The existence of an AI Governance for Content Readiness Group ensures coordination with the traditional Content Governance Groups and provides guidance to content owners of the source systems on how to get content AI ready to support priority AI use cases. By taking the following actions, the AI Governance for Content Readiness Group will help ensure that you have the content governance practices required to achieve AI-ready content:

• Define how content should be captured and managed in a way that is consistent, predictable, and interoperable for AI use cases.
• Incorporate in your AI solution roadmap a step, delivered through the Content Governance Groups, to guide content owners of the source systems on what is required to get content AI ready for inclusion in AI models.
• Provide guidance to the Content Governance Group on how to train and communicate with system owners and asset owners on how to prepare content for AI.
• Take the technical and strategic steps necessary to connect content source systems to AI systems for effective content ingestion and interpretation.
• Coordinate with the Content Governance Group to develop and adopt content governance processes that address content gaps identified through the detection of bias, hallucinations, or misalignment, or unanswered questions during AI testing.
• Automate AI governance processes leveraging AI to identify content gaps, auto-tag content, or identify new taxonomy terms for the AI solution.

Semantic Models Governance

Similar to the importance of coordinating with the content governance groups, coordinating with semantic models governance groups is key for AI readiness. This involves establishing roles and responsibilities for the creation, ownership, management, and accountability of semantic models (taxonomy, metadata, business glossary, and ontology models) in relation to AI initiatives. This also involves providing clear guidance for managing changes in the models and communicating updates to those involved in AI initiatives. By taking the following actions, the AI Governance for Content Readiness Group will help ensure that your organization has the semantic governance practices required to achieve AI-ready content: 

• Develop governance structures that support the development and evolution of semantic models in alignment with both existing and emerging AI initiatives.
• Align governance roles (e.g. taxonomists, ontologists, semantic engineers, and AI engineers) with organizational needs for developing and maintaining semantic models that support enterprise-wide AI solutions.
• Ensure that the systems used to manage taxonomies, metadata, and ontologies support enforcing permissions for accessing and updating the semantic models.
• Work with the Semantic Models Governance Groups to develop processes that help remediate gaps in the semantic models uncovered during AI testing. This includes providing guidance on the recommended steps for making changes, suggested decision-makers, and implementation approaches.
• Work with the Semantic Models Governance Groups to establish metrics and processes to monitor, tune, refine, and evolve semantic models throughout their lifecycle and stay up to date with AI efforts.
• Coordinate with the Semantic Models Governance Groups to develop and adopt processes that address semantic model gaps identified through the detection of bias, hallucinations, or misalignment, or unanswered questions during AI solution testing.

For example, imagine an organization is developing business taxonomies and ontologies that represent skills, job roles, industries, and topics to support an Employee 360 View solution. It is essential to have a governance model in place with clearly defined roles, responsibilities, and processes to manage and evolve these semantic models as the AI solutions team ingests content from diverse business areas and detects gaps during AI testing. Therefore, coordination between the AI Governance for Content Readiness Group and the Semantic Models Governance Groups helps ensure that concepts, definitions, entities, properties, and relationships remain current and accurately reflect the knowledge domain for both today’s needs and future AI use cases.  

Conclusion

Unstructured content remains as one of the most common knowledge assets in organizations. Getting that content ready to be ingested by AI applications is a balancing act. By cleaning it up, filling in gaps, applying rich semantic models to add structure and context, securing it with unified entitlements, and leveraging AI governance, organizations will be better positioned to succeed in their own AI journey. We hope after reading this blog you have a better understanding of the actions you can take to ensure your organization’s content is AI ready. If you want to learn how our experts can help you achieve Content AI Readiness, contact us at info@enterprise-knowledge.com. 

The post How to Ensure Your Content is AI Ready appeared first on Enterprise Knowledge.

The question of data AI readiness stems from the very real concern that when AI is pointed at data that isn’t correct or that doesn’t have the right context associated with it, organizations could face risks to their reputation, their revenue, or their customers’ privacy. With the additional nuance that data brings by often being presented in formats that require transformation, lacking in context, and frequently containing multiple duplicates or near-duplicates with little explanation of their meaning, data (although seemingly already structured and ready for machine consumption) requires greater care than other forms of knowledge assets to comprise part of a trusted AI solution. 

This blog focuses on the key actions an organization needs to perform to ensure their data is ready to be consumed by AI. By following the steps below, an organization can use AI-ready data to develop end-products that are trustworthy, reliable, and transparent in their decision making.

1) Understand What You Mean by “Data” (Data Asset and Scope Definition)

Data is more than what we typically picture it as. Broadly, data is any raw information that can be interpreted to garner meaning or insights on a certain topic. While the typical understanding of data revolves around relational databases and tables galore, often with esoteric metrics filling their rows and columns, data takes a number of forms, which can often be surprising. In terms of format, while data can be in traditional SQL databases and formats, NoSQL data is growing in usage, in forms ranging from key-value pairs to JSON documents to graph databases. Plain, unstructured text such as emails, social media posts, and policy documents are also forms of data, but traditionally not included within the enterprise definition. Finally, data comes from myriad sources—from live machine data on a manufacturing floor to the same manufacturing plant’s Human Resources Management System (HRMS). Data can also be categorized by its business role: operational data that drives day-to-day processes, transactional data that records business exchanges, and even purchased or third-party data brought in to enrich internal datasets. Increasingly, organizations treat data itself as a product, packaged and maintained with the same rigor as software, and rely on data metrics to measure quality, performance, and impact of business assets.

All these forms and types of data meet the definition of a knowledge asset—information and expertise that an organization can use to create value, which can be connected with other knowledge assets. No matter the format or repository type, ingested, AI-ready data can form the backbone of a valuable AI solution by allowing business-specific questions to be answered reliably in an explainable manner. This raises the question to organizational decision makers—what within our data landscape needs to be included in our AI solution? From your definition of what data is, start thinking of what to add iteratively. What systems contain the highest priority data? What datasets would provide the most value to end users? Select high-value data in easy-to-transform formats that allows end users to see the value in your solution. This can garner excitement across departments and help support future efforts to introduce additional data into your AI environment. 

2) Ensure Quality (Data Cleanup)

The majority of organizations we’ve worked with have experienced issues with not knowing what data they have or what it’s intended to be used for. This is especially common in large enterprise settings as the sheer scale and variety of data can breed an environment where data becomes lost, buried, or degrades in quality. This sprawl occurs alongside another common problem, where multiple versions of the same dataset exist, with slight variations in the data they contain. Furthermore, the issue is exacerbated by yet another frequent challenge—a lack of business context. When data lacks context, neither humans nor AI can reliably determine the most up-to-date version, the assumptions and/or conditions in place when said data was collected, or even if the data warrants retention.

Once AI is introduced, these potential issues are only compounded. If an AI system is provided data that is out of date or of low quality, the model will ultimately fail to provide reliable answers to user queries. When data is collected for a specific purpose, such as identifying product preferences across customer segments, but not labeled for said use, and an AI model leverages that data for a completely separate purpose, such as dynamic pricing models, harmful biases can be introduced into the results that negatively impact both the customer and the organization.

Thankfully, there are several methods available to organizations today that allow them to inventory and restructure their data to fix these issues. Examples include data dictionaries, master data (MDM data), and reference data that help standardize data across an organization and help point to what is available at large. Additionally, data catalogs are a proven tool to identify what data exists within an organization, and include versioning and metadata features that can help label data with their versions and context. To help populate catalogs and data dictionaries and to create MDM/reference data, performing a data audit alongside stewards can help rediscover lost context and label data for better understanding by humans and machines alike. Another way to deduplicate, disambiguate, and contextualize data assets is through lineage. Lineage is a feature included in many metadata management tools that stores and displays metadata regarding source systems, creation and modification dates, and file contributors. Using this lineage metadata, data stewards can select which version of a data asset is the most current or relevant for a specific use case and only expose said asset to AI. These methods to ensure data quality and facilitate data stewardship can aid in action towards a larger governance framework. Finally, at a larger scale, a semantic layer can unify data and its meaning for easier ingestion into an AI solution, assist with deduplication efforts, and break down silos between different data users and consumers of knowledge assets at large. 

Separately, for the elimination of duplicate/near-duplicate data, entity resolution can autonomously parse the content of data assets, deduplicate them, and point AI to the most relevant, recent, or reliable data asset to answer a question. 

3) Fill Gaps (Data Creation or Acquisition)

With your organization’s data inventoried and priorities identified, it’s time to start identifying what gaps exist in your data landscape in light of the business questions and challenges you are looking to address. First, ask use case-based questions. Based on your identified use cases, what data would an AI model need to answer topical questions that your organization doesn’t already possess?

At a higher level, gaps in use cases for your AI solution will also exist. To drive use case creation forward, consider the use of a data model, entity relationship diagram (ERD), or ontology to serve as the conceptual map on which all organizational data exists. With a complete data inventory, an ontology can help outline the process by which AI solutions would answer questions at a high level, thanks to being both machine and human-readable. By traversing the ontology or data model, you can design user journeys and create questions that form the basis of novel use cases.

Often, gaps are identified that require knowledge assets outside of data to fill. A data model or ontology can help identify related assets, as they function independently of their asset type. Moreover, standardized metadata across knowledge assets and asset types can enrich assets, link them to one another, and provide insights previously not possible. When instantiated in a solution alongside a knowledge graph, this forms a semantic layer where data assets, such as data products or metrics, gain context and maturity based on related knowledge assets. We were able to enhance the performance of a large retail chain’s analytics team through such an approach utilizing a semantic layer.

To fill these gaps, organizations can look to collect or create more data, as well as purchase publicly available/incorporate open-source datasets (build vs. buy). Another common method of filling identified organizational gaps is the creation of content (and other non-data knowledge assets) to identify a gap via the extraction of tacit organizational knowledge. This is a method that more chief data officers/chief data and AI officers (CDOs/CDAOs) are employing, as their roles expand and reliance on structured data alone to gather insights and solve problems is no longer feasible.

As a whole, this process will drive future knowledge asset collection, creation, and procurement efforts and consequently is a crucial step in ensuring data at large is AI ready. If no such data exists for AI to rely on for certain use cases, users will be presented unreliable, hallucination-based answers, or in a best-case scenario, no answer at all. Yet as part of a solid governance plan as mentioned earlier, the continuation of the gap analysis process post-solution deployment can empower organizations to continually identify—and close—knowledge gaps, continuously improving data AI readiness and AI solution maturity.

4) Add Structure and Context (Semantic Components)

A key component of making data AI-ready is structure—not within the data per se (e.g., JSON, SQL, Excel), but the structure relating the data to use cases. As a term, ‘structure’ added meaning to knowledge assets in our previous blog, but can introduce confusion as a misnomer in this section. Consequently, ‘structure’ will refer to the added, machine-readable context a semantic model adds to data assets, rather than the format of the data assets themselves, as data loses meaning once taken out of the structure or format it is stored in (e.g., as takes place when retrieved by AI).

Although we touched on one type of semantic model in the previous step, there are three semantic models that work together to ensure data AI readiness: business glossaries, taxonomies, and ontologies. Adding semantics to data for the purpose of getting it ready for AI allows an organization to help users understand the meaning of the data they’re working with. Together, taxonomies, ontologies, and business glossaries imbue data with the context needed for an AI model to fully grasp the data’s meaning and make optimal use of it to answer user queries. 

Let’s dive into the business glossary first. Business glossaries define business context-specific terms that are often found in datasets in a plaintext, easy-to-understand manner. For AI models which are often trained generally, these glossary terms can further assist in the selection of the correct data needed to answer a user query. 

Taxonomies group knowledge assets into broader and narrower categories, providing a level of hierarchical organization not available with traditional business glossaries. This can help data AI readiness in manifold ways. By standardizing terminology (e.g., referring to “automobile,” “car,” and “vehicle” all as “Vehicles” instead of separately), data from multiple sources can be integrated more seamlessly, disambiguated, and deduplicated for clearer understanding. 

Finally, ontologies provide the true foundation for linking related datasets to one another and allow for the definition of custom relationships between knowledge assets. When combining ontology with AI, organizations can perform inferences as a way to capture explicit data about what’s only implied by individual datasets. This shows the power of semantics at work, and demonstrates that good, AI-ready data enriched with metadata can provide insights at the same level and accuracy as a human. 

Organizations who have not pursued developing semantics for knowledge assets before can leverage traditional semantic capture methods, such as business glossaries. As organizations mature in their curation of knowledge assets, they are able to leverage the definitions developed as part of these glossaries and dictionaries, and begin to structure that information using more advanced modeling techniques, like taxonomy and ontology development. When applied to data, these semantic models make data more understandable, both to end users and AI systems. 

5) Semantic Model Application (Labeling and Tagging) 

The data management community has more recently been focused on the value of metadata and metadata-first architecture, and is scrambling to catch up to the maturity displayed in the fields of content and knowledge management. Through replicating methods found in content management systems and knowledge management platforms, data management professionals are duplicating past efforts. Currently, the data catalog is the primary platform where metadata is being applied and stored for data assets. 

To aggregate metadata for your organization’s AI readiness efforts, it’s crucial to look to data stewards as the owners of, and primary contributors to, this effort. Through the process of labeling data by populating fields such as asset descriptions, owner, assumptions made upon collection, and purposes, data stewards help to drive their data towards AI readiness while making tacit knowledge explicit and available to all. Additionally, metadata application against a semantic model (especially taxonomies and ontologies) contextualizes assets in business context and connects related assets to one another, further enriching AI-generated responses to user prompts. While there are methods to apply metadata to assets without the need for as much manual effort (such as auto-classification, which excels for content-based knowledge assets), structured data usually dictates the need for human subject matter experts to ensure accurate classification. 

With data catalogs and recent investments in metadata repositories, however, we’ve noticed a trend that we expect will continue to grow and spread across organizations in the near future. Data system owners are more and more keen to manage metadata and catalog their assets within the same systems that data is stored/used, adopting features that were previously exclusive to a data catalog. Major software providers are strategically acquiring or building semantic capabilities for this purpose. This has been underscored by the recent acquisition of multiple data management platforms by the creators of larger, flagship software products. With the features of the data catalog being adapted from a full, standalone application that stores and presents metadata to a component of a larger application that focuses as a metadata store, the metadata repository is beginning to take hold as the predominant metadata management platform.

6) Address Access and Security (Unified Entitlements)

Applying semantic metadata as described above helps to make data findable across an organization and contextualized with relevant datasets—but this needs to be balanced alongside security and entitlements considerations. Without regard to data security and privacy, AI systems risk bringing in data they shouldn’t have access to because access entitlements are mislabeled or missing, leading to leaks in sensitive information.

A common example of when this can occur is with user re-identification. Data points that independently seem innocuous, when combined by an AI system, can leak information about customers or users of an organization. With as few as just 15 data points, information that was originally collected anonymously can be combined to identify an individual. Data elements like ZIP code or date of birth would not be damaging on their own, but when combined, can expose information about a user that should have been kept private. These concerns become especially critical in industries with small population sizes for their datasets, such as rare disease treatment in the healthcare industry.

EK’s unified entitlements work is focused on ensuring the right people and systems view the correct knowledge assets at the right time. This is accomplished through a holistic architectural approach with six key components. Components like a policy engine capture can enforce whether access to data should be given, while components like a query federation layer ensure that only data that is allowed to be retrieved is brought back from the appropriate sources. 

The components of unified entitlements can be combined with other technologies like dark data detection, where a program scrapes an organization’s data landscape for any unlabeled information that is potentially sensitive, so that both human users and AI solutions cannot access data that could result in compliance violations or reputational damage. 

As a whole, data that exposes sensitive information to the wrong set of eyes is not AI-ready. Unified entitlements can form the layer of protection that ensures data AI readiness across the organization.

7) Maintain Quality While Iteratively Improving (Governance)

Governance serves a vital purpose in ensuring data assets become, and remain, AI-ready. With the introduction of AI to the enterprise, we are now seeing governance manifest itself beyond the data landscape alone. As AI governance begins to mature as a field of its own, it is taking on its own set of key roles and competencies and separating itself from data governance. 

While AI governance is meant to guide innovation and future iterations while ensuring compliance with both internal and external standards, data governance personnel are taking on the new responsibility of ensuring data is AI-ready based on requirements set by AI governance teams. Barring the existence of AI governance personnel, data governance teams are meant to serve as a bridge in the interim. As such, your data governance staff should define a common model of AI-ready data assets and related standards (such as structure, recency, reliability, and context) for future reference. 

Both data and AI governance personnel hold the responsibility of future-proofing enterprise AI solutions, in order to ensure they continue to align to the above steps and meet requirements. Specific to data governance, organizations should ask themselves, “How do you update your data governance plan to ensure all the steps are applicable in perpetuity?” In parallel, AI governance should revolve around filling gaps in their solution’s capabilities. Once the AI solutions launch to a production environment and user base, more gaps in the solution’s realm of expertise and capabilities will become apparent. As such, AI governance professionals need to stand up processes to use these gaps to continue identifying new needs for knowledge assets, data or otherwise, in perpetuity.

Conclusion

As we have explored throughout this blog, data is an extremely varied and unique form of knowledge asset with a new and disparate set of considerations to take into account when standing up an AI solution. However, following the steps listed above as part of an iterative process for implementation of data assets within said solution will ensure data is AI-ready and an invaluable part of an AI-powered organization.

If you’re seeking help to ensure your data is AI-ready, contact us at info@enterprise-knowledge.com.

The post How to Ensure Your Data is AI Ready appeared first on Enterprise Knowledge.

AI implementations are already showing signs of knowledge gaps: hallucinations, wrong answers, incomplete answers, and even “unanswerable” questions. There are multiple causes for AI hallucinations, but an important one is not having the right knowledge to answer a question in the first place. While LLMs may have been trained on massive amounts of data, it doesn’t mean that they know your business, your people, or your customers. This is a common problem when organizations make the leap from how they experience “Public AI” tools like ChatGPT, Gemini, or Copilot, to attempting their own organization’s AI solutions. LLMs and agentic solutions need knowledge—your organization’s unique knowledge— to produce results that are unique to your and your customers’ needs, and help employees navigate and solve challenges they encounter in their day-to-day work. 

In a recent article, EK outlined key strategies for preparing content and data for AI. This blog post builds on that foundation by providing a step-by-step process for identifying and closing knowledge gaps, ensuring a more robust AI implementation.

The Importance of Bridging Knowledge Gaps for AI Readiness

EK lays out a six-step path to getting your content, data, and other knowledge assets AI-ready, yielding assets that are correct, complete, consistent, contextual, and compliant. The diagram below provides an overview of these six steps:

Identifying and filling knowledge gaps, the third step of EK’s path towards AI readiness, is crucial in ensuring that AI solutions have optimized inputs. 

Prior to filling gaps, an organization will have defined its critical knowledge assets and conducted a content cleanup. A content cleanup not only ensures the correctness and reliability of the knowledge assets, but also reveals the specific topics, concepts, or capabilities that the organization cannot currently supply to AI solutions as inputs.

This scenario presupposes that the organization has a clear idea of the AI use cases and purposes for its knowledge assets. Given the organization knows the questions AI needs to answer, an assessment to identify the location and state of knowledge assets can be targeted based on the inputs required. This assessment would be followed by efforts to collect the identified knowledge and optimize it for AI solutions. 

A second, more complicated, scenario arises when an organization hasn’t formulated a prioritized list of questions for AI to answer. The previously described approach, relying on drawing up a traditional knowledge inventory will face setbacks because it may prove difficult to scale, and won’t always uncover the insights we need for AI readiness. Knowledge inventories may help us understand our known unknowns, but they will not be helpful in revealing our unknown unknowns. 

Identifying the Gap

How can we identify something that is missing? At this juncture, organizations will need to leverage analytics, introduce semantics, and if AI is already deployed in the organization, then use it as a resource as well. There are different techniques to identify these gaps, depending on whether your organization has already deployed an AI solution or is ramping up for one. Available options include:

Before and After AI Deployment

Leveraging Analytics from Existing Systems

Monitoring and assessing different tools’ analytics is an established practice to understand user behavior. In this instance, EK applies these same methods to understand critical questions about the availability of knowledge assets. We are particularly interested in analytics that reveal answers to the following questions:

• When are our people giving up when navigating different sections of a tool or portal? 
• What sort of queries return no results?
• What queries are more likely to get abandoned? 
• What sort of content gets poor reviews, and by whom?
• What sort of material gets no engagement? What did the user do or search for before getting to it? 

These questions aim to identify instances of users trying, and failing, to get knowledge they need to do their work. Where appropriate, these questions can also be posed directly to users via surveys or focus groups to get a more rounded perspective. 

Semantics

Semantics involve modeling an organization’s knowledge landscape with taxonomies and ontologies. When taxonomies and ontologies have been properly designed, updated, and consistently applied to knowledge, they are invaluable as part of wider knowledge mapping efforts. In particular, semantic models can be used as an exemplar of what should be there, and can then be compared with what is actually present, thus revealing what is missing.

We recently worked with a professional association within the medical field, helping them define a semantic model for their expansive amount of content, and then defining an automated approach to tagging these knowledge assets. As part of the design process, EK taxonomists interviewed experts across all of the association’s organizational functional teams to define the terms that should be present in the organization’s knowledge assets. After the first few rounds of auto-tagging, we examined the taxonomy’s coverage, and found that a significant fraction of the terms in the taxonomy went unused. We validated our findings with our clients’ experts, and, to their surprise, our engagement revealed an imbalance of knowledge asset production: while some topics were covered by their content, others were entirely lacking. 

Valid taxonomy terms or ontology concepts for which few to no knowledge assets exist reveal a knowledge gap where AI is likely to struggle.

After AI Deployment

User Engagement & Feedback

To ensure a solution can scale, evolve, and remain effective over time, it is important to establish formal feedback mechanisms for users to engage with system owners and governance bodies on an ongoing basis. Ideally, users should have a frictionless way to report an unsatisfactory answer immediately after they receive it, whether it is because the answer is incomplete or just plain wrong. A thumbs-up or thumbs-down icon has traditionally been used to solicit this kind of feedback, but organizations should also consider dedicated chat channels, conversations within forums, or other approaches for communicating feedback to which their users are accustomed.

AI Design and Governance 

Out-of-the-box, pre-trained language models are designed to prioritize providing a fluid response, often leading them to confidently generate answers even when their underlying knowledge is uncertain or incomplete. This core behavior increases the risk of delivering wrong information to users. However, this flaw can be preempted by thoughtful design in enterprise AI solutions: the key is to transform them from a simple answer generator into a sophisticated instrument that can also detect knowledge gaps. Enterprise AI solutions can be engineered to proactively identify questions which they do not have adequate information to answer and immediately flag these requests. This approach effectively creates a mandate for AI governance bodies to capture the needed knowledge. 

AI can move beyond just alerting the relevant teams about missing knowledge. As we will soon discuss, AI holds additional capabilities to close knowledge gaps by inferring new insights from disparate, already-known information, and connecting users directly with relevant human experts. This allows enterprise AI to not only identify knowledge voids, but also begin the process of bridging them.

Closing the Gap

It is important, at this point, to make the distinction between knowledge that is truly missing from the organization and knowledge that is simply unavailable to the organization’s AI solution. The approach to close the knowledge gap will hinge on this key distinction. 

If the ‘missing’ knowledge is documented or recorded somewhere… but the knowledge is not in a format that AI can use it, then:

Transform and migrate the present knowledge asset into a format that AI can more readily ingest. 

If the ‘missing’ knowledge is documented or recorded somewhere… but the knowledge exists in private spaces like email or closed forums, then:

Establish workflows and guidelines to promote, elevate, and institutionalize knowledge that had been previously informal.

If the ‘missing’ knowledge is documented or recorded somewhere… but the knowledge exists in different fragments across disjointed repositories, then: 

Unify the disparate fragments of knowledge by designing and applying a semantic model to associate and contextualize them. 

If the ‘missing’ knowledge is not recorded anywhere… but the company’s experts hold this knowledge with them, then: 

Choose the appropriate techniques to elicit knowledge from experts during high-value moments of knowledge capture. It is important to note that we can begin incorporating agentic solutions to help the organization capture institutional knowledge, especially when agents can know or infer expertise held by the organization’s people. 

If the ‘missing’ knowledge is not recorded anywhere… and the knowledge cannot be found, then:

Make sure to clearly define the knowledge gap and its impact on the AI solution as it supports the business. When it has substantial effects on the solution’s ability to provide critical responses, then it will be up to subject matter experts within the organization to devise a strategy to create, acquire, and institutionalize the missing knowledge. 

Conclusion

Identifying and closing knowledge gaps is no small feat, and predicting knowledge needs was nearly impossible before the advent of AI. Now, AI acts as both a driver and a solution, helping modern enterprises maintain their competitive edge.

Whether your critical knowledge is in people’s heads or buried in documents, Enterprise Knowledge can help. We’ll show you how to capture, connect, and leverage your company’s knowledge assets to their full potential to solve complex problems and obtain the results you expect out of your AI investments. Contact us today to learn how to bridge your knowledge gaps with AI.

The post How to Fill Your Knowledge Gaps to Ensure You’re AI-Ready appeared first on Enterprise Knowledge.