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only be actively maintaining 15-20% of their unstructured information, with the rest being duplicate, near-duplicate, outdated, or completely incorrect.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.

EK will evaluate your organization on the following five components of Enterprise AI, providing detailed reports on your assessed current state, desired target state, and customized roadmap activities to reach your target:

• Organizational Readiness
• Current State of Data & Content
• Technical Capabilities
• Skill Sets & Roles
• Operations & Sustainability

EK will create a detailed design and framework based on prioritized needs, including customized AI models and solutions architecture that leverage secure and sustainable AI tailored to your organization’s core data, content, and systems. We will then develop a customized, iterative, task-based plan to achieve organizational AI use cases, and implement your prioritized pilots that take into account your organization’s pain points, pilot value, and technical complexity.

By the end of the AI Readiness, Benchmarking & Strategy engagement, your organization will have:

• A deeper understanding of the building blocks needed to leverage AI across the enterprise.
• A completed assessment that will uncover existing gaps in any of the five core AI dimensions, enabling you to set clear organizational priorities to address them.
• Short-term, mid-term, and long-term goals for establishing, sustaining, and evolving your AI maturity.
• Measurable success criteria and key performance indicators (KPIs) for tracking progress over time.
• A roadmap and AI pilots backlog with fully customized, iterative, task-based plans to achieve your AI transformation, alongside considerations for making decisions that will prevent the accumulation of future technical debt.

The post AI Readiness Assessment, Benchmarking & Strategy appeared first on Enterprise Knowledge.