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.

Though the world is enthralled with the myriad of public AI solutions, many organizations struggle to make the leap to reliable AI within their organizations. A recent MIT report, “The GenAI Divide,” reveals a concerning truth: despite significant investments in AI, 95% of organizations are not seeing any benefits from their AI investments. 

One of the core impediments to achieving AI within your own organization is poor-quality content and data. Without the proper foundation of high-quality content and data, any AI solution will be rife with ‘hallucinations’ and errors. This will expose organizations to unacceptable risks, as AI tools may deliver incorrect or outdated information, leading to dangerous and costly outcomes. This is also why tools that perform well as demos fail to make the jump to production.  Even the most advanced AI won’t deliver acceptable results if an organization has not prepared their content and data.

This blog outlines seven top ways to ensure your content and data are AI-ready. With the right preparation and investment, your organization can successfully implement the latest AI technologies and deliver trustworthy, complete results.

1) Understand What You Mean by “Content” and/or “Data” (Knowledge Asset Definition)

While it seems obvious, the first step to ensuring your content and data are AI-ready is to clearly define what “content” and “data” mean within your organization. Many organizations use these terms interchangeably, while others use one as a parent term of the other. This obviously leads to a great deal of confusion. 

Leveraging the traditional definitions, we define content as unstructured information (ranging from files and documents to blocks of intranet text), and data as structured information (namely the rows and columns in databases and other applications like Customer Relationship Management systems, People Management systems, and Product Information Management systems). You are wasting the potential of AI if you’re not seeking to apply your AI to both content and data, giving end users complete and comprehensive information. In fact, we encourage organizations to think even more broadly, going beyond just content and data to consider all the organizational assets that can be leveraged by AI.

We’ve coined the term knowledge assets to express this. Knowledge assets comprise all the information and expertise an organization can use to create value. This includes not only content and data, but also the expertise of employees, business processes, facilities, equipment, and products. This manner of thinking quickly breaks down artificial silos within organizations, getting you to consider your assets collectively, rather than by type. Moving forward in this article, we’ll use the term knowledge assets in lieu of content and data to reinforce this point. Put simply and directly, each of the below steps to getting your content and data AI-ready should be considered from an enterprise perspective of knowledge assets, so rather than discretely developing content governance and data governance, you should define a comprehensive approach to knowledge asset governance. This approach will not only help you achieve AI-readiness, it will also help your organization to remove silos and redundancies in order to maximize enterprise efficiency and alignment.

2) Ensure Quality (Asset Cleanup)

We’ve found that most organizations are maintaining approximately 60-80% more information than they should, and in many cases, may not even be aware of what they still have. That means that four out of five knowledge assets are old, outdated, duplicate, or near-duplicate. 

There are many costs to this over-retention before even considering AI, including the administrative burden of maintaining this 80% (including the cost and environmental impact of unnecessary server storage), and the usability and findability cost to the organization’s end users when they go through obsolete knowledge assets.

The AI cost becomes even higher for several reasons. First, AI typically “white labels” the knowledge assets it finds. If a human were to find an old and outdated policy, they may recognize the old corporate branding on it, or note the date from several years ago on it, but when AI leverages the information within that knowledge asset and resurfaces it, it looks new and the contextual clues are lost.

Next, we have to consider the old adage of “garbage in, garbage out.” Incorrect knowledge assets fed to an AI tool will result in incorrect results, also known as hallucinations. While prompt engineering can be used to try to avoid these conflicts and, potentially even errors, the only surefire guarantee to avoid this issue is to ensure the accuracy of the original knowledge assets, or at least the vast majority of it.

Many AI models also struggle with near-duplicate “knowledge assets,” unable to discern which version is trusted. Consider your organization’s version control issues, working documents, data modeled with different assumptions, and iterations of large deliverables and reports that are all currently stored. Knowledge assets may go through countless iterations, and most of the time, all of these versions are saved. When ingested by AI, multiple versions present potential confusion and conflict, especially when these versions didn’t simply build on each other but were edited to improve findings or recommendations. Each of these, in every case, is an opportunity for AI to fail your organization.

Finally, this would also be the point at which you consider restructuring your assets for improved readability (both by humans and machines). This could include formatting (to lower cognitive lift and improve consistency) from a human perspective. For both humans and AI, this could also mean adding text and tags to better describe images and other non-text-based elements. From an AI perspective, in longer and more complex assets, proximity and order can have a negative impact on precision, so this could include restructuring documents to make them more linear, chronological, or topically aligned. This is not necessary or even important for all types of assets, but remains an important consideration especially for text-based and longer types of assets.

3) Fill Gaps (Tacit Knowledge Capture)

The next step to ensure AI readiness is to identify your gaps. At this point, you should be looking at your AI use cases and considering the questions you want AI to answer. In many cases, your current repositories of knowledge assets will not have all of the information necessary to answer those questions completely, especially in a structured, machine-readable format. This presents a risk itself, especially if the AI solution is unaware that it lacks the complete range of knowledge assets necessary and portrays incomplete or limited answers as definitive. 

Filling gaps in knowledge assets is extremely difficult. The first step is to identify what is missing. To invoke another old adage, organizations have long worried they “don’t know what they don’t know,” meaning they lack the organizational maturity to identify gaps in their own knowledge. This becomes a major challenge when proactively seeking to arm an AI solution with all the knowledge assets necessary to deliver complete and accurate answers. The good news, however, is that the process of getting knowledge assets AI-ready helps to identify gaps. In the next two sections, we cover semantic design and tagging. These steps, among others, can identify where there appears to be missing knowledge assets. In addition, given the iterative nature of designing and deploying AI solutions, the inability of AI to answer a question can trigger gap filling, as we cover later. 

Of course, once you’ve identified the gaps, the real challenge begins, in that the organization must then generate new knowledge assets (or locate “hidden” assets) to fill those gaps. There are many techniques for this, ranging from tacit knowledge capture, to content inventories, all of which collectively can help an organization move from AI to Knowledge Intelligence (KI).    

4) Add Structure and Context (Semantic Components)

Once the knowledge assets have been cleansed and gaps have been filled, the next step in the process is to structure them so that they can be related to each other correctly, with the appropriate context and meaning. This requires the use of semantic components, specifically, taxonomies and ontologies. Taxonomies deliver meaning and structure, helping AI to understand queries from users, relate knowledge assets based on the relationships between the words and phrases used within them, and leverage context to properly interpret synonyms and other “close” terms. Taxonomies can also house glossaries that further define words and phrases that AI can leverage in the generation of results.

Though often confused or conflated with taxonomies, ontologies deliver a much more advanced type of knowledge organization, which is both complementary to taxonomies and unique. Ontologies focus on defining relationships between knowledge assets and the systems that house them, enabling AI to make inferences. For instance:

<Person> works at <Company>

<Zach Wahl> works at <Enterprise Knowledge>

<Company> is expert in <Topic>

<Enterprise Knowledge> is expert in <AI Readiness>

From this, a simple inference based on structured logic can be made, which is that the person who works at the company is an expert in the topic: Zach Wahl is an expert in AI Readiness. More detailed ontologies can quickly fuel more complex inferences, allowing an organization’s AI solutions to connect disparate knowledge assets within an organization. In this way, ontologies enable AI solutions to traverse knowledge assets, more accurately make “assumptions,” and deliver more complete and cohesive answers. 

Collectively, you can consider these semantic components as an organizational map of what it does, who does it, and how. Semantic components can show an AI how to get where you want it to go without getting lost or taking wrong turns.

5) Semantic Model Application (Tagging)

Of course, it is not sufficient simply to design the semantic components; you must complete the process by applying them to your knowledge assets. If the semantic components are the map, applying semantic components as metadata is the GPS that allows you to use it easily and intuitively. This step is commonly a stumbling block for organizations, and again is why we are discussing knowledge assets rather than discrete areas like content and data. To best achieve AI readiness, all of your knowledge assets, regardless of their state (structured, unstructured, semi-structured, etc), must have consistent metadata applied against them. 

When applied properly, this consistent metadata becomes an additional layer of meaning and context for AI to leverage in pursuit of complete and correct answers. With the latest updates to leading taxonomy and ontology management systems, the process of automatically applying metadata or storing relationships between knowledge assets in metadata graphs is vastly improved, though still requires a human in the loop to ensure accuracy. Even so, what used to be a major hurdle in metadata application initiatives is much simpler than it used to be.

6) Address Access and Security (Unified Entitlements)

What happens when you finally deliver what your organization has been seeking, and give it the ability to collectively and completely serve their end users the knowledge assets they’ve been seeking? If this step is skipped, the answer is calamity. One of the express points of the value of AI is that it can uncover hidden gems in knowledge assets, make connections humans typically can’t, and combine disparate sources to build new knowledge assets and new answers within them. This is incredibly exciting, but also presents a massive organizational risk.

At present, many organizations have an incomplete or actually poor model for entitlements, or ensuring the right people see the right assets, and the wrong people do not. We consistently discover highly sensitive knowledge assets in various forms on organizational systems that should be secured but are not. Some of this takes the form of a discrete document, or a row of data in an application, which is surprisingly common but relatively easy to address. Even more of it is only visible when you take an enterprise view of an organization. 

For instance, Database A might contain anonymized health information about employees for insurance reporting purposes but maps to discrete unique identifiers. File B includes a table of those unique identifiers mapped against employee demographics. Application C houses the actual employee names and titles for the organizational chart, but also includes their unique identifier as a hidden field. The vast majority of humans would never find this connection, but AI is designed to do so and will unabashedly generate a massive lawsuit for your organization if you’re not careful.

If you have security and entitlement issues with your existing systems (and trust me, you do), AI will inadvertently discover them, connect the dots, and surface knowledge assets and connections between them that could be truly calamitous for your organization. Any AI readiness effort must confront this challenge, before your AI solutions shine a light on your existing security and entitlements issues.

7) Maintain Quality While Iteratively Improving (Governance)

Steps one through six describe how to get your knowledge assets ready for AI, but the final step gets your organization ready for AI. With a massive investment in both getting your knowledge assets in the right state for AI and in  the AI solution itself, the final step is to ensure ongoing quality of both. Mature organizations will invest in a core team to ensure knowledge assets go from AI-ready to AI-mature, including:

• Maintaining and enforcing the core tenets to ensure knowledge assets stay up-to-date and AI solutions are looking at trusted assets only;
• Reacting to hallucinations and unanswerable questions to fill gaps in knowledge assets; 
• Tuning the semantic components to stay up to date with organizational changes.

The most mature organizations, those wishing to become AI-Powered organizations, will look first to their knowledge assets as the key building block to drive success. Those organizations will seek ROCK (Relevant, Organizationally Contextualized, Complete, and Knowledge-Centric) knowledge assets as the first line to delivering Enterprise AI that can be truly transformative for the organization. 

If you’re seeking help to ensure your knowledge assets are AI-Ready, contact us at info@enterprise-knowledge.com. 

The post Top Ways to Get Your Content and Data Ready for AI appeared first on Enterprise Knowledge.

Enterprise Knowledge CEO Zach Wahl speaks with Jeff Vargas, former Director of Knowledge Management at Paramount.

In this conversation, Zach and Jeff discuss being KM evangelists and the trick to selling KM programs to executives. They get into “invisible KM,” talking about how everyone is doing KM but may not realize it, and how to find and boost those pockets of success. Jeff also shares the “lightbulb” moment that catapulted him from technical writing, to learning and development, and, ultimately, to knowledge management.

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

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

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

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

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

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

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

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

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

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

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

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

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

Enterprise Knowledge CEO Zach Wahl speaks with Sierra Woods, Public Servant in the Canadian Government.

In this conversation, Zach and Sierra discuss how to facilitate and “design” KM conversations to ask the right questions and reach the “a-ha” moment, developing durable skills in a hybrid work environment, what KM can do to help organizations thrive during periods of turbulent change, and what Sierra is looking forward to at Knowledge Summit Dublin.

For more information on Knowledge Summit Dublin, check it out here!

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

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Enterprise Knowledge CEO Zach Wahl speaks with Gianni Giacomelli, Head of Design Innovation for the Collective Intelligence Design Lab at MIT and founder of Supermind.Design.

In this conversation, Zach and Gianni discuss how KM goes beyond simple document management or SharePoint, how powering AI with a knowledge graph naturally mirrors how human brains work, Gianni’s work in the innovation space as a practitioner and an industry advisor for organizations like Goldman Sachs and BCG, and his keynote presentation at the upcoming Knowledge Summit Dublin conference in June.

For more information on Knowledge Summit Dublin, check it out here!

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

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Enterprise Knowledge CEO Zach Wahl again speaks with Barry Byrne, Global Head of Knowledge Management at Novartis and founder and organizer of the Knowledge Summit Dublin conference.

In this conversation, Zach and Barry discuss Barry’s growing knowledge management team at Novartis, how to measure KM success, and best practices for conducting (and scaling!) knowledge capture before valuable team members leave an organization. They also share what they’re most excited about at Knowledge Summit Dublin this year, especially the “salmon of knowledge.”

For more information on Knowledge Summit Dublin, check it out here!

Click ⁠here⁠ to listen to Barry’s first Knowledge Cast episode.

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

The post Knowledge Cast – Barry Byrne at Novartis (pt. 2) appeared first on Enterprise Knowledge.

Enterprise Knowledge CEO Zach Wahl speaks with Liam Fahey, Partner and Co-Founder at Leadership Forum LLC.

In this conversation, Zach and Liam discuss Liam’s claim to fame as co-host of the first Knowledge Management conference in 1994, the importance of nomenclature and terminology in securing buy-in for KM initiatives, and why having a KM charter is critical to long-term sustainment. The two also talked about the intersection of KM and intelligence work and how to become a good facilitator. Liam will be speaking at the upcoming Knowledge Summit Dublin in June 2025.

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

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Enterprise Knowledge CEO Zach Wahl speaks with Ahren Lehnert, Principal Taxonomist at Nike.

In this conversation, Zach and Ahren discuss the future of taxonomy and artificial intelligence (AI), emphasizing both the augmentation of traditional roles and growth to include new ones, and how to demonstrate the value of a taxonomy initiative for your organization more tangibly. They walk through highs and lows of the taxonomy development and refinement process and the importance of avoiding semantic debt to ensure adaptable solutions that stand the test of time. Ahren also shares his thoughts on the state of the market for semantic tools as the influence of AI/ML grows.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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