For the last several years, I’ve written this article on the Top Knowledge Management Trends of the year. As CEO of the world’s largest Knowledge Management consulting company, I’m lucky to get to witness these trends forming each year. As in past years, I brought together EK’s KM consultants and thought leaders to guide the development of this list. I looked at the rising themes we see from our clients and prospective clients, including burgeoning topics in requests for proposals we receive. As we’ve helped many of our leading clients develop multi-year KM Transformation roadmaps, or develop their annual priorities and budgets, key themes have taken shape. I’ve supplemented those insights with a series of interviews from KM leaders and practitioners (both internal and external), reviewed topics and discussions from the world’s KM conferences and publications, and evaluated briefings and product roadmaps from vendors across the KM, information management, content management, and data management software spaces. 

You can review my recent annual blogs for 2023, 2022, 2021, 2020, and 2019 to get a sense of how the world of KM has rapidly progressed.

The following are the top KM trends for 2024.

Artificial Intelligence, Obviously – It would be a real failure in thought leadership to simply present AI as a new KM trend and leave it as that, especially since I first identified that growing trend for KM back in 2019. However, the specific interplay and overlap between the two disciplines does merit some additional discussion, as there are new and exciting things happening here. 

First, organizations are continuing to recognize that their AI initiatives will fail without the appropriate building blocks in place. We’re not talking about black box AI here, but rather explainable AI that can be trusted by even the most risk-averse organizations. In these cases, traditional KM disciplines including knowledge capture and digital communities (to get the expert knowledge in a digital and ingestible form); content structuring (to ensure it is machine readable and configurable); taxonomies, ontologies, and content tagging (to ensure it can be categorized, related, and contextualized); and information governance (to ensure only the correct and appropriate information is utilized by the AI solution) can provide the necessary building blocks to make AI work. None of these KM topics are new, in fact most are decades old, but collectively they can lay the foundation for enterprise AI. With AI as a top executive priority, but many AI initiatives stalled or experiencing early failures, executives are open to revisiting the benefits of KM.

With AI as a top executive priority, but many AI initiatives stalled or experiencing early failures, executives are open to revisiting the benefits of KM.

The second KM and AI trend flips the first and focuses on leveraging AI to enhance and improve some traditional KM practices. Over the course of my now quarter-century in KM, there have been several early stumbling blocks to successful KM transformations, largely borne out of the highly labor-intensive nature of KM tasks like content cleanup, content tagging, and content restructuring. These tasks are critical to achieving high KM maturity for an organization, but they can take a massive effort to accomplish effectively. AI offers a solution by automating these and other critical but monotonous KM tasks, speeding up transformations while still delivering a high level of accuracy. This trend promises to drastically improve the speed and/or completion of KM and Digital Transformations.

As we progress more deeply into the KM/AI trend, there are three primary use cases that I expect will continue. The first, and most common we’ve seen move to production, is customized learning, where AI is being used to automatically assemble individual learning paths. This involves assembling formal and informal learning, access to experts, knowledge assets, and job aids into customized curricula for each individual learner. The second use case is leveraging AI for the assembly and creation of new knowledge articles, combining an organization’s knowledge, content, and data, into newer, richer, and more actionable knowledge assets. I address that in greater detail when speaking about Semantic Layers and Conversational KM later on. The last, and one that I’m particularly excited about, is the use of AI to identify and capture new knowledge from experts. This is not a new idea, but we’re beginning to see investment in this space that will allow solutions to identify risks related to human expertise leaving an organization, as well as the appropriate moments and mechanisms to capture that expertise so it may be preserved within the organization. This is an early trend, but it’s one I think we’ll be seeing a lot more of a year from now.

Focus on KM Doing What AI Can’t – I discuss above how KM can be an enabling factor for AI, and AI can be an accelerator for KM as well. Equally, there has been a lot of discussion about which jobs AI will replace. Though AI will do a lot to facilitate and accelerate KM efforts, the role of the (human) Knowledge Management Expert has never been more important within an organization. Though I have no doubt it will get there in time, AI simply can’t do what we can. To that end, the 2024 KM Trend here is a focus on these key gaps, largely 1) capturing expertise with context and interpretation, ensuring an organization is relying on accurate, current, and trusted information, 2) relating knowledge and facilitating people in ways that will foster collaboration, learning, and innovation, and 3) defining the ontologies and large language models to deliver a digital map of how business is done and the relationships that exist therein. None of these are new skills or topics, but at present they are deeply in demand and are highly valued by mature organizations. The first point will be of particular prominence this year; as organizations are increasingly successful in harnessing their collective knowledge, information, and data, the importance of tacit knowledge capture will surge for many organizations hoping to fill gaps in their organizational intelligence.

It is important to note that what the generative AI community cutely calls hallucinations are actually extremely problematic for an organization seeking AI. A hallucination is actually either a poorly designed connection, a gap in knowledge, or more likely an incorrect input (as in, old, obsolete, or just plain incorrect information). Knowledge Management professionals should be an organization’s hallucination assassins.

Knowledge Management professionals should be an organization’s hallucination assassins.

Content Structure and Quality – At the beginning of my career, the simple selling point for taxonomies and tagging was adding structure to unstructured information. Now, twenty-five years later, we’re still seeking to enhance our content, but the definition of content has broadened, and the structure we’re seeking is much more mature. The key theme here from the content perspective, as I covered last year, is that KM now covers all forms of content, from tacit to explicit, information to data, and including people, products, and processes all as discrete knowledge assets that can be included as part of an organization’s KM ecosystem. Structure has also progressed from the simple topics of taxonomy and tagging to the design of enterprise-level ontologies, content types, text analytics, and natural language processing to drive not just an understanding of each individual knowledge asset, but the relationships between them and within them.

Building the Semantic Layer – The semantic layer is also not a new concept, but it is quickly becoming one of the biggest trends in the overlapping space between KM, Data, and AI. In past years, I’ve written about the trend of Knowledge Graphs and how they’re enabling AI, and now semantic layers are set up to be the next, more powerful, step in that progression. A semantic layer is a standardized framework that organizes and abstracts organizational data (structured, unstructured, semi-structured) and serves as a connector for data and knowledge. It combines many of the core design elements of KM, namely information architecture, taxonomies, ontologies, metadata, and content types, along with traditionally data-centric elements like business glossaries and data catalogs to deliver highly contextualized, integrated knowledge at the point of need. If this is a new term for you, get ready to hear it a lot more. It is more than an enabler for AI, setting organizations up to realize longstanding KM goals of breaking down silos; connecting all forms of data, information, and knowledge with the people who need it; and leveraging analytics to fill gaps in knowledge and performance. In short, it is the solution that may finally deliver true enterprise knowledge for an organization. 

Renewed Executive Interest and Openness – I’ve noted in the past that executives were already more open to investment in KM due to the pandemic and subsequent trend toward remote/hybrid work, in addition to the “Great Resignation” and battle for talent. Adding to that, at present, is the massive focus on AI. The key to this trend is that it likely means bigger budgets for knowledge management, but only if you know what to listen for. Executives will be asking for the big AI solution, and they will be more specifically seeking automated content assembly, content cleanup, learning, and knowledge layers. The letters “K” and “M” may not be in the request, but mature KM professionals need to understand the ask and know the central role they play in delivering on it. Put simply, KM’ers can finally be the cool kids, but only if they know how to position KM where it belongs within the organization.

KM’ers can finally be the cool kids, but only if they know how to position KM where it belongs within the organization.

Conversational Knowledge Management – We’ve all been amazed by the conversational nature of ChatGPT that allows novice users to ask for answers to questions, images, ideas, and even code, conversing to clarify exactly what you want. As we jointly mature in KM and AI, we’re trending toward “conversational” KM solutions that expand on advanced search, knowledge portals, and intelligent chatbots to allow any user to interact with an organization’s knowledge assets and get increasingly pertinent and customized answers that will help them complete their mission. We’ve already delivered this for some of our more advanced customers, but both the associated technologies and the organizational use cases are hitting a point of inflection where conversational KM capabilities will quickly become the norm.

KM for Risk Identification and Mitigation – Historically, the value of KM has been difficult to express, but we’ve actually made great progress in that area by focusing on the business outcomes of KM, including improved productivity, cost reduction, employee retention, faster and better onboarding and learning, and customer retention, to name a few of the big ones. A new trend in KM comes with a new type of business value: risk identification and mitigation. KM can help identify and mitigate risks by leveraging comprehensive KM solutions to spot improperly secured or incorrect content. This is of particular value for highly regulated industries or those dealing with confidential information. The ROI on this is clear, as one accidental release of proprietary information can cost millions. Worse yet, the wrong information delivered about how to use a product can cost lives. There are other use cases specifically about spotting gaps in knowledge before they become dire, but in short, an enterprise approach to KM can allow an organization to better understand all of their knowledge, content, and data, allowing them to proactively address measurable risks that might occur. This final trend is particularly noteworthy given how easy it is to justify investment, delivering major impact for organizations.

Do you need help understanding and harnessing the value of these trends? Contact us to learn more and get started.

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As CEO of the world’s largest Knowledge Management consulting company, I am fortunate to possess a unique view of KM trends. For each of the last several years, I’ve written an annual list of these KM trends, and looking back, I’m pleased to have (mostly) been on point, having successfully identified such KM trends as Knowledge Graphs, the confluence of KM and Learning, the increasing focus on KM Return on Investment (ROI), and the use of KM as the foundation for Artificial Intelligence.

Every year in order to develop this list, I engage EK’s KM consultants and thought leaders to help me identify what trends merit inclusion. We consider factors including themes in requests for proposals and requests for information; the strategic plans and budgets of global organizations; priorities for KM transformations; internal organizational surveys; interviews with KM practitioners, organizational executives, and business stakeholders; themes from the world’s KM conferences and publications, interviews with fellow KM consultancies and KM software leaders; and the product roadmaps for leading KM technology vendors.

The following are the seven KM trends for 2023:

KM at a Crossroads – The last several years have seen a great deal of attention and funding for KM initiatives. Both the pandemic and great resignation caused executives to realize their historical lack of focus on KM resulted in knowledge loss, unhappy employees, and an inability to efficiently upskill new hires. At the same time, knowledge graphs matured to the point where KM systems could offer further customization and ability to integrate multiple types of content from disparate systems more easily.

In 2023, much of the world is bracing for a recession, with the United States and Europe likely to experience a major hit. Large organizations have been preparing for this already, with many proactively reducing their workforce and cutting costs. Historically, organizations have drastically reduced KM programs, or even cut them out entirely, during times of economic stress. In 2008-2009, for instance, organizational KM spending was gutted, and many in-house KM practitioners were laid off.

I anticipate many organizations will do the same this year, but far fewer than in past recessions. The organizations that learned their lessons from the pandemic and staffing shortages will continue to invest in KM, recognizing the critical business value offered. KM programs are much more visible and business critical than they were a decade ago, thanks to maturation in KM practices and technologies. Knowledge Management programs can deliver business resiliency and competitive advantage, ensure that knowledge is retained in the organization, and enable employee and customer satisfaction and resulting retention. The executives that recognize this will continue their investments in KM, perhaps scaled down or more tightly managed, but continued nonetheless. 

Less mature organizations, on the other hand, will repeat the same mistakes of the past, cutting KM, and with it, walking knowledge out the door, stifling innovation, and compounding retention issues, all for minimal and short-term savings. This KM trend, put simply, will be the divergence between organizations that compound their existing issues by cutting KM programs and those that keep calm and KM on.

Focus on Business Value and ROI – Keying off the previous trend, and revisiting a trend I’ve identified in past years, 2023 will bring a major need to quantify the value of KM. In growth years when economies are booming, we’ve typically seen a greater willingness for organizations to invest in KM efforts. This year, there will be a strong demand to prove the business value of KM. 

For KM practitioners, this means being able to measure business outcomes instead of just KM outcomes. Examples of KM outcomes are improved findability and discoverability of content, increased use and reuse of information, decreased knowledge loss, and improved organizational awareness and alignment. All of these things are valuable, as no CEO would say they don’t want them for their organization, and yet none of them are easily quantifiable and measurable in terms of ROI. Business outcomes, on the other hand, can be tied to meaningful and measurable savings, decreased costs, or improved revenues. Business outcomes resulting from KM transformations can include decreased storage and software license costs, improved employee and customer retention, faster and more effective employee upskilling, and improved sales and delivery. The KM programs that communicate value in terms of these and other business outcomes will be those that thrive this year.

This KM trend is a good one for the industry, as it will require that we put the benefits to the organization and end users at the center of any decision.

Knowledge Portals – Much to the surprise, if not disbelief, of many last year, I predicted that portals would make a comeback from their heyday in the early 2000’s. The past year validated this prediction, with more organizations making multi-year and multi-million dollar investments in KM transformations with a Knowledge Portal (or KM Portal) at the center of the effort. As I wrote about recently, both the critical awareness of KM practices as well as the technology necessary to make a Knowledge Portal work have come a long way in the last twenty years. Steered further by the aforementioned drivers of remote work and the great resignation, organizations are now implementing Knowledge Portals at the enterprise level. 

The use cases for Knowledge Portals vary, with some treating the system as an intranet or knowledge base, others using it as a hub for learning or sales, and still others using it more for tacit knowledge capture and collaboration. Regardless of the use cases, what makes these Knowledge Portals really work is the usage of Knowledge Graphs. Knowledge Graphs can link information assets from multiple applications and display them on a single screen without complicated and inflexible interface development. CIOs now have a way to do context-driven integration, and business units can now see all of the key information about their most critical assets in a single location. What this means is that Knowledge Portals can now solve the problem of application information silos, enabling an organization to collectively understand everything its people need to know about its most important knowledge assets.

Context-Driven KM – We’ve all heard the phrase, “Content is King,” but in today’s KM systems, Context is the new reigning monarch. The new trend in advanced knowledge systems is for them to be built not just around information architecture and content quality, but around knowledge graphs that provide a knowledge map of the organization. A business model and knowledge map expressed as an ontology delivers a flexible, expandable means of relating all of an organization’s knowledge assets, in context, and revealing them to users in a highly intuitive, customized manner. Put simply, this means that any given user can find what they’re looking for and discover that which they didn’t even know existed in ways that feel natural. Our own minds work in the same way as this technology, relating different memories, experiences, and thoughts. A system that can deliver on this same approach means an organization can finally harness the full breadth of information they possess across all of their locations, systems, and people for the purposes of collaboration, learning, efficiency, and discovery. Essentially, it’s what everyone has always wanted out of their information systems, and now it’s a reality.

Data Firmly in KM – Historically, most organizations have drawn a hard line between unstructured and structured information, managing them under different groups, in different systems, with different rules and governance structures. As the thinking around KM continues to expand, and KM systems continue to mature, this dichotomy will increasingly be a thing of the past. The most mature organizations today are looking at any piece of information, structured or unstructured, physical or digital, as a knowledge asset that can be connected and contextualized like any other. This includes people and their expertise, products, places, and projects. The broadening spectrum of KM is being driven by knowledge graphs and their expanding use cases, but it also means that topics like data governance, metadata hubs, data fabric, data mesh, data science, and artificial intelligence are entering the KM conversation. In short, the days of arguing that an organization’s data is outside the realm of a KM transformation are over.

Push Over Pull – When considering KM systems and technology, the vast majority of the discussion has centered around findability and discoverability. We’ve often talked about KM systems making it easier for the right people to find the information they need to do their jobs. As KM technologies mature, the way we think about connecting people and the knowledge they need is shifting. Rather than just asking, “How can we enable people to find the right information?”, we can also think more seriously about how we proactively deliver the right information to those people. This concept is not new, but the ability to deliver on it is increasingly real and powerful.

When we combine an understanding of all of our content in context, with an understanding of our people and analytics to inform us how people are interacting with that content and what content is new or changing, we’re able to begin predictively delivering content to the right people. Sometimes, this is relatively basic, providing the classic “users who looked at this product also looked at…” functionality by matching metadata and/or user types, but increasingly it can leverage graphs and analytics to recognize when a piece of content has changed or a new piece of content of a particular type or topic has been created, triggering a push to the people the system predicts could use that information or may wish to be aware of it. Consider a user who last year leveraged twelve pieces of content to research a report they authored and published. An intelligent system can recognize the author should be notified if one of the twelve pieces of source content has changed, potentially suggesting to the content author they should revisit their report and update it.

Overall, the trend we’re seeing here is about Intelligent Delivery of content and leveraging AI, Machine Learning, and Advanced Content Analytics in order to deliver the right content to individuals based on what we know and can infer about them. We’re seeing this much more as a prioritized goal within organizations but also as a feature software vendors are seeking to include in their products.

Personalized KM – With all the talk of improved technology, delivery, and context, the last trend is more of a summary of trends. KM, and KM systems, are increasingly customized to the individual being asked to share, create, or find/leverage content. Different users have different missions, with some more consumers of knowledge within an organization and others more creators or suppliers of that knowledge. Advanced KM processes and systems will recognize a user’s responsibility and mandates and will enable them to perform and deliver in the most intuitive and seamless way possible. 

This trend has a lot to do with content assembly and flexible content delivery. It means that, with the right knowledge about the user, today’s KM solutions can assemble only that information that pertains to the user, removing all of the detritus that surrounds it. For instance, an employee doesn’t need to wade through hundreds of pages of an employee handbook that aren’t pertinent to them; instead, they should receive an automatically generated version specifically for their location, role, and benefits.

The customized KM trend isn’t just about consuming information, however. More powerfully, it is also about driving knowledge sharing behaviors. For example, any good project manager should capture lessons learned at the end of a project, yet we often see organizations fail to get their PMs to do this consistently. A well-designed KM system will recognize an individual as a PM, understand the context of the projects they are managing, and be able to leverage data to know when that project is completed, thereby prompting the user with a specific lessons learned template at the appropriate time to capture that new set of information as content. That is customized KM. It becomes part of the natural work and operations of systems, and it makes it easier for a user to “do the right thing” because the processes and systems are engineered specifically to the roles and responsibilities of the individual.

Another way of thinking about these trends is by invoking the phrase “KM at the Point of Need,” derived from a phrase popularized in the learning space (Learning at the Point of Need). We’re seeing KM head toward delivering highly contextualized experiences and knowledge to the individual user at the time and in the way they need it and want it. What this means is that KM becomes more natural, more simply the way that business is done rather than a conscious or deliberate act of “doing KM.” This is exciting for the field, and it represents true business value and transformation.

Do you need help understanding and harnessing the value of these trends? Contact us to learn more and get started.

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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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The future of enterprise data management and architecture is evolving rapidly in some areas and returning to core principles in others. Based on our experience through our engagements across industries, diverse projects and client use cases, and our vendor partnerships, we continue to have the opportunity to observe and address the dynamic challenges organizations are facing in managing and getting value out of their data. These interactions, coupled with inputs from our advisory board, are helping us gain a good picture of the evolving landscape. 

Drawing from these sources, I have identified the key trends in the data management and architecture space that we expect to see in 2025. Overall, these trends highlight how organizations are adapting to technological advancements while shifting towards a more holistic approach – focusing on people, processes, and standards – to maximize returns on their data investments.

1. Wider Adoption of a Business or Domain-Focused Data Strategy

The conventional approach to data management architecture often involved a monolithic architecture, with centralized data repositories and standardized reporting systems that served the entire organization. While this worked for basic reporting and operational needs, the last decade has proven that such a solution couldn’t keep pace with the complexities of modern businesses. In recent years, a more agile and dynamic approach has gained momentum (and adoption) – one that is putting the business first. This shift is driven by the growing need not only to manage vast and diverse data but also to address the persistent challenge of minimizing data duplication while making data actionable, relevant, and directly aligned with the needs of specific business users.

A Business or Domain-Focused data strategy approach emphasizes decentralized data ownership and federated governance across various business domains (e.g., customer service, HR, sales, operations) – where each domain or department owns “fit-for-purpose” tools and the data within. As a result, data is organized and managed by the business function it supports, rather than by data type or format. 

This has been an emerging trend for a couple of years and part of the data mesh architecture. It is now gaining traction through the wider adoption of business-aligned data products or data domains in support of business processes – where data products empower individual business units to standardize and contextualize their data and derive actionable insights without heavy reliance on central IT, data teams, or enterprise-wide platforms. Why is this happening now? We are seeing two key drivers fueling the growing adoption of this strategy:

1. The shift in focus from the physical data to descriptive metadata, and the advancement in the corresponding solutions that enable this approach (such as a semantic layer or data fabric architectures that connect domain-specific data platforms without the need for data duplication or migration); and 
2. The rise of Artificial Intelligence (AI), specifically Named Entity Recognition (NER), Natural Language Processing (NLP), Large Language Models (LLMs) and Machine Learning (ML) – playing a pivotal role in augmenting organizational capabilities with automation.

As a result, we are starting to see the traditional method of relying on static reports and dashboards becoming obsolete. By integrating the federated capabilities and trends discussed below, we anticipate organizations moving beyond static reporting dashboards to the ability to “talk” to their data in a more dynamic and reliable way.

2. Semantic Layer Data Architecture

One of the key concepts that is significantly fueling the adoption of modern data stacks today is the “zero-copy” principle – building a data architecture that greatly reduces or eliminates the need to copy data from one system to another, thus allowing organizations to access and analyze data from multiple sources in real-time without duplicating it. This principle is changing how organizations manage and interact with their data.

In 2020, I first discussed Semantic Layer Architecture through a white paper I published called, What is a Semantic Architecture and How do I Build One?. In 2021, Gartner dubbed it “a data fabric/data mesh architecture and key to modernizing enterprise data management.” As the field continues to evolve, technical capabilities are advancing semantic solutions. A semantic layer in data architecture takes a metadata-first approach and is becoming an essential component of modern data architectures, enabling organizations to simplify data access, improve consistency, and enhance data governance. 

From an architect’s point of view, a semantic layer architecture adds significant value to modern data architecture and it is becoming a trend organizations are embracing – primarily because it provides the framework for addressing these traditional challenges for the data organization:

• Business alignment through standardized metadata by translating business context and relationships between raw data through metadata and ontology, making it ‘machine reliable’; 
• Simplified data access for business users through shared vocabulary (taxonomy);
• Enhanced data connection and interoperability through a virtualized access and central source of “view” that connects data (through metadata) from various sources without requiring the physical movement of data; 
• Improved data governance and security by enforcing the application of consistent business definitions, metrics, and data access rules to data; and
• The flexibility to future-proof data architecture by decoupling the complexities of data storage and presentation facilitates a zero-copy principle and ensures data remains where it is stored, without unnecessary duplication or replication, This helps organizations create a virtualized layer to address the challenges of working with diverse data from multiple sources while maintaining consistency and usability.

This trend reflects a broader shift from legacy application/system-centric architecture to a more data-centric approach where data doesn’t lose its meaning and context when taken out of a spreadsheet, a document, SQL table, or a data platform – helping organizations unlock the true potential of their knowledge and data.

3. Consolidation & Rebundling of Data Platforms 

The enterprise data technology landscape has been going after the “modern data stack” strategy, characterized by a best-of-breed approach, where organizations adopt specialized tools from various vendors to fulfill different needs – be it data storage, analytics, data cataloging and discovery, or AI. However, with the growing complexity of managing multiple platforms and tighter budgets, organizations are facing mounting pressures to optimize. 

Much akin to the retro experience that we’re seeing within the TV streaming industry, the landscape of data technologies is undergoing a significant shift – one of rebundling. This change is primarily driven by the need to simplify data management solutions in order to handle increasing organizational data complexity, optimize the costs associated with data storage and IT infrastructure across multiple vendors, and enhance the ability to experiment with and extract value from AI.

As a result, we are seeing the pace of technology bundling and mergers and acquisitions accelerating as large, well-established data platforms are acquiring smaller, specialized vendors and offering integrated, end-to-end solutions that aim to simplify data management. One good, well-publicized example of this is Salesforce’s recent bundling with and acquisition of various vendors to unveil the Unlimited Edition+ bundle, which provides access across Slack, Tableau, Sales Cloud, Service Cloud, Einstein AI, Data Cloud, and more, all in a single offering. In a recent article, my colleague further discussed the ongoing consolidation in the semantic data software industry, highlighting how the sector is increasingly recognizing the importance of semantics and how well-funded software companies are acquiring many independent vendors in this space to provide more comprehensive semantic layer solutions to their customers.

In 2025, we expect more acquisitions to be on the horizon. For CIOs and CDAOs looking to take advantage of this trend, there are important factors to consider. 

Limitations and Known Challenges:

• Complexity in data migration: Migrating data from multiple platforms into a unified one is a resource-intensive process. Such transitions typically introduce disruptions to business operations, leading to downtime or performance issues during the shift. 
• Data interoperability: The ability of different data systems, platforms, applications, and organizations to exchange, interpret, and use data seamlessly across various environments is paramount in today’s data landscape. This interoperability ensures data flows without losing its meaning, whether within an organization (e.g., between departments and various systems) or externally (e.g., regulatory reporting). Single-vendor technology bundles are often optimized for internal use, and they can limit data exchange with external systems or other vendors’ tools. This creates challenges and costs when trying to integrate non-vendor systems or migrate to new platforms. To mitigate these risks, it’s important for organizations to adopt solutions based on standardized data formats and protocols, invest in middleware and APIs for integration, and leverage cloud-based systems that support open standards and external system compatibility.
• Potential vendor lock: By committing to a single platform, organizations often become overly dependent on a specific vendor’s technology for all their data needs. This limits the data organization’s flexibility, especially when new tools or platforms are required, forcing the use of a proprietary solution that may no longer meet your evolving business needs. Relying on one platform also restricts data access and complicates integration with other systems, hindering the ability to gain holistic insights across your organizational data assets.

Benefit Areas:

• Better control over security and compliance: As businesses integrate AI and other advanced technologies into their data stacks, having a consolidated security framework is particularly top of mind. Facilitating this simplification through a unified platform reduces the risks associated with managing security across multiple platforms and helps ensure better compliance with regulatory data security requirements.
• Streamlined access and entitlement management: Consolidating the management of organizational access to data, roles, and permissions allows administrators to unify user access to data and content across applications within a suite – typically from a central dashboard, making it easier to enforce consistent access policies across all connected applications. This streamlines better management to prevent unauthorized access to critical data. It helps ensure that only authorized users have the appropriate access to diverse types of data, including AI models, algorithms, and media, strengthening the organization’s overall security posture.
• Simplified vendor management: Using a single vendor for a bundled suite reduces the administrative complexity of managing multiple vendors, which sometimes involves different support processes, protocols, and system compatibility issues. A unified data platform provides a more streamlined approach to handling data across systems and a single point of contact for support or troubleshooting.

When properly managed, bundling has its benefits; the focus should be on finding the balance, ensuring that data interoperability concerns are addressed while still leveraging the advantages of bundled solutions. Depending on the priority for your organization, this trend will be beneficial to watch (and adopt) for your streamlined data landscape and architecture.

4. Refocused Investments in Complementary AI Technologies (Beyond LLMs)

While LLMs have garnered significant attention in conversational AI and content generation, organizations are now recognizing that their data management challenges require more specialized, nuanced, and somewhat ‘traditional’ AI tools that address the gaps in explainability, precision, and the ability to align LLMs with organizational context and business rules. 

Despite the draw to AI’s potential, many organizations prioritize the reliability and trustworthiness of traditional knowledge assets. They also want to integrate human intelligence, ensuring that an organization’s collective knowledge – including people’s experience and expertise – is fully captured. We refer to this as Knowledge Intelligence (KI) rather than just AI, to indicate the integration of tacit knowledge and human intelligence with AI, thereby capturing the deepest and most valuable information within an organization.

As such, organizations have started reinvesting in Natural Language Processing (NLP), Named Entity Recognition (NER), and Machine Learning (ML) capabilities, realizing that these complementary AI tools are just as essential in tackling their complex enterprise data and knowledge management (KM) use cases. Specifically, we are seeing this trend reemerging to embrace the advancements in AI capabilities for enabling the following key priorities for the enterprise. 

• Expert Knowledge Capture & Transfer: Programmatically encoding expert knowledge and business context in structured data & AI;
• Knowledge Extraction: Federated connection and aggregation of organizational knowledge assets (unstructured, structured, and semi-structured sources) for knowledge extraction; and
• Business Context Embedding: Providing standardized meaning and context to data and all knowledge assets in a machine-readable format.

We see this renewed focus in holistic AI technologies as more than just a passing shift, it is marking a pivotal trend in the world of enterprise data management as a strategic move toward more reliable, intelligent, and efficient information and data management. 

For organizations looking to enhance their ability to extract value from experts and diverse data and content assets, the trend in comprehensive AI capabilities facilitates this integration and ensures that AI can operate not just as a tool, but as an intelligent organizational partner that understands the unique nuances of an organization – ultimately delivering knowledge and intelligence to the data organization.

5. A Unified Approach to Data and Content Management: Data & Analytics Teams Meet Unstructured Content & Knowledge Management

One of the most subtle yet significant changes we have been seeing over the last 2-3 years is the blending of traditionally siloed data management functions. In particular, the boundary between data and knowledge management teams is increasingly dissolving, with data and analytics professionals now addressing challenges that were once primarily the domain of KM. This shift is largely due to the growing recognition that organizations need a more cohesive approach to handling both structured and unstructured content.

Just a few years back, data management was largely a function of structured data, confined to databases and well-defined formats and handled by data engineers, data analysts, and data governance officers. Knowledge and content management, on the other hand, dealt primarily with unstructured content such as documents, emails, and multimedia, managed by different teams including knowledge officers and document management specialists. 

However, in 2025, as organizations continue to strive for a more flexible approach to benefit from their overall organizational knowledge assets, we are witnessing a convergence where data teams are now actively engaged in managing unstructured knowledge. With advancements in GenAI, machine learning, NER, and NLP technologies, data and analytics teams are now expected to not only manage and analyze structured data but also tackle the complexities of unstructured content – ranging from documents, emails, text, and social media posts to contracts and video files. 

By bridging the gap between data teams and business-oriented KM teams, organizations are able to better connect technical initiatives to actual use cases for employees, customers, and their stakeholders. For example, we are seeing a successful adoption of this trend with the data & analytics teams at a large global retailer. We are supporting their content and information management teams’ ability to enable the data teams with a knowledge and semantic framework to aggregate and connect traditionally siloed data and unstructured content. The KM team is doing this by providing knowledge models and semantic standards such as metadata, business glossaries, and taxonomy/ontology (as part of a semantic layer architecture) – explicitly providing business context for data, categorizing and labeling unstructured content, and providing the business logic and context for data used in their AI algorithms.

In 2025, we expect to see this trend to become more common for many organizations looking to enable cross-functional collaboration, with traditional data and IM offices starting to converge and professionals from diverse backgrounds working together to manage both structured and unstructured data.

5. Shift in Organizational Roles: From Governance to Enablement

This trend reflects how the previously mentioned shifts are becoming a reality within enterprises. As organizations embrace a more integrated approach to connecting overall organizational knowledge assets, the roles within the organization are also shifting. Traditionally, data governance teams, officers, and compliance specialists have been the gatekeepers of data quality, privacy, and security. While these roles remain crucial, the focus is increasingly shifting toward enablement rather than control.

Additionally, knowledge managers are steadily growing beyond their traditional role of providing the framework for sharing, applying, and managing the knowledge and information of an organization. They are now also serving as the providers of business context to data teams and advancements in Artificial Intelligence (AI). This heightened visibility for KM has pushed the industry to identify more optimized ways to organize teams and measure and convey their value to organizational leaders. On top of that, AI has been fueling the democratization of knowledge and data, leading to a growing recognition of the interdependence between data, information, and knowledge management teams. 

This is what is driving the evolution of roles within KM and data from governance and control to enablement. These roles are moving away from strict oversight and regulation and towards fostering collaboration, access, and self-sufficiency across the organization. Data officers and KM teams will continue to play a critical role in setting the standards for data quality, privacy, and security. However, as their roles shift from governance to enablement, these teams will increasingly focus on establishing frameworks that support transparency, collaboration, and compliance across a more data-centric enterprise – availing self-service analytics tools that allow even non-technical staff to analyze data and generate insights independently.

As we enter 2025, the landscape of enterprise data management is being reshaped by shifts in strategy, architecture, platform focus, and the convergence of data and knowledge management teams. These changes reflect how organizations are moving from siloed approaches to a more connected, enablement-driven model. By leveraging a combination of AI-powered tools, self-service capabilities, and evolving governance practices, organizations are unlocking the full value of their data and knowledge assets. This transformation will enable faster, more informed decision-making, helping companies stay ahead in an increasingly competitive and rapidly evolving business environment.

How do these trends translate to your specific data organization and landscape? Is your organization embracing these trends? Read more or contact us to learn more and grow your data organization.

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“Knowledge graphs, presently, are one of the keys to successful implementation of Knowledge AI. I was happy to share EK’s experience putting these exciting concepts and technologies into practice for our clients,” said Hilger.

Database Trends and Applications is a magazine covering data and information management, big data, and data science. In addition, their website connects visitors with white papers, webinars, and other learning opportunities in the field. The magazine and website deliver advanced trends analysis and case studies serving the IT and business stakeholders of complex data environments.

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These seven KM trends for 2021 are largely informed by our experience as the largest Knowledge Management Consulting company globally. The trends I identified are based on what we’re seeing from our customers, the new enquiries we’re getting for services, the trends and priorities of our clients, the trends we’re seeing in our proprietary KM maturity benchmark, and the internal surveys we’ve run. These trends represent where KM is today and where I see it going in the years to come.

The first two of these trends (demand for ROI and Artificial Intelligence) are very similar to items from my KM Trends in 2019 and KM Trends in 2020 articles. As I noted last year, these two items are likely to continue trending for the foreseeable future. The remaining five trends pick up some themes from previous years, but certainly reflect more of what’s happening dynamically within KM today. 

Quantifying the Business Value and ROI of Knowledge Management – As I’ve covered in past years, KM initiatives have often suffered from being considered as “nice-to-haves” within an organization, rather than business critical elements that will ensure competitive advantage and quantified business returns. In today’s world, the days of overly academic or theoretical KM have thankfully yielded to an understanding of Enterprise Knowledge Management, comprising and enabling your People’s performance and expertise, the effective application of business Processes, the stewardship and enhancement of all forms of Content, the development and support for company Culture, and the design and integration of enabling Technology to ensure KM is accessible and actionable for your business. This new and comprehensive understanding of KM comes with a requirement that its value is quantifiable and traceable to tangible return on investment for the organization. As more and more organizations are investing in multi-year KM transformations to address their business challenges, they are also rightfully demanding proof that these investments will be worthwhile. EK has invested heavily in metrics and calculators to quantify the value of KM, and these data points, along with a consistent plan to measure the value of KM within your organization, must be a part of any true KM effort.

KM as the Foundation for Artificial Intelligence – Organizations across the globe recognize that Artificial Intelligence must be a priority for them. Many organizations, however, have already realized that they’re ill prepared to seize the opportunities the latest advances in AI offer. What good is a chatbot if it is leveraging incorrect data and content to provide answers? What value does a content assembly engine provide if its source content is old or incorrect? What’s the point of creating an automated expertise locator if you don’t have consistent ways of describing or quantifying the competencies your organization possesses? In order to make AI work for your organization, core KM foundations are critical predecessors. These include the design and maintenance of taxonomies, content governance, cleanup and formatting, definition of content types and search hit types, and automated classification and auto-tagging strategies to ensure connectedness and machine readability of unstructured content across your multiple content repositories. Organizations are quickly realizing that they need to get their KM house in order before they can possibly realize meaningful value from AI.

Knowledge Graphs, Knowledge Graphs, and more Knowledge Graphs – Last year I discussed knowledge graphs as one of the means by which organizations are beginning to realize Knowledge AI capabilities. However, this field has quickly exploded and is one of the key elements on the roadmaps of many organizations, meriting its own unique placement on this list. A knowledge graph is a representation of an organization’s knowledge, work, and work products that can be understood and “read” by both humans and machines. It is a collection of references to your organization’s knowledge assets, content, and data that leverages an ontology to describe your organization’s people, places, and things and how they are related. Knowledge graphs can be used to power smart search, chatbots, recommendation engines, content assembly tools, and a variety of other high-value use cases. Just like AI, knowledge graphs require solid KM foundations, and we’re seeing the organizations who have previously and consistently invested in their KM maturity as those same organizations that are putting valuable knowledge graphs into production most successfully. This trend is particularly important as it provides a proven model for KM to enhance the value of big data by embedding context and tacit/explicit knowledge, thereby serving as a focused product that makes KM real and visible within an organization and elevates its importance in tangible terms.

Refocus on Enterprise Search – Search has experienced its ebbs and flows, but is presently trending strongly as a critical component of a KM transformation, if not the most visible and central element to an organization’s KM initiatives. There are a couple drivers for this. First, the pandemic and consequent sudden dispersion of workforces resulted in a profound realization by many organizations that their newly remote workforce couldn’t easily find the materials they needed to perform their job. This has, of course, been a longstanding issue for many organizations, but one that colocation in offices and cubes helped to shield. Before the pandemic, many employees reported the number one means of finding information within their organizations was social. They’d ask the people they knew for help on what information existed and where to find it, and in turn, those people would ask others. This is a highly inefficient process, but for many organizations it hid their larger findability challenges. The pandemic, however, created physical and geographic barriers, especially for newer employees, resulting in this newfound organizational awareness that they had a search problem. The second big driver for search regaining its prominence is the realization by many organizations that they will never be able to fully consolidate their knowledge, information, and data into a single, or even a few systems. Promises of data lakes and single master content management systems have failed, and so organizations are again focused on uniting content via search instead of trying to physically bring it all together. The final reason search is now trending is that the technology has made another leap, introducing conversational search, graph search, and natural language search as new features that can make search better and more intuitive if designed correctly.

KM as an Enabler for Remote Work – I’ve written previously about the value of KM to remote work, and this past year has served as an unfortunate proof point for this. Organizations suddenly went remote and saw productivity, collaboration, and culture fall behind. KM can enable and improve findability, connections, collaboration, and culture for organizations that are either choosing or being forced to go remote. This concept is not new, but the pandemic made this gap in most organization’s readiness for remote work abundantly clear. Now, some organizations are choosing not to return to their physical offices, necessitating new maturity around KM to enable their long-term ability to perform and learn. This trend, like the Knowledge Graphs trend above, is responsible for greater visibility and recognition for the importance of KM as a dedicated strategy and investment.

360 Views – If you’re a jargon-watcher, you have probably noticed the rapid increase in mentions of “360 Degree Views,” “Customer 360,” “Client 360,” “Company 360,” and “Employee 360.” The concept behind these new marketing terms is the idea of a single, consolidated view of all knowledge, information, and data regarding your entities. Though 360 degree views aren’t typically being discussed in the context of KM, at their core they are very much KM concepts and require KM solutions to make them real. We’re seeing an increase in organizations seeking an Employee 360 Solution and, moreover, recognizing that such a solution will require KM foundations and will itself address existing KM challenges. If designed and implemented correctly, Employee 360 will offer a single integrated view of an employee from the time they are hired, through the work they are doing, the competencies they possess and the competencies they should possess, and through to the time they leave the company (and potentially beyond). It enables better KM by providing a single place to find everything an employee knows and what they have worked on, and integrates Enterprise Learning by powering tools to help the employee develop new skills and competencies so they may progress through the organization. You should expect to hear more about 360 views over the next year, and when you do, look for the KM foundations and solutions that will help to make it real.

Executive Focus – As the individual trends above should express, KM itself is hot again. Executives up to C-levels are setting KM and the associated items discussed above as organizational priorities and investing at levels we haven’t seen before. Services organizations are taking note and working to improve their own KM capabilities, both for themselves as well as for their offerings. Of course the industry has been here before. Executive interest doesn’t mean there isn’t a healthy amount of skepticism and outright lack of trust in KM based on past organizational failures resulting from overly academic and complex KM initiatives. However, the circumstances in business today as well as marked leaps in technology mean that the letters K and M are ones increasingly uttered in C-suites.

As a bonus eighth trend, one that I see as an emerging trend, is Headless CMS and omnichannel delivery, as additional leaps in technology and process gain greater prominence over the year to come. A headless CMS is a platform that facilitates the authoring and management of content that can later be consumed through multiple channels (hence, omnichannel). You can think of a headless CMS as a content repository database that can serve its content to multiple channels and to multiple types of clients. A headless CMS can be viewed as an extensive collection of web services focused on the authoring, management, and retrieval activities of the content management lifecycle, with a UI catering to content authors. Headless CMS and omnichannel is not exclusively a KM trend, but it does introduce a great deal of functionality that fits within the KM realm, including content customization, content assembly, content delivery, and UI customization. 

Overall, there are a few important themes to pull out from all of this. First, I noted last year that there was an increasing trend of the recognition that KM can and should be enabled by technology and that the right technology can be how an organization realizes the most significant business value from overarching KM programs. The fact that we’re now talking about multiple KM technology trends in search, knowledge graphs, Headless CMS, and AI overall continue this trend and put it into focus. The other theme of note is the overall recognition of KM within an organization. Many of these trends demonstrate the common fact that KM is increasingly central to an organization, shifting from a peripheral nice-to-have to a recognized enterprise-wide initiative. KM, definitively, is on the rise (again), and positioned to enable organizational initiatives and power sweeping changes in how we work, perform, and innovate.

If you’re looking for help in realizing business value from any of these KM trends, or are seeking guidance on your own organization’s transformation, get in touch with EK. We’re ready to help.

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The field of knowledge management continues to evolve quickly, embracing new disciplines including semantic technologies and artificial intelligence as core parts of the growing field. Based on our experience as the largest Knowledge Management Consulting company globally, I’ve once again defined the trends that I believe we can expect to see increasing over the next year and beyond. 

The first two of these five trends (demand for ROI and Artificial Intelligence) are very similar to items from the KM Trends in 2019 article I wrote last year. I don’t anticipate those falling off the list any time soon. The remaining three certainly continue themes from previous years, but are formulated more directly to address the most immediate knowledge management trends of today.

Demand for clear business value and return on investment in KM efforts – Last year when I wrote about this, I noted through my years of KM Consulting, I’d seen KM efforts lose funding or be deprioritized when the economy took a hit. I have spent these years stressing the criticality of tying KM to tangible business value, measurable success, and hard return on investment for knowledge management initiatives. Good KM measurably results in improved productivity, improved customer and employee satisfaction, increased revenues, preparedness for artificial intelligence, and effective remote work. Every KM project should’ve already justified its existence by showing these connections. Now, in this time of global pandemic and economic uncertainty the importance of proving the critical value KM offers is more important than ever. The right KM efforts for an organization will help organizations be more agile and perform more effectively in the worst of times, and any smart CEO wouldn’t dare cut those business critical initiatives.

Clear understanding of Knowledge Management and Enterprise AI powered by ontologies and knowledge graphs – Increasingly, one of those tangible business benefits for KM is that it lays the foundation for real artificial intelligence within an organization. Foundational KM activities like taxonomy and tagging, content types and content cleanup, content governance, and tacit knowledge capture are all critical to an organization’s goals of connecting their knowledge, content, and data and automating ways of pushing it to the right users and assembling it for greater value and action. With a good KM foundation, AI isn’t something that organizations can dream of for another day. This is achievable now.

Acceptance and recognition of the enabling role of technology in KM – I think, for too long in the KM space, the impression of KM is that it is a “soft” skill, and too many KM practitioners gladly reinforce that concept. These issues have actually exacerbated my first theme above, regarding the linkage of KM and business value. The reality is that KM is a mix of “soft” and “hard” skills and the best KM efforts bring these together. Good KM, therefore, encompasses a mix of tacit knowledge, unstructured content, and structured data, and should leverage today’s technologies to more effectively capture, manage, share, relate, and find that mix of knowledge, information, and data. To be clear, technology is still just an enabling factor to successful KM; that’s why we list technology last in our five components of KM (People, Process, Content, Culture, and Technology). However, the field as a whole is increasingly recognizing the integral nature of this technology. 

Improved understanding of the knowledge ecosystem including all types of knowledge, information, and data – Over the years, the KM consulting “ask” from clients has moved from, “I want to be able to effectively capture, manage, and find my files,” to “I want to be able to effectively capture, manage, and find my files and data,” to “I want to be able to effectively capture, manage, and find everything together.” Where we are now is a clear recognition that the most mature organizations will be able to consolidate, present, find, discover, and relate all of their different types of content (with content loosely defined and including files, data, knowledge, collaborative materials, and even people). This enables paths of discovery where an end user can traverse content, data, and people in order to find all of the content that can help them complete their immediate mission and develop their knowledge over the longer-term. This is the true hallmark of a mature KM organization, leveraging everything they have, making it easily and intuitively available to their people, connecting it so it is enhanced and contextualized, and allowing their people to act on it in ways that feel natural and complete their goals.

Recognition of KM organizations and mandates as a critical success factor – As the last top KM trend of 2020, it is important to note that KM doesn’t just happen in organizations. For years, organizations have asked individuals to practice “hero KM,” in their free time or offered a big title like Chief Knowledge Officer without the authority, reporting lines, or staff to effect change. Today, we are thankfully seeing the trend that organizations are moving more toward building functional KM organizations. This trend is likely fueled by some of the others I’ve noted, namely the recognition of KM business value and its critical role at the center of AI. Organizational design is thereby increasingly overlapping with KM efforts, where designing, training, and coaching the fledgling KM unit with an organization and the successful establishment of such a unit is a critical success factor to broader KM.

When you put these trends together, you see that KM and business are consistently coming together, placing knowledge management at the center of an organization’s strategies for effectiveness and efficiency.

If you’re looking for help with bringing these trends and their business benefits to your organization, contact us.

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This annual listing of trends is derived from EK’s collective intelligence as the world’s largest dedicated Knowledge Management consultancy. Factors we consider include what we’re seeing in requests for proposals and requests for information; the strategic plans of organizations; priorities for KM transformations; internal surveys; interviews with KM practitioners, organizational executives, and business stakeholders; and the product roadmaps for leading KM technology vendors. 

These seven trends represent where Knowledge Management is now and where it is heading, not just over the course of the year, but beyond. 

Knowledge Graphs Move to the Enterprise – For the last several years of my KM trends articles, I’ve written about Knowledge Graphs. In past years, I’ve identified Knowledge Graphs as a growing trend, one where organizations were beginning to realize their value and experiment with their design and implementation. This year, I am proclaiming 2022 as the Year of the Knowledge Graph. Organizations with mature KM programs (or those that have committed to developing one) are making multimillion-dollar, multi-year investments in designing and developing enterprise Knowledge Graphs. These organizations recognize the potential value of Knowledge Graphs, and they equally recognize the competitive advantage they can offer. In 2022, we’ll see many of these solutions moving into production on an enterprise level. Those that are executed correctly will yield a host of capabilities for their organizations, linking critical business content of all kinds (structured and unstructured, data, people, products, etc.) with context and the business domain in order to power intelligent chatbots, content assembly, customized content delivery, adaptive learning, and semantic search and recommendations engines, to name just a few.

We are also seeing Knowledge Graphs serve as the foundational layer within the emerging trends in the data management space, powering data fabric and data mesh architectures that seek to align and achieve economies of scale across multiple data initiatives and needs that are brewing within pockets of the enterprise. 

Enterprise Knowledge Management Is for Everything – Back in my 2020 trends blog, I’d discussed the concept of the knowledge ecosystem and the shift from KM being largely associated with tacit knowledge, broadening to include unstructured content, and then further expanding to include both structured and unstructured content. We now understand that good KM practices and technologies are for literally everything—all your stuff, across the entire enterprise. Yes, this includes your traditional definitions of knowledge, information, and data, but it also includes your people themselves, your products and applications, and external information that can enhance your own. As many organizations have shifted to remote or hybrid work, including people and learning content are of particular importance, leveraging KM practices and systems in order to approximate (or better yet, improve) the social knowledge sharing that previously occurred naturally in offices. When harnessed properly, and by integrating concepts of ontologies and knowledge graphs, this means that organizations can create connected experiences for all of their materials, in context, and customized for their people.

Portals Are Back (Kind Of) – I spent the first decade of my career in the ‘90’s and 2000’s working on enterprise portal initiatives. These were, at the time, cutting-edge concepts and compelling business offerings that would securely unite access to a wide range of content via a single browse and search interface, while integrating a host of “widget” functionalities. The promise of these portals, however, ended up falling flat for most organizations, which found them to be difficult to maintain. Adoption lagged as many users preferred to interact with the content in its native locations. The technology wasn’t there, but neither were the core KM techniques regarding taxonomy, content types, search hit types, and governance that would’ve driven the successful adoption and long-term enhancement of these systems.

We’re now seeing the resurgence of portals, this time less as a standalone product and more as a combination of leading technologies, including advanced enterprise search, headless CMS, and semantic engines like graph databases. When combined with the mature KM design and governance techniques we offer today, a new, highly mature breed of portals are being launched, offering significant gains for organizations in knowledge sharing, knowledge use/reuse, and overall findability and discoverability of content. 

The big difference between portals of the previous decades and what we’re seeing now is that these are successfully integrating that wide range of content I identified in the second trend above, creating a true 360-degree view for organizations and enabling users to traverse one type of content to find, discover, and act on others. In short, portals are no longer a gimmick; they have the potential to be business-centric, highly actionable KM tools to unite all of an organization’s knowledge, information, and data assets.

Digital Transformations and KM Transformations Are Colliding – For the last decade, the term “digital transformation” has been on the rise, leveraged heavily by management consultancies, technology firms, and stakeholder organizations. This term is used to express the dramatic organizational shifts in solutions, processes, and content that are being sought by organizations globally in order to keep up with technological advancements, maintain and grow competitive advantages, and leverage the knowledge, information, and data they have developed. When considerations for how knowledge, content, and data (collectively, content) should be connected across the enterprise are included as part of these digital transformations, these are, in fact, KM initiatives. Many organizations that have reached the concluding stages of their digital transformations have been left without the results they anticipated, as content is still not findable, silos are still stifling innovation and collaboration, and different types of content are still stuck in their native systems rather than being integrated into actionable collections. In short, these digital transformations missed the KM, and stakeholders that have invested millions are still looking for the ROI. 

Conversely, we’re seeing organizations plan for and invest in large-scale, multi-year, enterprise KM transformations, where they’re putting significant resources into the modernization of their people, processes, content, culture, and technologies. Is this sounding familiar? In short, digital transformations and KM transformations are increasingly the same thing, and in a world where there is a lot more budget and global focus on digital transformations, this is a good trend for the field of KM. It is also an important reminder for KM practitioners and organizational leaders to take an enterprise view of their organizations and learn from past successes and failures of digital transformations. Furthermore, putting a KM lens on digital transformations will help to put most organizations’ two greatest assets, their people and their content, at the center of a transformation initiative.

Learning Organizations Are Taking Ownership of KM – I am often asked where Knowledge Management should sit with an organization. There is no single right answer to this, and I’ve seen a lot of different models work, though increasingly I’ve noted the top three most successful KM reporting structures are lines to the C-suite (specifically reporting up to the COO), owned by a for-profit business line, or operating alongside or within the learning organization. KM as a partner to learning is an increasing trend, one that makes a lot of sense for many organizations but only if they’re taking a modern approach to learning. For organizations that equate their learning organization with required classes and traditional learning, a KM organization placed within will be stifled. However, if your organization’s learning strategy utilizes a complete learning ecosystem of training, performance support, and social learning with peers and subject matter experts, it is a strong candidate in which to place your KM organization. Put simply, good KM should fuel learning and development in organizations that are ready to think about both learning and Knowledge Management in modern terms.

KM Helping to Replace the Office – Now in year two of the pandemic, many organizations have decided to abandon their traditional views of the office and move to full work from home or hybrid environments in perpetuity. Many others are still in a holding pattern of deciding what the future of work will look like. Still others are dedicated to returning to the office but recognize that today’s competition for talent means there will always be some percentage of remote employees. Regardless of where your organization is on this spectrum, KM is increasingly seen as an area for investment to replicate the social KM that used to occur in the hallways, kitchens, and cubes of former workplaces. As I detailed toward the beginning of the pandemic, the quick shift to remote work has, for many organizations, shone a light on their poor (or poorly followed) KM systems and practices. What could be covered up by social KM and heroics from individuals has risen to the surface in the ensuing work from home period and is forcing organizations to invest in KM in order to improve how they capture, manage, enhance, and share knowledge across their organizations. 

More mature KM organizations have, as a whole, fared better during the pandemic, and still others are now making significant investments in order to catch up and alleviate some of the problems the sudden shift to work from home has caused. What this means for the field is that more executives and decision makers now understand the value of KM and are willing to invest their organization’s resources in order to accommodate the new realities of work. Regardless of an organization’s long-term plans to return to the office, this period has created more awareness of KM. As a result, it is more prominently surfacing as a good investment, offering a long list of real business outcomes including improved employee satisfaction and retention, improved customer retention and acquisition, greater productivity, faster and more consistent upskilling of new employees, and reduced risk from outdated, incorrect, or obsolete information.

KM and Information Security – Perhaps more of a prediction than a full-fledged trend, we’re beginning to see momentum behind the use of KM technologies and practices to help mitigate information security risks. As semantic tools have increased in their capabilities, we’re starting to observe use cases for organizations using these tools to automatically spot sensitive content that can be secured, archived, or dispositioned. Some of this is content that is simply old, obsolete, or incorrect, presenting the risk that an end user might find it and act on it. Depending on the industry, this alone is a multimillion-dollar risk. In other use cases, however, this becomes more about cyber security and defense against a successful hack. If your systems are penetrated, the less content left lying around, the better. My colleague addressed this idea of phantom data in a previous article, but put simply, many organizations should be losing sleep over the content that has been left about, and KM can help to address that while solving a host of more traditional KM challenges as well.

Are you ready to seize the business value these KM trends can offer? Get in touch with EK, and we can help. 

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I limited the list to the top five items and encouraged all conference participants to listen for these themes. Though I didn’t intend to do so, in discussing this list of trends I realized that I was also covering EK’s defined elements of KM: People, Process, Content, Culture, and Technology. This list included:

1. Increasing maturity and capabilities with knowledge graphs, taxonomy/ontology management and auto-categorization, natural language, and advanced search: Though we refer to Technology as an enabling factor of KM, the fact is that we are at a very exciting place with some of the latest technologies. Concepts and ideas that have been discussed for years, like reliable auto-tagging, natural language processing, and ontological mapping of different types and sources of data are now a reality. What this means is that the findability, discoverability, and relatability of knowledge and information is improving.
2. Leveraging KM and maturing technologies to move toward Artificial Intelligence: As I mentioned above, the various technology suites that often come up in the context of KM have improved drastically to the point where they’re realities for organizations. The next step in this evolution is Knowledge Artificial Intelligence. Organizations that have invested in their KM foundations (taxonomy, content types, content architecture, content governance, tagging, etc) are finding themselves vastly more prepared to address their AI goals. Combining a solid KM foundation with ontology management, semantic web, and knowledge graph technologies translates into an extremely “smart” system powered by your organization’s best knowledge. This isn’t the future. If you are taking the right approach, this is your organization’s now.
3. Increasing use of human-centered approaches with KM including user-centered design, design thinking, and Organization Development/Knowledge Management mergers: The trend here, in short, is long overdue. KM will always fail if the end users and stakeholders aren’t in the center of the strategy, design, implementation, and operations. I’m proud that EK has been a major driver in this space, and we will continue to be so. It is great to see the field as a whole becoming increasingly aware that KM needs to consider humans and human behaviours in order to succeed.
4. Demand for hard ROI and clear measurement of analytics to justify KM and guide its evolution: I’ve been doing this long enough to have experienced several rather uncomfortable economic downturns. In each one, I saw my client’s KM efforts cut due to economic constraints. KM has for too long been considered a “nice to have” as opposed to the necessity it is. Too many practitioners continue to stress theory instead of practice and business value. Every KM effort should include a clear justification of business value and outcomes and I’m pleased to see that as a core element of the discussion.
5. Blurring content lines (between internal and external, between structured and unstructured, etc): This point speaks to technology changes, but significantly moreover, to the different manners in which organizations are capturing, managing, finding, and using their information. The days of a list of links to files serving as a good search result have long passed. We’re at a point where our users want the right information (regardless of its source, structure, or form) in an easily consumable manner, and also within the context of the question asked and the need expressed. What this means is that the actual state and location of content is increasingly less important. What matters is the ability to integrate, contextualize, and surface it amongst other knowledge in order for end users to take action.

I view each of the trends here as encouraging. Each of these, if they continue, will result in KM fueling the business outcomes we want for our organizations. If leveraged properly, KM and the specific trends we see here will lead to organizations better using, reusing, and acting upon their knowledge, resulting in greater productivity, learning, and satisfaction.  

Are you harnessing these trends effectively? Enterprise Knowledge is here to help.

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1. The Need for Business Application and Enterprise Use Cases

AI is no longer an experience, it is a business requirement that increasingly requires clear justification and tangible business values with return on investment (ROI) in order to receive funding and organizational support. Clear understanding of how advanced AI capabilities can impact business or help address relevant use cases is becoming the cornerstone for the success of Enterprise AI implementation and organizational alignment.

2. A Narrowing Gap Between Structured and Unstructured Data/Information

In the past, the academic debate on how we manage data vs. information vs. knowledge has been a central driver in determining  how we practice these disciplines. Now, semantic solutions allow organizations to structure previously unstructured information by capturing meaningful relationships between data entities in a scalable and efficient manner. This makes it possible to train machines to interpret data in a human-centered way, powering knowledge and data discovery. 

3. Advanced Data Management and Governance Approaches

There are more resources being dedicated toward Enterprise AI efforts to derive value from existing content and data, providing a better alternative to purchasing nebulous “big data” storages to glean a comprehensive picture of the data within the enterprise. By employing enterprise knowledge graphs, automated categorization, or a recommendation system, organizations can now achieve optimized access, natural language search, and use and re-use their information assets without a costly and burdensome migration. 

4. Open Source Solutions and Computing Capabilities

Over the years, web standards, open source training models, and linked open data for a number of industries, such as open government data (OGD) or open science data, have emerged to help organizations craft customized Enterprise AI solutions for their business. This means an organization that is looking to start leveraging AI for their business no longer has to start from scratch. Further, Cloud computing capabilities have also progressed in a way that makes implementing these solutions a pragmatic and cost effective reality.

5. Expanded Roles and Skill Sets

Recently, there has been an increasing demand for individuals who have the technical skills to engineer advanced machine learning and intelligent solutions, as well as business knowledge experts who can transform data to a paradigm that aligns with how users and customers communicate knowledge. This is becoming achievable with growing skill sets in data science, knowledge engineering, and semantic architecture that facilitate unprecedented collaboration between knowledge and data across an organization.

6. The Ability to Start Small and  Scale Incrementally

The most successful Enterprise AI initiatives are not attempting to incorporate every data set, every business unit, and every enterprise system. Organizations are more often building a rapid prototype or pilot, prioritizing a backlog of enhancements, and scaling iteratively. 

These trends are key indicators that AI for the enterprise is no longer a hype, rather, it is becoming a necessity with feasible environmental and infrastructure support – now more than ever. If you are interested in how to benefit from your knowledge and data to accelerate enterprise AI capabilities, email us.  

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