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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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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