Lucidea’s Lens: Knowledge Management Thought Leaders
Part 115 – Zach Wahl

Zach Wahl is CEO and co-founder of Enterprise Knowledge (EK), the world’s largest dedicated knowledge, data, and information management consultancy. He has expertise in knowledge management strategy, governance, taxonomy, and content management. 

His specialties include knowledge bases, intranets, content management, document management, taxonomy management, websites (eCommerce and informational), and other information management systems. Zach has run over 250 taxonomy design workshops based on his own business taxonomy design methodology. He conducts training on information governance, web strategy, and taxonomy design. 

Here are definitions for five of Zach’s specialties: 

• Artificial intelligence (AI): The capacity of a computer to perform operations analogous to learning and decision making in humans, as by an expert system 

• Intranets: Private computer networks that use Internet protocols, network connectivity, and the public telecommunication system to securely share part of an organization’s information or operations with its employees. 

• Content management: Creating, managing, distributing, publishing, and retrieving structured information – the complete lifecycle of content as it moves through an organization. 

• Knowledge bases: Repositories typically used to store answers to questions or solutions to problems enabling rapid search, retrieval, and reuse, either by help desk personnel or directly by those needing support. 

• Taxonomy: A particular classification arranged in a hierarchical structure that can be used to organize information so that it can be readily found through navigation, search, and links between related content. 

Zach created the following content. I have curated it to represent his contributions to the field. 

Book by Zach Wahl: Making Knowledge Management Clickable 

Top Knowledge Management Trends – 2025 

1) AI-KM Symbiosis – 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. 

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. 

4) AI-Assisted Tacit Knowledge Capture – Since the late 1990s, 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. 

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 Intelligence: The Role of AI in KM, and the Role of KM in AI 

 

Making Knowledge Management Clickable