This blog explores how the integration of secure access management within an enterprise AI framework can transform enterprise AI systems from simple automation tools into secure, context-aware knowledge platforms. We’ll discuss approaches for how modern Role-Based Access Control (RBAC), enhanced by AI capabilities, works to streamline and create a dynamic ecosystem where information flows securely to those who need it most.

Understanding Enterprise AI and Access Control

Enterprise AI represents a significant advancement in how organizations process and utilize their data, moving beyond basic automation to intelligent, context-aware systems. This awareness becomes particularly powerful when combined with sophisticated access management systems. Role-Based Access Control (RBAC) serves as a cornerstone of this integration, providing a framework for regulating access to organizational knowledge based on user roles rather than individual identities. Modern RBAC systems, enhanced by AI, go beyond static permission assignments to create dynamic, context-aware access controls that adapt to organizational needs in real time.

Key Features of AI-Enhanced RBAC

1. Dynamic Role Assignment: AI systems continuously analyze user behavior, responsibilities, and organizational context to suggest and adjust role assignments, ensuring access privileges remain current and appropriate.
2. Intelligent Permission Management: Machine learning algorithms help identify patterns in data usage and access requirements, automatically adjusting permission sets to optimize security while maintaining operational efficiency, thereby upholding the principles of least privilege in the organization.
3. Contextual Access Control: The system considers multiple factors including time, location, device type, and user behavior patterns to make real-time access decisions.
4. Automated Compliance Monitoring: AI-powered monitoring systems track access patterns and flag potential security risks or compliance issues, enabling proactive risk management.

This integration of enterprise AI and RBAC creates a sophisticated framework where access controls become more than just security measures – they become enablers of knowledge flow within the organization.

Secure Access Management for Enterprise AI

Integrating access management with enterprise AI creates a foundation for secure, intelligent knowledge sharing by effectively capturing and utilizing organizational expertise.

Modern enterprises require a thoughtful approach to incorporating domain expertise into AI processes while maintaining strict security protocols. This integration is particularly crucial where domain experts transform their tacit knowledge into explicit, actionable frameworks that can enhance AI system capabilities. The AI-RBAC framework embodies this principle through two key components that work in harmony:

1. Adaptable Rule Foundation (ARF) for systematic content classification
2. Expert-driven Organizational Role Mapping for secure knowledge sharing

While ARF provides the structure for explicit knowledge through content tagging, the role mapping performed by Subject Matter Experts (SMEs) injects critical domain intelligence into the organizational knowledge framework, creating a robust foundation for secure knowledge sharing. The ARF system exemplifies this integration by classifying and managing data across three distinct levels, while SMEs provide the crucial expertise needed to map these classifications to organizational roles. This combination ensures that organizational knowledge is not only properly categorized but also securely accessible to the right people at the right time, effectively bridging the gap between AI-driven classification and human expertise.

The Adaptable Rule Foundation (ARF) system exemplifies this integration by classifying and managing data across three distinct levels:

• Core Level: Includes fundamental organizational knowledge and critical business rules, defined with input from domain SMEs.
• Common Level: Contains shared knowledge assets and cross-departmental information, with SME guidance on scope.
• Unique Level: Manages specialized knowledge specific to individual departments or projects, as defined by SMEs.

SMEs play a crucial role in adjusting the scope and definitions of the Core, Common, and Unique levels to inject their domain expertise into the ARF framework. This ensures the classification system aligns with real-world organizational knowledge and needs.

This three-tiered approach, powered by AI, enables organizations to:

• Automatically classify incoming data based on sensitivity and relevance
• Dynamically apply appropriate access controls using expert-driven organizational role mapping
• Enable domain experts to contribute knowledge securely without requiring technical expertise
• Adapt security measures in real-time based on organizational changes

The ARF system’s intelligence goes beyond traditional access management by understanding not just who should access information, but how that information fits into the broader organizational knowledge ecosystem. This contextual awareness ensures that security measures enhance, rather than hinder, knowledge sharing.

The Future of Enterprise AI

As organizations continue to leverage AI capabilities, the interaction between access management and enterprise AI becomes increasingly crucial. This integration ensures that AI systems serve as secure, intelligent platforms for knowledge sharing and decision-making. The combination of dynamic access controls and enterprise AI framework creates an environment where:

• Security becomes an enabler rather than a barrier to innovation
• Domain expertise naturally flows into AI systems through secure channels
• Organizations can adapt quickly to changing knowledge needs while maintaining security
• AI systems become more contextually aware and organizationally aligned

If your organization is looking to enhance AI capabilities while ensuring robust data security, our enterprise AI access management framework offers a powerful solution. Contact us to learn how to transform your organization’s knowledge infrastructure into a secure, intelligent ecosystem that drives innovation and growth.

The post Enterprise AI Meets Access and Entitlement Challenges: A Framework for Securing Content and Data for AI appeared first on Enterprise Knowledge.

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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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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