The Evolution of KM Roles (Presented at Knowledge Summit Dublin 2025)
0 likes161 views
On June 23, 2025, at the Knowledge Summit Dublin, Lulit Tesfaye and Jess DeMay presented “The Evolution of Knowledge Management (KM) & Organizational Roles: Integrating KM, Data Management, and Enterprise AI through a Semantic Layer.” The session examined how KM roles and responsibilities are evolving as organizations respond to the increasing convergence of data, knowledge, and AI. Drawing from multiple client engagements across sectors, Lulit and Jess shared patterns and lessons learned from initiatives where KM, Data Management, and AI teams are working together to create a more connected and intelligent enterprise. They highlighted the growing need for integrated strategies that bring together semantic modeling, content management, and metadata governance to enable intelligent automation and more effective knowledge discovery. The presentation emphasized how KM professionals can lead the way in designing sustainable semantic architectures, building cross-functional partnerships, and aligning programs with organizational priorities and AI investments. Presenters also explored how roles are shifting from traditional content stewards to strategic enablers of enterprise intelligence.
The Evolution of KM Roles (Presented at Knowledge Summit Dublin 2025)
- 1. The Evolution of Knowledge Management (KM) & Organizational Roles Integrating KM, Data Management, and Enterprise AI through a Semantic Layer Lulit Tesfaye Partner and VP, Knowledge & Data Services Enterprise Knowledge, LLC. [email protected] Jess DeMay KM Consultant Enterprise Knowledge, LLC. [email protected]
- 2. ENTERPRISE KNOWLEDGE What’s the biggest challenge in aligning KM, data, and AI efforts in your organization? ahaslides.com Code: 9KBIE Add your top 3 & click Submit
- 3. ENTERPRISE KNOWLEDGE What Matters Most Today?
- 4. The Changing Organizational Reality Artificial Intelligence (AI) and machine learning (ML) technologies do not eliminate the need for establishing a sustainable KM program. Rather, they make foundational KM practices even more essential. ~ Gartner 70% of organizations are implementing AI-driven tools to enhance KM. ~ McKinsey 65% of knowledge management roles are increasingly requiring skills in data analytics and AI technology, as organizations seek professionals who can leverage these tools effectively. ~ Deloitte 80% of enterprises will use GenAI in production by 2026, up from less than 5% in 2023. ~ Gartner
- 5. Advancements in Enterprise Solutions Impacting KM… A Semantic Layer sits between your data and your business users to allow you to better integrate, connect, and provide context to information. Semantic Layer The volume and dynamism of organizational data and content (structured and unstructured) is growing exponentially. AI helps to parse through it. Knowledge Intelligence
- 6. ENTERPRISE KNOWLEDGE What Is a Semantic Layer? “A semantic layer is a standardized framework that organizes and abstracts organizational knowledge and data (structured, unstructured, semi-structured) and serves as a connector for all organizational knowledge assets.”
- 7. ENTERPRISE KNOWLEDGE How a Semantic Layer Weaves Together Knowledge Management, Data Management, & Enterprise AI CONNECTING & AGGREGATING DATA A framework to bridge disparate content and data sources, allowing for connection and a unified view of organizational knowledge. PROVIDING ORGANIZATIONAL CONTEXT & MEANING Adds a layer of context and meaning to data, making it more understandable and actionable for both humans and AI systems. ENABLING KNOWLEDGE DISCOVERY & INSIGHTS By connecting and contextualizing data, the semantic layer facilitates the discovery of hidden relationships and insights - enabling holistic decision-making. SUPPORTING AI-DRIVEN APPLICATIONS Provides a structured and meaningful foundation for AI applications, enabling them to access and process organizational knowledge in a machine readable way. Unstructured Data Documentation, Presentations, Multimedia Existing Metadata Metadata Repositories, CMS/Catalog Data, Taxonomy Structured Data Datasets, Data Warehouse, ETL Lineage
- 8. ENTERPRISE KNOWLEDGE ● Categorize ● Organize ● Map ● Contextualize “Which products fall under this ‘Product’ category across sales, marketing, and customer support?” Taxonomy & Ontology ● Connect ● Analyze ● Reason ● Infer “What is the most purchased product across customers in a given region?” Knowledge Graph ● Architect ● Integrate ● Orchestrate (ETL/ELT) ● Virtualize “How do we implement across the enterprise?” Connections & Integrations ● Search ● Data Dashboards ● Data Visualization ● Chatbots ● Recommendation Systems “How many end-user applications can we support?” Applications ● Describe ● Standardize ● Catalog “What data do we have on our products? “ Metadata Deconstructing the Components of a Semantic Layer
- 9. ENTERPRISE KNOWLEDGE Activity: The AI Race and Semantic Layer Simulation 1. Divide in two groups 2. Use the provided templates to answer the following business question: “Which professor taught Physics at both Harvard and MIT?” 3. Reconvene with the larger group to present your outcomes and key takeaways Objective: Compare how quickly and accurately participants can mimic AI and answer a business question using either: 1. Common enterprise content/data Vs. 2. Using the semantic data model (connected business concepts with relationships) Outcome: Visualize how a semantic layer bridges KM, data management, and AI
- 10. ENTERPRISE KNOWLEDGE Knowledge Intelligence (KI) is a framework that integrates institutional knowledge, business context, and expertise to enhance AI capabilities through effective knowledge and data management practices. What Is Knowledge Intelligence?
- 11. Expert Knowledge Capture & Transfer Programmatically encode 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 Business Context Embedding For data and all knowledge assets in a machine readable format ENTERPRISE KNOWLEDGE Knowledge Intelligence Approach
- 12. ENTERPRISE KNOWLEDGE KM Within the Enterprise Today… Strategic Focus Enterprise AI Enablement User-Centric Experiences Remote Collaboration Risk Management KM leaders now see their roles as strategic advisors within their organizations, a movement from operational tasks to contributing to organizational strategy and innovation. Traditional KM and data management practices are becoming the focus of increased AI spend. KM is providing a programmatic way to embed organizational knowledge and context for AI solutions. KM initiatives are moving away from traditional, top-down mandates to adopting user-centric strategies—prioritizing knowledge capture and sharing, personalized learning paths, employee/customer engagement, and social collaboration. Many organizations have shifted their operations as well as KM practices to support remote work. As a result, KM roles are adapting to new work environments and focusing on digital collaboration tools. KM has evolved from a repository of information to a strategic asset used to inform data-driven decisions and compliance practices. KM professionals now collaborate with data analysts by capturing and sharing lessons learned from past incidents and to extract insights from organizational data.
- 13. ENTERPRISE KNOWLEDGE PC Era Early Computers Early 2000s Rise of the Web & Spreadsheets 2010 Big Data, Data lakes & Business Intelligence 2015 Cloud, Big Data, & Self-Service Analytics Today & Future Age of AI Semantic Layer Ontology + Linked Data Taxonomy + Metadata Knowledge Graph + AI The Evolution of Organizational Roles The Data Custodians The Data Wranglers The Information Architects & Specialized Officers The Data Democratizers The Enablers Aggregate and centralize data. Build a KM platform to enable others to find, search for, display, model, and connect information. Make data available to groups who need it and enable data customization. Connect groups through AI and anticipate business needs before they arise. Manage and clean data/content.
- 14. KM and IM Managers as Enablers for Data, Analytics & AI Supporting Case Study: A Global Retail Chain Engagement Objective: Establish a semantic ecosystem to enhance advanced data and analytics through KM and IM capabilities. Key Goals: ● Standardize metadata and vocabularies across enterprise data assets ● Assess current KM maturity and recommend an integration architecture for analytics and visualization ● Identify skill gaps and upskill KM staff with advanced capabilities Value Delivered: ● Complete Answers – Unified access reduces user frustration ● Shared Meaning – Standardized definitions improve consistency and efficiency ● Self-Service Opportunities – Personalized access reduces support burden ● User Engagement & Retention – Empowered users are more satisfied and loyal Sets the data product vision and strategy and oversees the product development. Builds knowledge models for data products. Defines reporting requirements and liaises with stakeholders. Data Product Manager Knowledge Engineer Data Visualization Lead Core Personas
- 15. Embedded KM Specialists Bridging KM & Data Supporting Case Study: A Construction Company Engagement Objective: Dual engagements in KM Strategy and Data Management accelerated delivery and elevated maturity across diverse business functions. Key Goals: ● Improve organizational connection, findability, and content usability ● Develop a repeatable approach for standardized, consistent content practices KM Strategy Team Data Catalog / Governance Team KM Sponsor Embedding KM Specialists Value Delivered: ● Strengthened KM-Data team partnerships ● Promoted governance best practices organization-wide ● Enabled focus on advanced data use cases ● Improved data interoperability and real-time access ● Boosted discovery, collaboration, and data quality across departments ● Assigned a point person to manage concurrent initiatives across teams ● Aligned roadmaps for KM strategy, data catalog scaling, and governance programs ● Maintained continuity and context between KM and structured data efforts
- 16. Aligning Siloed Departments Through Common Governance Practices Supporting Case Study: A Large Financial Institution Engagement Objective: Resolve misalignment of knowledge and data elements by creating a semantic layer to unify content and enforce consistent naming conventions. Key Goals: ● Develop a standard enterprise Semantic Layer ● Implement federated governance and contribution models ● Enable a shared catalog for data accreditation, discovery, and use Value Delivered: ● Strategic Focus: KM leaders as organizational advisors and innovation drivers ● Enterprise AI Enablement: Embedding KM into AI solutions for contextual intelligence ● User-Centric Experiences: Personalized knowledge sharing and collaboration ● Remote Collaboration: Adapting KM practices for digital, remote environments ● Risk Management: KM as a strategic asset driving compliance and data-informed decisions Aligning Siloed Departments Through Common Governance Practices Federated governance with 10+ departments contributing to ontology development Semantic Layer standardizing content across 349+ petabytes of data Enhanced cross-department data awareness and consistent understanding KM team’s role growth toward strategic leadership
- 17. ENTERPRISE KNOWLEDGE Interactive Session: Envisioning the Org of the Future 1. Divide into groups and answer the following questions: a. Which framework is compelling to you or pragmatic for your organization? ■ KM and IM roles as Enablers through governance & templates ■ Embedded KM Specialists Bridging KM & Data, and AI b. Who should own the semantic layer and knowledge models? c. Where does data and AI governance sit? d. How should KM and data professionals collaborate with data scientists and product teams? e. Where do new roles (e.g., knowledge engineers, AI/data product managers) belong? f. How can knowledge management avoid becoming siloed or reactive in this structure? 2. Reconvene with the larger group to present your outcomes and key takeaways (15 mins) Objective: Prototype the ideal roles and operating model for the future enterprise. Outcome: A forward-looking KM, Data, and AI operating model with clearly defined roles, governance structures, and collaboration pathways to support the enterprise of the future. 30 Minutes
- 18. ENTERPRISE KNOWLEDGE Interactive Session: “From Reactive to Strategic” KM Maturity Ladder 1. Divide into groups and use the maturity framework provided to discuss the following questions: a. Evaluate your current KM practices; where would you place your organization or practice? b. Which maturity level is more pragmatic for your organization? c. What would you change or add to this framework to tackle how knowledge management can avoid becoming siloed or reactive going forward? 2. Reconvene with the larger group to present your outcomes and key takeaways (15 mins) Objective: Identify gaps, roles, and capabilities needed to embed KM in AI within organizations. Outcome: A pragmatic KM evolution roadmap & operating model for emerging organizational needs. 30 Minutes
- 19. ENTERPRISE KNOWLEDGE KM Maturity Ladder: “From Reactive to Strategic” Ad-hoc Strategic Embedded What is it: ● KM is fragmented, unstructured, informal, and often invisible. No shared tools or strategy. Roles & Operating Model: ● No formal KM roles. Efforts are fragmented and led by individuals or specific teams. Tools are inconsistent or shadow IT. Duplication of work, inconsistent decisions, no institutional memory. AI and analytics efforts are hampered by inaccessible knowledge or poor-quality content/data. Outcomes Mandated Enablement | Service-Oriented Minimal collaboration. Knowledge is “stored” but not discoverable/usable. AI systems lack reliable training data. Resistance to KM adoption due to perceived overhead. Improved search, findability, and onboarding. AI models begin to leverage more structured context (e.g., metadata, tagged content). Reuse increases, accelerating AI experimentation. Semantic clarity enables AI/ML feature engineering, synthetic data generation, and reliable automation. Analytics becomes more interpretable. Graphs and ontologies start to unify disparate systems. Knowledge intelligence is fully AI-enabled. LLMs/analytics systems access trusted knowledge. Rapid experimentation & productization. KM is a competitive differentiator. What is it: ● KM becomes a compliance- driven requirement. Initiatives are driven by regulations, audits, or risk aversion. Seen as a burden rather than an enabler. Roles & Operating Model: ● Records managers, compliance officers, policy admins may enforce knowledge capture. Little semantic structure or cross-linking. What is it: ● KM practices are integrated into team workflows. Shared glossaries, taxonomies, and tagging become common. Governance begins to scale. Roles & Operating Model: ● Emerging KM roles (e.g., taxonomist, content strategist, data stewards) are embedded in teams. Shared tagging, workflow-based documentation. What is it: ● KM operates as a center of excellence, providing tooling, guidance, and shared vocabularies. Beginning to build foundational semantic infrastructure. Roles & Operating Model: ● Federated KM or DataOps teams manage vocabularies, metadata, and tooling. Knowledge engineers, data product managers, and semantic architects support AI teams. What is it: ● KM is a pillar of business & AI strategy. Knowledge is treated as infrastructure, structured, standardized, and surfaced in real-time for human and machine use. Roles & Operating Model: ● KM roles embedded into product, engineering, and data orgs. Semantic layer standards are operationalized. KM functions co-lead with data/AI governance.
- 20. Register now! 30% off with the code: KM_Dublin
- 21. [email protected] Lulit Tesfaye WWW.LINKEDIN.COM/IN/LULIT-TESFAYE/ Thank you! [email protected] Jess DeMay WWW.LINKEDIN.COM/IN/JESS-DEMAY/
- 22. EK At A Glance ESTABLISHED 2013 – PARTNERING WITH OUR CLIENTS TO TRANSFORM KNOWLEDGE INTO ACTION. SCALING TO OUR CLIENT’S NEEDS How We Can Engage Supporting at Any Stage DELIVERING VALUE ACROSS THE GLOBE HEADQUARTERED IN WASHINGTON, DC, USA PRESENCE IN BRUSSELS, BELGIUM DRIVING INDUSTRY THOUGHT LEADERSHIP Largest Open Knowledge Base Top-Ranked KM Podcast - Knowledge Cast KMWorld Reality Award Winner 2024 100 Companies That Matter in KM 2015-2024 AI 100 Trailblazer 2020-2024 Top-Selling Book on KM - Making Knowledge Management Clickable CONNECTING ALL ORGANIZATIONAL KNOWLEDGE ASSETS PRODUCTS KNOWLEDGE CONTENT INFORMATION DATA PEOPLE COLLABORATING TO ACHIEVE OUR CLIENT’S GOALS CONSISTENTLY AWARDED INC. BEST PLACES TO WORK BASED ON EMPLOYEE SATISFACTION SURVEYS