Whether you’re new to KM or looking for a fresh, practical perspective, this hands-on workshop bridges the gap between KM theory and KM in action. Drawing on foundational KM principles, proven methodologies from 12 Steps to KM Success, and real enterprise examples from Novartis, this workshop shows exactly how core KM concepts translate into daily wins inside a complex global organization. The workshop blends foundational teaching, live mentoring, and practical demonstrations, giving participants a rare chance to learn KM fundamentals and immediately apply them with support from two experienced practitioners. Garfield brings decades of KM leadership and theory, while Carlton shares “boots-on-the-ground” experience deploying KM approaches—spanning communities of practice, enterprise alignment, and modern engagement techniques—across one of the world’s largest life sciences companies. Get lots of tips and insights on how to apply KM theories in real organizational settings using examples from Novartis; how real-time mentoring accelerates learning, confidence, and practice; practical tools, techniques, and quick wins that work in any organization; and how to build or strengthen a KM program using proven steps, patterns, and lessons learned. Designed for beginners and experienced practitioners alike, this session delivers an energizing blend of insight, interaction, and immediate applicability, giving attendees a clear vision for what KM can achieve in their own organizations.

This workshop is all about transforming KM with Microsoft 365 and Copilot and creating strategies for the AI-enhanced workplace. In the age of AI, effective KM requires more than just good tools: It demands smart strategies. Hanley discusses how to harness the full potential of Microsoft 365 to drive KM outcomes, from enhancing explicit knowledge with metadata, Copilot, and native SharePoint capabilities to surfacing tacit knowledge and expertise through Viva Engage and AI. Learn how to design AI-assisted knowledge experiences grounded in your organizational content and culture – and strategies to ensure that your tools deliver organizational value. Get actionable insights and practical approaches from our experienced practitioner for using SharePoint, Engage, Copilot, and more to build a resilient, knowledge-rich workplace.

This hands-on workshop equips practitioners with the tools to guide their organizations through complex change—whether launching a KM platform, rolling out AI capabilities, or shifting culture. Turner draws on 2 decades of experience in enterprise change management, transformation communications, and inclusive leadership to deliver a highly interactive, scenario-based learning experience. Practice real-world techniques for stakeholder alignment, resistance management, and communication design and get a personalized playbook and deeper understanding of how to lead change with clarity, compassion, and measurable impact. Turner discusses a tech-agnostic change management framework aligned with KM principles; how to map stakeholders, anticipate resistance, and build momentum; communication strategies that drive understanding and action; how to design a change plan for enterprise initiatives; and tools, templates, and scripts to take back and use immediately.

Organizational knowledge has always required human curation. The insertion of AI and machine knowledge into organizational workflows and decision processes increases that need. Every RAG pipeline, fine-tuned model, and agent is only as trustworthy as the knowledge feeding it. Many organizations are discovering that their vast treasure trove of explicit and tacit knowledge was never curated well enough to begin with, let alone to support autonomous systems making real decisions. This is happening as waves of retiring experts are taking their knowledge with them. At the same time, AI-generated content is flooding back into existing repositories with little, if any, formal, structured path for capturing and maintaining the departing deep knowledge, along with the insights from which it was derived. Based on our KM expert’s many decades of experience, learn what knowledge is worth capturing and in what form, including how to surface deep tacit knowledge from experts before it's lost. Learn the ABC’s of designing and building a modern, evolutionary knowledge curation stack (data and text analytics, knowledge graphs, vector stores, ontologies, and more) that helps keep knowledge current, trustworthy, and accessible to both human users and AI agents. Measurable results include fewer repeated mistakes, shortened onboarding, increased productivity, and a more rapid cycle of innovation and learning. Work through a structured assessment of your organization's curation maturity, identify your highest-risk knowledge gaps, and leave with an initial curation plan and a clear, compelling business case specifically designed for this new hybrid human/AI environment.

AI ethics and governance, as well as organizational standards and practices, are at the top of people's minds. Join our expert and recent AI 2030 Responsible AI Leader of the Year recipient, who leads this interesting and exciting conversation with her co-author and former IBM colleague, as they share strategies, challenges, and practical tips. Explore the ethics of AI and learn how different standard organizations as well as corporate enterprises are setting guidelines for the use of AI and possible impacts on future implementations.

Expert knowledge, often tacit and deeply embedded in experience, is difficult to capture and transfer effectively. Many experts may not consciously recognize the skills they use or understand how to communicate them to others. This creates challenges in knowledge sharing among teams and external partners, especially on high-stakes, time-sensitive projects. This becomes even more critical in emergency situations, such as disaster response, where knowledge is key to mission success. This master class-style workshop addresses these challenges through a combination of case studies, expert insights, and GenAI tools. Drawing from the experiences of more than 200 top-level executives, engineers, and scientists across Fortune 500 companies, the military, and government agencies, our speaker explore strategies to enhance knowledge transfer and look at leveraging GenAI to assist in analyzing and transferring tacit knowledge captured through interviews, especially AI technologies that can help bridge the gap between experts and novices, creating a dynamic environment for knowledge exchange that is particularly valuable in dispersed or remote teams. By working through real-world challenges from attendee organizations, our speaker discusses strategies to enhance knowledge flow internally and externally, ensuring that best practices and lessons learned are not lost. Participants gain hands-on experience with AI-driven tools to document, share, and manage knowledge transfer processes.

Humans connect through stories, and KM is all about connecting people. Neuroscience has found that we are wired for stories. Organizational activities are related to story patterns. This interactive workshop goes beyond the foundations of story psychology and focuses on applications for KM and AI. Glover and Viles share winning storyteller strategies, success stories, techniques, and more through several fun activities. Lewis shares approaches and exercises to support strategies around KM systems, cultures, leadership, knowledge sharing, project documentation, data storytelling, and AI prompts and agents. He draws direct comparisons between story structure as a fundamental sensemaking framework and popular change models, like Six Sigma DMAIC, Design Thinking, Change Management, and Project Management. Discover how story thinking produces quad-loop learning, transforms change management into change leadership, and develops knowledge workers into knowledge leaders. Get a heightened awareness of the power of stories and learn how to run storytelling workshops in your organization. Come, learn, and enjoy “story-listening,” as KM practitioners tell their own tales. Led by our KM pros, attendees will learn a lot in a fun environment.

Text analytics and GenAI are both platform technologies that support multiple applications. The number and variety of powerful applications that organizations are building using them are staggering and growing. However, without careful attention to a solid foundation, many of those apps will not only fail to deliver real value, they are also susceptible to unethical manipulation. By combining these technologies, you get the best of both: GenAI that is smarter and safer and text analytics that is richer and easier to develop. GenAI’s limitations include the tendency to hallucinate. LLMs were trained on public information, but as we’ve seen many times, the content and vocabularies behind the enterprise firewalls are quite different. Text analytics can refine the general answers of GenAI with precision. Text analytics limitations deal with machine learning (ML) and sematic rules. ML is completely dependent on good training sets and so produces limited precision; semantic rules require a significant effort and expertise to develop. Adding GenAI to that development process enables rules that retain their accuracy while significantly reducing the development effort. Learn how to set up text analytics and GenAI environments starting with selecting the right components (software, LLMs, etc.). Our experienced practitioner, speaker, and the conference chair of Text Analytics Forum, Reamy focuses on creating a text analytics foundation (autocategorization, data extraction, and more) and a GenAI approach that combines text analytics, knowledge graphs, prompt engineering, merging enterprise LLMs with the larger public LLMs, and smarter search and RAG capability. He provides tips and techniques that remove bias in the training sets, reduce hallucinations, and guard against accepting generic, superficial answers that are accepted because it’s easier to just trust the GenAI answers. Attendees will definitely learn a lot in this interactive workshop.

Every organization has tribal knowledge—expertise, process, and institutional memory that lives in people's heads, email threads, and chat histories rather than in any findable system. Most organizations know this is a problem. Few have a systematic way to surface, assess, or fix it. This working session gives KMWorld attendees a practical framework for auditing their own tribal knowledge exposure—and a structured path from diagnosis to action. Using real-world patterns from Bloomfire's deployments across healthcare, financial services, retail, and professional services, participants work through a tribal knowledge audit of their organization and gain a framework for prioritizing what to fix first—and how to prove the knowledge your teams rely on has actually been received and understood, not just posted.

AI is no longer optional for organizations looking to remain competitive. However, many businesses struggle with where to start, what tools to use, and how to implement AI in a way that produces real results. Lindsey breaks down AI into practical, actionable strategies that organizations can immediately apply. Learn how to integrate AI into daily operations, automate repetitive tasks, and improve productivity across teams without requiring advanced technical expertise. She focuses on real-world application, not theory; AI tools that support workflow automation, content creation, communication, and decision making; and how organizations can build AI-ready teams through training, structured implementation, and workforce development strategies. Get a clear understanding of how to move from AI curiosity to AI execution, streamline operations, reduce manual workload, and position enterprises for long-term success using artificial intelligence.

This high-demand workshop focuses on how to build a successful KM strategy and revitalize knowledge sharing within your organization. Our workshop leaders engage participants, taking them through a step-by-step approach to rethinking the role of the KM function within an organization. It includes creating a decision/information flow map to understand the natural flows of knowledge, defining micro-projects that directly link to the decision support needs of senior executives, mapping the current flow paths for knowledge within the organization, and finding natural ways to manage the knowledge of the aging workforce as well as the IT-enabled apprenticeship. Using real-world examples, the workshop highlights winning strategies and insights to rejuvenate your knowledge-sharing practices. Always fresh and filled with interesting stories, this workshop continues to stand out with our audience!

Discover how to design an intranet that leverages Microsoft 365’s built-in capabilities, SharePoint, Viva Engage, and Copilot, while adding thoughtful enhancements where they matter most. Our experienced leader shares practical strategies for creating a foundation that supports AI-powered experiences, drives internal communications, and delivers measurable engagement. Learn what works out of the box, where customization adds value, and how to keep governance and branding aligned for long-term success. Get practical ideas for an AI-ready foundation: information architecture, metadata, and governance guardrails that improve answers, reduce risk, and sustain your intranet; designing for the flow of work with the SharePoint app to bring your intranet into Teams (including mobile), plus personalization patterns; where out-of-the-box ends and light customization begins—decision criteria that balance brand, usability, and support; and communications options: understanding your options for internal communications and exploring practical examples from different organizations. This workshop is filled with lots of tips and techniques for engaging, effective, and rewarding intranets.

Across research, academia and policy, we see plenty of insight and evidence of knowledge. How do we get value out of this knowledge and build momentum toward the future from work being done in the present? Our speakers fuse their experience and expertise in stories, knowledge, and collaboration as well as foresight and futuring into a practical session for knowledge managers, policymakers, and researchers to examine how technical experts transform their ability to land their work with key stakeholders; how knowledge workers, researchers, academics, policymakers, and futurists animate their research as they go; building the environment in which the work lands to achieve greater impact immediately and over time; and how narrative and other methods from the foresight toolkit can be used to expand horizons and nurture narratives of futures that draw people from the now into the next. Get some fresh ways to build momentum and success for KM products and processes with insights from three pioneering experts that may transform your thinking.

With today’s AI-integrated workplace, it’s hard to distinguish whether what is produced is mostly by AI or by humans. This 3-hour workshop is meant to help current practitioners amplify their human edge. It explores how a coaching mindset instead of a consultant approach can be just as, if not more, effective in obtaining KM buy-in. Our workshop leader discusses the benefit of investing in personal KM (PKM) to better address stakeholder needs, instead of spending time and energy in managing inner-conflict, information overload, and impostor syndrome. She shares her story of starting KM from scratch as well as further reflections on what could have been done better. She discusses how the power of meditation and mindfulness techniques as well as a coaching mindset supported her in becoming a better authentic partner and listener to clients, encouraging engagement across the organization. This interactive workshop includes meditation techniques that have proven effective in handling stressful situations and that promote intuition and clarity and tips on how best to communicate with stakeholders to engage them and identify the specific support they seek.

The volume of enterprise knowledge continues to grow exponentially. Many previously exclusive human tasks are now performed by AI, while knowledge workers are stressed with new, unforeseen demands. AI not only generates and shares real and synthetic data and knowledge with other agents, it is beginning to write many of the policies defining how that data and knowledge are used. As a result, leaders and decision makers have far less visibility into how and where their organization’s knowledge is generated, applied, and adapted. As the demand for speed increases, errors and gaps propagate while the voices of the people closest to the work are increasingly drowned out (you will see real examples of this and the serious consequences that can result). Based on our KM expert’s many decades of experience, learn how to significantly reduce these risks by applying the seven facets of enterprise knowledge governance. Get an initial top-level governance model and implementation plan, along with the right performance metrics for convincing stakeholders that knowledge governance is a business-critical capability. Discover ways that good governance actually helps reveal human tacit knowledge, a key missing ingredient given today’s emphasis on GenAI. Best of all, attendees help to create the conditions under which both people and AI can genuinely work together ethically and responsibly rather than against or around each other.

Enterprise knowledge consumption is rapidly shifting from humans-only to agent-first, and KM is reshaping itself for AI. Understanding the fundamentals of agentic AI—and the adaptations it requires from organizational knowledge infrastructure and assets—has become essential for knowledge managers. Lacroix, a web pioneer and entrepreneur, discusses the three prerequisites for successful agentic AI implementation: delineation and unification of knowledge domains, MCP-enablement of knowledge systems, and content preparation for AI. He engages workshop participants in a hands-on segment using role playing to better understand the roles of LLMs, agents, MCP (Model Context Protocol), and KM systems. Building simple agents and writing and testing prompts (no coding required), participants experience firsthand how knowledge domains empower AI agents to think and act autonomously. Get tips and techniques to make your knowledge AI-ready and future-proof your KM strategy.

This workshop is all about learning and doing it in a new way! First, test your art skills with our experienced artist and longtime KMer while discussing creativity with art-based interventions. Then discover what you can learn from the traditional cooking skills and recipes of our ancestors and how they have been handed down to others. Do we learn by doing? Experimenting? This fun event challenges your thinking, creativity, and culinary skills by offering a great opportunity to connect with fellow KMers.

Over the last several years, the concept of semantic layers has moved from obscurity to a central means of enabling enterprise AI. Our speakers, who have been leading the field in this area for well over a decade, define the value and purpose of a semantic layer; explain the component parts of a semantic layer, including the value each delivers; and lay out the various approaches to designing and implementing a semantic layer. They provide a highly interactive environment and opportunities to experience multiple design steps in the process through directed small group activities.

This hands-on workshop provides a practical introduction to retrieval-augmented generation (RAG), showing how to ground LLMs in trusted, up-to-date data for more accurate and explainable results. Participants build an end-to-end RAG pipeline with guided, runnable examples: document ingestion and chunking, embedding generation, vector indexing, retrieval strategies (keyword, dense, and hybrid), prompt construction, and response citation. Fried shares techniques to improve quality and robustness, including query rewriting, reranking, metadata filtering, evaluation of retrieval and generation, and defenses against hallucinations and prompt injection. The workshop covers operational considerations such as latency/cost trade-offs, observability, and updating indexes as data changes. Attendees leave with reusable reference code, a checklist for diagnosing common failure modes, and a clear set of patterns for deploying RAG applications in production.

The adoption of AI in the enterprise presents unique and significant challenges, moving beyond basic productivity improvements in developing textual content. Organizational complexity includes content in multiple languages, inconsistent "copies," important information embedded in trade names and numeric data, and the critical need to maintain security trimming. Furthermore, a major challenge across all sectors is identifying and managing the ad hoc use of workarounds by individuals, often referred to as "shadow AI.” This interactive workshop aims to address these critical issues by concentrating on the development of effective governance and architectural frameworks for AI, rather than discussions about technological advancements.

Join us for the opening of the Enterprise Solutions Showcase to explore the marketplace and connect with the community. Discover cutting-edge products and services from the industry’s top companies while enjoying live music, a complimentary beer and wine bar, and a selection of light hors d'oeuvres. Whether you're looking to scope out new tech or reconnect with industry peers, this vibrant reception is the perfect place to build your network.

Meet up and bring your ideas, questions, enterprise challenges, collaboration, knowledge-sharing concerns, and insights to our longtime practitioners and recent KM practitioners. These veterans and newer enterprise specialists share their experiences and learnings during the showcase reception on Monday following the workshops.

Join us for a night of friendly competition! Whether you're a seasoned strategist or just looking to unwind, we'll have a variety of games ready to play. Come grab a seat, meet fellow attendees, and let the games begin.

The workforce is greying. Across the OECD, the median age has climbed past 44, retirement waves are accelerating, and decades of hard-earned tacit knowledge are quietly walking out the door of every enterprise—often with no plan to capture it. For KM leaders, this is not just another demographic statistic. It is the moment this function moves from supporting role to center stage. Drawing on his new book, Pontefract shares the central framework of his research: the Wisdom Wheel. Built on five components—Mentor, Collaborate, Capture, Renew, and Purpose—it is a practical playbook for what enterprise learning, human-centric AI, and knowledge sharing must look like when half the workforce is over 50. He discusses a new concept, Rivers, Rocks, and Rubies, his framework for the three wisdom-sharing eras across every team member’s career, and the real organizational challenges piling up as the Rubies retire and expertise bottlenecks, leadership vacuums, and the collapse of institutional memory runs rampant. Expect golden nuggets and cases from BMW, Deutsche Bahn, TELUS, Tata Chemicals, and Turing AI and a candid look at how AI-enabled cognitive task analysis is finally helping organizations capture the expertise that has always been invisible. How can grey turn to gold? Come find out from our always entertaining and energizing speaker!

Most enterprise AI deployments share a quiet structural flaw: They are stateless. Each interaction begins from scratch, expertise evaporates between sessions, and the 80% of institutional knowledge that lives as tacit judgment in expert intuition, cultural norms, and unwritten heuristics—remains entirely invisible to the system. Clarke provides a different design paradigm, one that treats enterprise AI not as a collection of applications but as a cognitive operating system: combining the language fluency of LLMs with the formal reasoning guarantees of knowledge graphs and a compilation layer—drawing on Karpathy's LLM wiki concept and Polanyi's theory of tacit knowledge—that converts human experience into structured, compounding, queryable intelligence. The organizations that lead the next decade will not be those that find the best model. They will be those that build the best memory. Grab lots of insights and ideas from our experienced and popular speaker.

Enterprises are racing to deploy AI agents, but most are feeding them the same messy content that has confused human agents for decades. The result is predictable: Confident answers that are wrong, inconsistent, or out of policy. Gopal makes the case for a fundamental shift in how we think about KM: not as a library of content for organizations to store and humans to search, but as a set of instructions for AI to follow. When knowledge is structured, governed, and trusted, AI progresses from carefully parametered pilots to confident and consistent actions at scale. When it isn't, no amount of model horsepower can save you. Drawing on real-world deployments across banking and financial services, telecom, government, and healthcare, Gopal shows what it takes to turn institutional knowledge into instant, trusted answers at scale and why the organizations that treat knowledge as instruction, not documentation, will be the ones whose AI actually works.

This fast-paced keynote analyzes current data trends, including the limits of vector search and the traps of un-governed, auto-extracted graphs—to explore what true semantic grounding requires. Stripping away the hype, Stroker takes an honest look at the time, budget, and cross-functional effort needed to build an interconnected knowledge graph, counterbalanced by the tangible payoff: a deterministic, future-proofed cognitive asset that drives real organizational knowledge. Get lots of ideas and insights from our enterprise data strategist.

A major U.K. nuclear new build program set out to replicate a previous extended producer responsibility (EPR) project—reusing designs, supply chains, and an approved safety case to cut cost and risk. Replication sounds straightforward, yet it’s anything but. Reusing data, experience, and lessons at scale creates real complexity, especially in a safety-critical environment, where decisions must be traceable and trusted. Our speakers share an on-the-ground case study of how formal KM and information governance capability were introduced early to meet that challenge. The program faced familiar problems: document-heavy legacy practices, fragmented ownership of information, and a growing risk of losing critical expertise from an aging nuclear workforce. Hear how KM was built as a real organizational capability—not a side function—including defining clear roles, standards and governance, and embedding KM into everyday engineering and safety work. An enterprise data model and organizational knowledge graph gave teams a connected view of how engineering data, safety requirements, organizational knowledge, and business processes fit together. Ontology-based modeling helped establish a reliable “golden thread” for safety case evaluation and long-term asset insight. Get ideas for how change management and organizational learning can shift behaviors, and hear tangible outcomes and lessons learned that translate well beyond the nuclear sector.

AI is making explicit knowledge a commodity. It can retrieve, summarize, translate, and reorganize information in seconds. So where does that leave KM? After leading enterprise KM at Microsoft, Monney explored that question while writing and publishing a bilingual book with a Swiss cellar master and winemaker whose lifetime of expertise risked disappearing. The experience challenged one of KM’s oldest assumptions: that capturing tacit knowledge is the objective. Instead, Monney argues that explicit knowledge is only an intermediate step. The real challenge is designing knowledge that can be effectively transmitted from one human mind to another. Using the book as a real-world case study, he shows how AI augmented every stage of that journey and why AI is shifting the center of gravity of KM, from capturing knowledge to designing knowledge transmission.

Innovation doesn’t take root because of technology—it takes root because people feel invited, equipped, and safe enough to participate. Turner describes joining a large, highly regulated organization and, within 60 days, launching a fully operational innovation incubator that achieved 93% voluntary participation at kickoff. Drawing on experience in enterprise transformation, KM, and culture-building in finance and tech sectors, Turner breaks down the human dynamics that made rapid adoption possible in a risk-aware environment: psychological safety, trust-building, and empathy. She shows how to design engagement experiences that align with business strategy while accelerating learning, sparking experimentation, and strengthening cross-functional collaboration. Attendees leave with practical, actionable techniques for launching AI-related initiatives that feel energizing rather than intimidating.

Leading change during restructuring is rarely about perfect plans; it’s about helping people transition while the ground is still moving. During a period of major restructuring, our speaker’s R&D organization applied a structured, people-centered change management approach to support teams through uncertainty, shifting roles, and reduced capacity without losing momentum or focus on critical work. Starkey presents a real-world change management case study focused on how the approach was designed and executed in practice. Topics include how the change was framed for employees, how leaders were equipped to support their teams, and how readiness, communication, and sustainment were addressed amid ongoing disruption. Starkey’s approach emphasized the transition experience: how people make sense of what is changing, what is not, and how they continue to operate effectively as the organization evolves. KM practices were selectively embedded within the change strategy to reduce risk during transition periods, particularly when role changes and staffing reductions increased the likelihood of critical knowledge loss. KM was used intentionally as a supporting mechanism to preserve continuity without adding burden to already-stretched teams. She discusses the benefits and challenges of a practitioner operating across change management and KM roles and the impact this integration had on alignment, timing, and trust. Get practical insights for leading change in knowledge-intensive environments and designing change strategies teams can realistically absorb.

As organizations adopt AI-powered search and chatbots, many are discovering an uncomfortable truth—their knowledge was neither designed for AI consumption nor employee behaviors. Traditional knowledge practices prioritize completeness, accuracy, and sometimes visual flair. But with the emergence of AI in our daily lives, employees are increasingly expecting concise, trustworthy knowledge experiences. Speakers share lessons learned, current practices, tools and processes, and more. Elker shares lessons learned as an internal KM leader in a 20,000-person company making band-aids; discusses the intersection of KM, employee experience, and AI readiness; and focuses on the strategies and practices of enabling the shift from traditional KM to a more AI-enabled enterprise KM experience. Moffett discusses how organizations across Ireland have embedded KM principles into real operational practice and highlights how Irish public, private, and nonprofit organizations have adopted KM to improve decision making, strengthen innovation, and enhance organizational performance. She illustrates how firms have implemented knowledge-sharing cultures, collaboration, digital tools, and structured processes to capture, store, and apply organizational knowledge in meaningful ways, including the injection of AI and automation.

Wind down after a full day of stimulating sessions with a casual happy hour right on the showcase floor. Grab a drink, visit the booths, and dive deeper into conversations with fellow attendees, speakers, and sponsors. It’s the perfect, laid-back setting to talk shop, swap ideas, and solidify your new connections.

Your intranet should be more than a static portal—it can become a dynamic workplace partner that helps shape communications, strengthens culture, supports decision making, and serves as the authoritative foundation for AI. Many organizations struggle with scattered content, inconsistent messaging, and experiences that fail to meet employee expectations. This practical session provides a blueprint for building or enhancing a modern intranet that delivers real impact using the latest features of SharePoint, Viva Engage, Teams, and Copilot. Learn how to blend structured content, personalized experiences, and community engagement while creating AI-ready content models, building a trusted knowledge foundation, and establishing a governance framework to sustain quality. Get a clear road map for transforming your intranet into a strategic communications channel and a cohesive digital workplace hub that employees trust, leaders rely on, and AI can confidently use.

Most organizations have declared their AI principles. Few have operationalized them. That gap is where liability gets quietly laundered—where "human in the loop" becomes a rubber stamp and the people closest to harm are the last ones consulted. Drawing on assessments of hundreds of real-world AI use cases, fresh insights from the United Nations forum on AI governance, and her work building regional Centers of Excellence for Responsible AI, Boinodiris shows KM professionals how to move from principles to practice and how regions can stop waiting for federal leadership and start building the coalitions, infrastructure, and shared accountability frameworks they need now.

Get insights from our industry practitioners on their thoughts for future workspaces and places and how knowledge-sharing will evolve. They share case studies and real-world examples of which enterprises are utilizing new and emerging technology to support enterprise culture, change, communication, and knowledge-sharing. Hear their thoughts on how innovations are reshaping the way teams think about, collaborate on, and get actionable insights to help future-proof knowledge strategies and create work environments where AI and other new technologies add real value.

By aligning AI capabilities with human expertise and collaborative culture, organizations can transform enterprise systems into intelligent knowledge ecosystems that promote learning, preserve institutional memory, and support sustainable innovation across global operations. Modern enterprise platforms capture large volumes of structured and unstructured information across financial processes, supply chain operations, procurement workflows, and customer interactions. While these systems store valuable institutional knowledge, traditional interfaces limit employees’ ability to retrieve contextual insights or reuse prior expertise. AI-powered knowledge layers can convert operational data and documentation into searchable and interpretable knowledge resources. Mistry discusses a layered architecture consisting of secure knowledge ingestion pipelines, semantic indexing mechanisms, conversational interfaces, and AI reasoning services that interpret enterprise data in real time. Through natural language interaction, employees can access explanations of financial processes, operational patterns, and historical decision rationales, enabling faster learning and improved decision making. These systems function as collaborative knowledge partners that augment human expertise rather than replace it. Get tips, ideas, and real-world examples from our practitioner.

Now in their 20th year, the Intranet and Digital Workplace Awards globally recognize outstanding contributions to the fields of intranets and digital workplaces. The awards uncover remarkable solutions that deliver business value and then share them with the wider community. This session is a chance to meet award winners and look behind the firewall to see what the winners have achieved and, more importantly, how they achieved it! Get tips and ideas and see creative, inspirational, and valuable ideas to take back and apply in your organization.

Wind down after a full day of stimulating sessions with a casual happy hour right on the showcase floor. Grab a drink, visit the booths, and dive deeper into conversations with fellow attendees, speakers, and sponsors. It’s the perfect, laid-back setting to talk shop, swap ideas, and solidify your new connections.

Three forces are converging to reshape how organizations manage and share knowledge: AI is revolutionizing how enterprises operate, the "silver tsunami" risks decades of institutional wisdom walking out the door daily, and Gen Z and Millennials now make up two-thirds of your workforce—generations more likely to tap TikTok for guidance than a senior colleague’s shoulder. The clock is ticking, but the opportunity is enormous! Goetting’s interactive session looks at how to use AI to capture institutional knowledge before it disappears, build skills marketplaces where expertise flows in every direction regardless of title or tenure, and bridge the generational communication gaps that slow down knowledge adoption and reuse. Packed with live audience participation and immediately actionable frameworks, attendees leave with strategies to build a truly human-centric knowledge-sharing culture—and implement them Monday morning.

Organizations today are more generationally diverse than ever, bringing together Baby Boomers, Gen X, Millennials, and Gen Z in the same teams. Each generation brings unique expertise, work styles, and communication preferences, but knowledge is often lost when it is not effectively shared across age groups. Powell discusses successful strategies to bridge generational divides, foster cross-generational collaboration, and create sustainable knowledge transfer processes. Learn practical ways to harness the strengths of each generation, leverage mentorship and reverse-mentorship programs, and implement learning practices that ensure institutional knowledge is retained, applied, and expanded. With insights for both leaders and team members, Powell demonstrates how organizations can turn generational diversity into a competitive advantage in KM.

KM practitioners have always balanced people, process, and technology, but nothing has shifted that balance as rapidly and profoundly as AI. KM has evolved from taxonomy and search optimization to enterprise-wide governance and now human-centered AI augmentation. Haworth shares lessons from embracing change rather than resisting it and presents actionable insights into how to reframe AI as a collaborator, not a threat, aligning it with KM goals like findability, trust, and reuse; leverage human judgment as the defining competitive advantage in a world where tools can generate knowledge but cannot curate wisdom; navigate organizational change with empathy and curiosity, turning uncertainty into a source of joy and innovation; and drive adoption and resilience by tying new technologies to real human storytelling, work outcomes, and everyday wins. She blends personal narrative with practical takeaways, giving KM leaders and practitioners tools to thrive when the industry around them feels like it’s changing faster than the tools in their toolkit.

Most organizations say knowledge sharing is extremely important, yet they don’t make it a priority and leave it to chance. Teams are expected to collaborate, support one another, and transfer knowledge, but few are taught how to do it in real time when workloads shift, gaps appear, or critical know-how sits with just one person. This is where forceful backup comes in: actively stepping in when life changes plans, proactively sharing what you know, and maintaining the ability to support emergencies without losing the ability to support your customer. Forceful backup is based on the military concept of forceful team backup, where team members at any rank speak up when errors or safety issues are noted. Young explores the power of the forceful backup as a practical way to share knowledge and boost team performance. Knowledge sharing becomes a daily habit that surfaces hidden expertise, reduces the dependence on a single person, improves handoffs, and builds team responsibility into daily routines. Tacit knowledge sharing becomes supported through a culture of forceful backup. Teams need habits that make knowledge flow when it matters. Young illustrates how forceful backup makes knowledge sharing an active advantage and shares practical strategies to strengthen collaboration, keep work flowing, and build more resilient teams with daily knowledge-sharing behaviors.

AI is being rapidly deployed across enterprises, yet many organizations are discovering a hard truth: AI systems are only as effective as the knowledge that underpins them. No knowledge, no intelligence. Cannon challenges the prevailing assumption that AI implementation is primarily a technology problem, demonstrating that the success or failure of AI initiatives is fundamentally a KM issue. Without structured, governed, and trusted knowledge, AI produces inconsistent, biased, or low-value outputs. Drawing from real-world KM practices and emerging AI integration patterns, he shares a practical framework for aligning knowledge strategy with AI capability. Learn how knowledge quality, structure, context, and governance directly impact AI performance and decision-making outcomes.

Wind down after a full day of stimulating sessions with a casual happy hour right on the showcase floor. Grab a drink, visit the booths, and dive deeper into conversations with fellow attendees, speakers, and sponsors. It’s the perfect, laid-back setting to talk shop, swap ideas, and solidify your new connections.

In today’s environment of constant disruption, change is no longer a one-time event—it is continuous, complex, and deeply human. Our panel discusses what it takes to lead transformation while conditions are still evolving and draws on real-world experience across industries and organizational contexts. The conversation looks at how to support people through uncertainty, equip leaders and employees to communicate with clarity and confidence, and design change approaches that teams can realistically absorb. Panelists focus on the role of KM as a practical enabler of change—helping preserve continuity, reduce risk, and maintain momentum during periods of transition. They offer candid insights, practical lessons learned, and tips on dealing with change in a world that refuses to stand still. Get lots of ideas on how to navigate continuous change by connecting people, knowledge, and action.

Knowledge repositories were built to store information, but storing information and activating organizational intelligence in an AI-first world are fundamentally different problems. Brittan makes the case that the next evolution in KM is context: building a persistent, measurable understanding of what an organization knows, who knows it, and whether that knowledge is healthy enough to be trusted. Drawing on Bloomfire’s innovative work building an enterprise intelligence engine, he discusses the power of conducting knowledge health assessments with enterprise customers; explores what it means to move from passive content management and RAG-based search to an active, context-rich intelligence infrastructure; and shares real-world examples of organizations building intelligence infrastructures. Get tips on creating healthier knowledgebases, reductions in compute power, and overall better AI outcomes.

Every company wants AI. Almost none have solved the thing that makes AI trustworthy: the knowledge underneath it. Research shows most organizations can manually verify only 8%–12% of their knowledge, and AI scales the errors in everything else at machine speed. This is the knowledge accuracy gap, and it is the real reason AI programs stall. Nucci discusses how KM is not a support function but rather the infrastructure that decides whether AI works at scale or quietly fails. He looks at why accuracy, not model choice, is the true bottleneck; what separates the companies pulling ahead; and why knowledge managers are about to become the most important people in their organizations. If you own knowledge at your company, this is the year that work stops being invisible.

Our resident futurist discusses a Top 10 list of what is new in AI related to KM and AI. Given the pace of change, topics will be up-to-the-minute at the conference.

Several years ago, TechnipFMC’s KM team designed a process to grab expertise, experience and knowledge before it walked out the door. However, this structured knowledge transfer process was cumbersome and time-consuming and wasn’t adopted. The company is piloting an AI-based interview tool that enables experts to share their knowledge through a guided, self-paced experience, creating a faster and more scalable approach to knowledge transfer. The interview transcript becomes the foundation for AI-generated knowledge pages in the enterprise wiki, moving employees most of the way to a completed asset in a fraction of the time the previous approach required. What was once a resource-intensive process spanning 3–6 months can now be completed in weeks. Conversational AI then makes that knowledge easier to access and apply by enabling employees to ask questions and receive contextual answers grounded in approved content across the wiki, Teams, and mobile devices. Speakers share a real-world example involving a retiring expert and demonstrate how recorded interviews, AI-assisted content creation, and streamlined workflows can accelerate knowledge transfer while maintaining quality and trust. Get insight into a practical, enterprise-ready approach for preserving critical expertise and making it more accessible across the organization.

Digital transformation often underdelivers because organizations manage data, systems, AI, knowledge, and performance as separate initiatives rather than one connected system. Green shares a practical framework to understand where digital efforts break down and how to rebuild trust in outcomes. Using plain-language explanation and real-world scenarios, she illustrates how the framework can be used to diagnose weak points in a digital ecosystem, strengthen trust in digital and AI-enabled outcomes, and guide smarter investment, governance, and adoption decisions. Get a simple but actionable model for connecting technology capability with organizational trust, usability, and long-term performance. Drawing on topics of the SECI model, RAG, governance, and knowledge stewardship, Green provides a practical methodology for integrating AI into KM without sacrificing accuracy, traceability, or trust, as well as tips on how to align people, processes, and technology to build systems where AI accelerates access to knowledge and KM provides the foundation that makes AI reliable and useful.

AI can make work more efficient and easier. But at what cost? As employees increasingly rely on AI-generated answers, are they exercising the critical thinking and creative problem-solving skills needed to navigate uncertainty and adapt to change? Speakers share emerging research on the cognitive impact of AI-assisted work and provide practical recommendations for leaders seeking to build teams that remain curious, analytical, and capable of independent judgment and strategic decision making in an AI-driven workplace. They have been investigating how GenAI is reshaping critical thinking, particularly in journalism, media studies, and education. Traditional skills like source evaluation and bias detection are no longer sufficient. Through theoretical analysis and empirical research, they redefine critical thinking for the AI era and develop educational tools that empower people to critically engage with both human and AI-generated content.

How did one of the oldest and largest health and human services nonprofits in the world shift from an aging KM model to AI readiness? With an eye on the future, this session explores how one organization reversed decades of inattention to reinvigorate content governance, culture change, technology, and KM value for a global network. Learn how fragmented knowledge was transformed through content governance frameworks, scalable enterprise focused maturity models, and user-centered design principles that prioritize findability and trust. Speakers share practical strategies that demonstrate how a strong KM foundation enables AI readiness by powering more effective search, smarter insights, and measurable, network-wide impact.

In early 2026, Novartis created its first Enterprise Knowledge Council (EKC)—a “community of communities” designed to align enterprise-wide KM strategy, eliminate duplication, and accelerate the reuse and visibility of KM assets across the organization. Instead of a traditional governance body, the EKC acts as a shared strategic space, built by the KM community for the KM community. Carlton shares the blueprint behind building the EKC from the ground up: how to unite distributed KM efforts, bring diverse teams into one coherent forum, and create a single enterprise voice around KM priorities. She provides tips on how to create an enterprise-wide forum that aligns KM strategy without imposing hierarchy; design a “community of communities” model that fosters trust, contribution, and shared ownership; reduce duplication and increase reuse through visibility, shared standards, and streamlined collaboration; and ensure representation that feels balanced, meaningful, and energizing. Get practical steps, lessons learned, and inspiration for organizations seeking to unify KM efforts into one shared, strategic direction.

The central promise of KM has always been simple: Get the right knowledge to the right person at the right time. For decades, the discipline delivered on that promise by solving the findability problem—investing in taxonomy, search, and governance to make knowledge accessible. The implicit assumption was that if someone could find the information, they would use it. That assumption is now broken. In an environment where AI can produce fluent, confident, well-formatted answers to nearly any question in seconds, the challenge is no longer finding information, it is knowing whether to believe it. This credibility crisis has spread to every knowledge asset in the enterprise. Employees now bring the same skepticism they developed toward AI hallucinations to internal wikis, process documentation, and knowledgebase articles. The default posture has shifted from search-and-trust to search-and-verify, which is quietly destroying the productivity gains that knowledge systems were built to deliver. Trust is a measurable, designable property of a knowledge system, and it may be the most consequential capability that knowledge practitioners can build in the next 5 years. Mohr discusses a practical trust architecture framework that gives KM leaders concrete tools to make trust explicit, visible, and governable. Get a clear model for diagnosing where trust is breaking down, a vocabulary for communicating the business cost of knowledge credibility failures to leadership, and a set of immediately actionable design principles for building knowledge systems that people actually believe.

When USAID was dismantled in early 2025, groups and individuals mobilized to rescue information resources created by staff and partners. The patchwork of federal laws, policies, and guidance and a lack of compliance enforcement meant that preservation of the agency’s legacy through official channels was in question. The USAID Knowledge Rescue effort, along with partner organizations and other volunteers, has formed a decentralized network to preserve and provide public access to over 60 years’ worth of USAID data, information, and knowledge. Get a snapshot of the progress and challenges faced by those involved in rescuing USAID’s information. The USAID Knowledge Rescue effort has captured over 274,000 materials, made 136,000 available to the public, and is building a digital repository to ensure public access. Other organizations and individuals have mirrored websites and rescued data and information resources that are now hosted on a variety of online sites. Former USAID partners played a key role in rescuing information resources and providing continued access. USAID’s experience in 2025 suggests that federal agencies and organizations would benefit from a knowledge “living will” or legacy plan to preserve data, information, and knowledge. These would include inventories and contingency plans aimed at ensuring public information remains accessible and is preserved.

Johnson was a new KMer who attended several KMWorld events in 2024 and was able to begin a new enterprise KM program at the end of 2025. Get tips and lessons learned as Johnson shares the journey of an organization that realized its knowledge lived in people's heads, scattered emails, laminated handouts, and outdated SharePoint files and decided to build something better. This session shares what it takes to build something from nothing, without a dedicated team, a formal mandate, or a playbook to follow. It discusses diagnosing the problem: mapping the chaos of decentralized knowledge across emails, Teams chats, paper job aids, and an intranet that couldn't serve associates on live customer calls; building the case: connecting KM investment to organizational priorities with measurable KPIs for each; designing for the user: creating an article template, and writing standards built around one question—Can an associate find what they need in 10 seconds while talking to a customer? She highlights executing with college interns; leveraging AI tools using Claude to accelerate article creation, quality review, and content standardization; and allowing a small team to produce at a scale that wouldn't have been possible otherwise. Get an honest perspective on what it takes to get organizational buy-in, keep content alive, and make knowledge a discipline rather than a project.

As enterprises move from AI experimentation to scaled adoption, the biggest challenge is no longer access to GenAI: It is creating the organizational intelligence, governance, and workflow integration needed for AI to deliver repeatable business value. Sajan discusses the concept of an enterprise intelligence layer (EIL), a connected operating model where human expertise, enterprise knowledge, AI decision making, and agent-enabled execution continuously inform one another. Drawing from work being led by his team at United Airlines, Sajan illustrates how an EIL can help organizations move beyond fragmented AI pilots and isolated chatbot use cases toward a more scalable, governed, and workflow-embedded AI model. He emphasizes keeping humans in the loop; how AI can synthesize, recommend, draft, and accelerate work; and how enterprise teams establish business context, validate domain expertise, approve outputs, interpret risk, and own accountability. Get a practical model for scaling AI responsibly by connecting people, knowledge, tools, governance, and outcomes into one closed-loop intelligence system. Blum discusses how AI doesn’t replace work. When grounded in the right knowledge, AI amplifies how to think, decide, and act. Learn practical lessons on how to use AI tools directly in day-to-day workflows by starting with the simplest interface of all: the chat window. Blum demystifies AI by showing how to ask better questions (prompts), clarify intent, and use AI as a thinking partner, but only when it has access to good, trusted knowledge. She discusses the critical role of KM in making AI effective, including how well-organized, accessible information enables more accurate outputs, better decisions, and more reliable automation. Attendees get ideas of how to use conversational AI to shape and refine effective prompts; recognize the importance of well-managed knowledge in AI outcomes; and identify work activities that can be automated, streamlined, or augmented with AI and leave with a clearer understanding of how AI works best when paired with strong KM and with concrete ideas that make AI truly work for them.

Every organization has experts who perform at an exceptional level—and can’t explain how. When they leave, that knowledge leaves with them. Not because no one tried to capture it, because most knowledge capture efforts only reach the surface—and what reaches the surface is rarely structured in a way that organizations can actually use. Asher presents a complete practitioner's framework—from extraction to embedding to AI-ready structuring—developed through doctoral research and more than 2 decades of fieldwork across engineers, chemists, craftsmen, managers, and high-stakes operational environments and grounded in peer-reviewed research in the publication The Learning Organization. Get a working understanding of the three layers of tacit knowledge and the methods suited to each as well as practical approaches to embedding tacit knowledge in ways that match how people actually acquire it.

As AI and advanced analytics reshape how organizations capture, process, and leverage knowledge, effective leadership is more critical than ever. Robinson discusses how professional services firms are full of expertise, but capturing and sharing that knowledge at scale is often harder than it should be. Too many knowledge programs rely on a small group of already busy experts, leading to low participation, outdated content, and burnout. Robinson shares how to build knowledge systems that work with people’s day-to-day reality. Drawing from real experience in leading knowledge and marketing strategy for a multi-office professional services firm, she offers simple, repeatable ways to capture expertise without asking people to “do more.” Get practical ideas to apply right away to move beyond tribal knowledge, reduce dependence on a few key individuals, and build knowledge ecosystems that scale across teams and offices without burning people out.

One of the most important challenges facing KM professionals is not just capturing, organizing, or retrieving explicit knowledge, it is helping organizations convert that knowledge into judgment, insight, action, and competitive advantage. This is the next evolution of KM from KM to wisdom management. As AI makes access to information faster, cheaper, and more scalable, the role of humans becomes even more—not less—important. Organizations can now acquire and synthesize explicit knowledge at unprecedented speed, but they still need human wisdom to interpret context, make decisions, recognize risk, exercise judgment, and apply knowledge to create business value. Meanwhile, corporate reorganizations, retirements, workforce disruption, and rapid change are causing critical tacit knowledge to walk out the door faster than many organizations can preserve it. Building on their talk at KMWorld 2025, Reskilling for Success in an Agentic AI World, our experienced speakers provide practical tips and insights on how KM professionals can “level up” their role in an AI-enabled enterprise by helping organizations to capture, translate, and activate tacit knowledge across functions in order to turn what the organization knows into what the organization can do. This is at the heart of organizational learning.

Organizations increasingly struggle to translate fragmented information, institutional knowledge, and stakeholder perspectives into coherent governance and decision-making systems. While AI promises new efficiencies, many organizations remain uncertain about how to apply it to strengthen organizational learning, collaboration, and human decision making. Speakers discuss a human-centered approach to AI-enabled KM through the lens of an organizational learning assessment and a real-world humanitarian network governance initiative involving international organizations and distributed stakeholders. They share practical lessons from applying positive inquiry-based assessment tools and AI-supported knowledge synthesis in complex environments, including approaches for integrating structured and unstructured knowledge, supporting stakeholder trust, and designing KM processes grounded in human interpretation and decision making. The case study combines AI with informal and formal documentation in a complex humanitarian network that included governance protocols, meeting records, and prior frameworks with informal knowledge sources such as interviews, notes, and stakeholder observations. Using AI-assisted synthesis and comparative analysis, the team identified critical disconnects between documented organizational behavior and stakeholder perceptions. This enabled the network to surface tacit operational patterns, validate organizational realities across multiple perspectives, and develop governance structures to more accurately reflect how the network actually functioned.

How do you make an AI recommendation system trustworthy in education? It's less about algorithms and more about KM. Pasquon shares lessons from designing an AI-powered learning pathway recommender for a Swiss university's project management continuing education that guides professionals through a complex modular catalogue, matching competency gaps, career goals, budget, and time to optimized, explainable sequences. She discusses how faculty intuitively knew module prerequisites, effective combinations, and ideal pathways, but nothing was formalized. The real hurdle wasn't prompts or models; it was knowledge elicitation and structuring. She discusses using stakeholder interviews, co-designed competency frameworks, encoding prerequisites, and role logic to form a structured knowledgebase. This fed auditable AI prompts, where recommendations cite specific gaps and counseling rationale.

Connect with peers over drinks and great conversation on KM, AI, search, data analytics, and taxonomies. Exchange insights and challenges in a relaxed atmosphere at the hotel bar. (This is a cash bar event.)

Rob Stein believes success becomes far more attainable when ambition is paired with the right blueprint, decisive action, relentless consistency, and time. A national speaker, performance coach, entrepreneur, and author, Stein has built and scaled multiple businesses, created the Impossible to Fail framework, and coached thousands of entrepreneurs and high performers on how to move from overthinking to focused execution. In this session, Stein shares a clear, repeatable approach for turning big goals into sustainable progress. Attendees learn how to overcome fear, simplify decision making, build traction, and move forward with clarity and confidence. Drawing from the principles in Impossible to Fail, Stein offers practical strategies for leaders and teams who need less noise, more focus, and a reliable way to take action without hype, fluff, or guesswork.

As regulators raise the bar on explainability and accountability, compliance teams are under pressure to move beyond simply generating alerts toward building systems that can withstand scrutiny. Our popular speaker discusses what "defensibility" means in practice for AI-driven surveillance; the shift from detection to demonstrable, auditable control; and lessons learned from deploying AI across financial crime compliance programs. Using real-world examples, she provides insights, ideas, and tips for the successful enterprise.

Autonomous AI agents change the way we access information, interact with enterprise data, and decide between building or buying our applications. These agents also come with many risks and unknowns, especially when applied to business-critical information. Donzé discusses how to ensure conversational AI agents provide reliable, hallucination-free answers; how to avoid chaos when your business users can vibe-code their own apps in a few hours; and what kind of enterprise foundation you need to fully benefit from AI acceleration while controlling the risks. He illustrates with real-world examples and provides lots of insights and tips.

As organizations race to adopt AI and agentic AI, much of the attention is focused on models, workflows, and interfaces. But the long-term success of AI will depend on something more fundamental: the structure, interpretability, quality, and accessibility of the organization’s knowledge and data. AI systems cannot reason effectively, act reliably, or earn trust if they cannot understand the information they depend on. Enterprise knowledge is often fragmented across documents, systems, teams, taxonomies, and processes. For AI agents to deliver value beyond narrow use cases, they must be able to navigate this complexity, interpret context correctly, retrieve the right information, and apply it in ways that are accurate, explainable, and aligned with business needs. Stihec discusses why knowledge and data must become the foundation of a scalable AI strategy; looks at the gap many organizations face when they invest heavily in AI models and workflows while underinvesting in the knowledge layer that makes those systems useful; and provides a knowledge-first approach that can improve trust, reduce hallucinations, expand AI use cases, and create the conditions for AI and agents to scale across the enterprise. Illustrating with real-world examples, he shares insights and ideas for the successful enterprise.

Our organizations are changing as demographics, technology, and AI reshape how people learn, collaborate, and create value. As explicit knowledge becomes increasingly accessible through AI, a new challenge is emerging: How do organizations ensure that experience, judgment, and institutional know-how continue to flow across generations? Engage with our panelists as they discuss how evolving workplace expectations are redefining mentoring, collaboration, leadership, and organizational learning. Drawing on perspectives from KM, publishing, HR, and the future of work, the discussion looks at practical approaches to preserving and transmitting the human expertise that AI cannot replace.

From learning to interact with AI and prompt engineering to improving workflows and making an impact on enterprise processes, this discussion shares what’s happening in our organizations now and what this might look like in the future.

Getting new talent up-to-speed, retraining enterprise knowledge, and building platforms to spark innovation are critical for enterprise success. Join the conversation, share your practices and insights, and hear what other KMers are doing in their enterprises.

KMWorld magazine is proud to sponsor the 2026 KMWorld awards, KM Promise & KM Reality, which are designed to celebrate the success stories of knowledge management. The awards will be presented along with Step Two’s Digital Awards, where you get a sneak peek behind the firewall of these organizations.

Our experienced speaker and writer looks at what the role of humans is in new KM and AI world, specifically the intersection of human ingenuity and artificial intelligence. Moving beyond the myth that creativity is an inborn gift, he discusses how it is a “muscle” that can be strengthened through AI and human understanding, and reframed not as a threat, but as a “mirror’ that can reflect our ambitions. He shares neuroscience and practical tools to navigate a future where human intention remains the core of meaningful creation.

While GenAI prototypes have captured the industry's imagination, transitioning isolated models into compliant, secure, and production-grade enterprise systems requires a massive architectural paradigm shift. For Fortune 500 organizations, the LLM is only a fraction of the solution. The true challenge lies in orchestration, legacy system integration, and enterprise-grade governance. Drawing on applied research and hands-on deployment experience at Google Cloud, Kaur discusses the technical realities of scaling agentic AI and next-generation contact center platforms and the critical "AgentOps" layer and demonstrates how to move beyond basic chatbot workflows into complex, multi-agent enterprise networks. As enterprises scale their deployment of intelligent discovery engines, they face a critical tension between personalization and data privacy. Jha discusses how recommendation systems drive immense value across enterprise search, content discovery, and internal knowledge assets, but traditional architectures often risk exposing proprietary business context or sensitive user information. He shares how practical approaches to engineering privacy-preserving recommendation systems maintain high predictive accuracy without compromising security and looks at how modern architecture frameworks, including self-hosted configurations, private AI modeling, and anonymization pipelines, prevent data leakage. Get lots of ideas and insights from our speakers.

Your data is most likely stored and analyzed in layers, across environments with an architecture that is far beyond APIs and tech stacks. When you think about it, this collective is training the AI that your CEO thinks will replace you. Spoiler alert: It won't replace you, but it will expose every gap, inconsistency, and 'we'll fix that later' decision you've ever made. At NASA, where human capital data management and responsible AI use must navigate OPM compliance, executive orders, and administrative workforce directives, we've learned there is no successful recipe to operate and execute if data standardization and knowledge transfer are not key ingredients. Get insights, ideas, and tips for success with AI in your enterprise.

"Is your content AI-ready?" is the wrong question. A policy document, a technical manual, a marketing white paper, and a step-by-step procedure all need to be AI-ready in fundamentally different ways—and the readiness criteria shift again depending on whether the content is feeding RAG, an enterprise search index, a chatbot, or an agentic workflow. Treating content readiness as a single checklist is why so many AI initiatives produce technically clean content that still fails in production. Earley shares a three-way framework: content purpose × AI deployment use case × readiness dimension. He explains why chunking quality matters most for RAG but barely for AI search, why question-answer alignment is the dominant dimension for chatbots, and why agentic workflows demand explicit relationship modeling that other use cases tolerate without. Get practical methods for diagnosing which dimensions matter for their content and their AI use case—instead of trying to be ready for everything at once.

Prompt engineering is the practice of designing structured, intentional inputs that guide AI systems toward accurate, reliable, and high-value outputs. As organizations increasingly rely on GenAI, understanding how to shape model behavior becomes a critical skill—not just for developers, but for analysts, educators, and business leaders. Patrick discusses the core principles of prompt engineering, from foundational concepts to practical frameworks such as persona-based prompting, chain-of-thought structuring, and RAPID/CARE-style frameworks that help users create prompts that are consistent, transparent, and repeatable. Understand why prompt engineering matters—how it improves accuracy, reduces hallucinations, enhances reasoning, and unlocks more predictable performance across tools like Copilot, Gemini, and AskSage. He shares how to apply these techniques through examples that show the difference between ad hoc prompting and systematic, framework-driven design; how the distinction between chatbots and AI agents helps teams decide when simple conversational interfaces are enough; and when agentic systems can deliver greater automation and impact. Get a clear mental model of prompt engineering, a set of practical frameworks to apply immediately, and a grounded understanding of how modern AI systems—from chatbots to agents—fit into their workflows.

The PDF is the most widely used digital document format with more than 290 billion new PDF documents created every year. Government agencies, regulated industries, and other information-reliant organizations rely heavily on PDFs, with millions of official documents published in PDF format. The PDF software market, currently valued at more than $2 billion, is projected to nearly triple to $5.72 billion by 2033. As AI is reshaping how organizations extract value from content and data, but the world’s most popular document format, the PDF, has emerged as one of AI’s biggest obstacles. The PDF has critical limitations when it comes to AI, KM, search, and discovery: There is no semantic meaning, a PDF is not a table to a machine, and a heading is not a heading. AI systems require structured, semantically rich data. Enterprises are spending billions converting PDF archives into structured formats to unlock knowledge that is unreadable by machines. No matter your industry or whether you're in document management, AI/ML, legal tech, or enterprise IT, solving the PDF problem is one of the most pressing—and underappreciated—challenges of the AI era. Get lots of ideas from this talk!

As organizations race to deploy AI, many are discovering that access to documents alone does not produce trustworthy results. Traditional repositories may store content, but without business context, AI systems struggle to deliver accurate, explainable, and actionable outputs. Our speaker explores how enterprises can move beyond fragmented repositories and keyword retrieval to design context-first AI systems. He discusses why context is the missing layer between content and reasoning; how knowledge graphs and enterprise ontologies transform KM from passive storage into a living model of the business; and how connecting information to its relationships, provenance, and operational meaning organizations can ground AI in a structure that supports better retrieval, stronger governance, and more auditable reasoning. He shares practical design patterns for building context-rich knowledge environments to improve relevance, reduce hallucinations, and increase trust in AI-generated outputs and shows how explainability, traceability, and governance become essential design principles when AI is used in high-value enterprise workflows. Get a framework for rethinking KM as the foundation for enterprise AI, not just managing documents, but enabling systems that can reason responsibly over knowledge.

In the oil and gas sector, the primary value of KM lies in its ability to convert isolated incidents into proactive, enterprise-wide resilience. Our speaker discusses the Saudi Aramco Technical Alerts & Recommendations System (STARS) as a robust framework for "closing the loop" on technical learning. By consolidating varying sources of critical intelligence (such as internal plant incident investigations, major lessons learned, vendor technical alerts [VTAs] and government directives), STARS institutionalizes knowledge into a single, unified program. This integration breaks down the natural silos within large enterprises due to different operations or geographical locations, ensuring that vital learnings are not trapped within specific departments but are instead distributed proactively to prevent operational interruptions and HSSE (health, safety, security, and environment) consequences. Learn how this structured KM ecosystem fosters accountability and continuous improvement and how Aramco ensures that knowledge remains an active, living asset rather than a static record. Get a blueprint for how large enterprises can embed KM into their operational fabric, leveraging SME validation and cross-organizational collaboration to drive proactive risk management, and ensure that critical technical alerts result in tangible, verified improvements in facility reliability and safety.

Enterprises are spending heavily on AI, yet studies find that approximately 80% of AI deployments produce no measurable business impact. The root cause is almost never the model—it is the missing knowledge layer underneath it. AI agents are only as good as the knowledge they can access and reason over, yet most organizations treat knowledge architecture as an afterthought to their AI stack rather than its foundation. Speakers Alliata and Dewey highlight a practical, four-quadrant architecture that positions structured knowledge— specifically knowledge graphs—as the strategic backbone of any enterprise AI system. Drawing on applied research across regulated industries, including financial services, insurance, and compliance-intensive operations, they show how organizations that build a governed knowledge layer before scaling AI agents see dramatically better outcomes than those that scale first and structure later. Leveraging RAG is table stakes for KM agents.

Where is KM going with all the AI developments for the enterprise? How are our organizations responding to the social structures and changes in our world? How are they innovating and exceeding customer expectations? Hear highlights from the conference speakers and get inspiration and ideas from our practitioners and futurists to be ready for KM in 2027.