Enterprise AI World is part of KMWorld 2026 featuring five co-located events: KMWorld 2026, Taxonomy Boot Camp, Enterprise Search & Discovery, Text Analytics Forum, and Enterprise AI World. Upgrade to a Platinum Pass for your choice of two preconference workshops or access to Taxonomy Boot Camp on Monday. Workshops are also separately priced.

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.

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.

As AI increasingly influences how organizations operate, make decisions, and create value, leaders face a new challenge: preparing people for a future where work, learning, and decision making are fundamentally changing. Rainie explores how organizations can build resilience, adaptability, and human capability in an AI-driven world. Drawing on research and expert perspectives, he examines the opportunities and disruptions AI may create, the evolving relationship between humans and intelligent systems, and the leadership strategies needed to help people thrive through continuous change.

Rasmus shares his analysis of the most important developments shaping the AI landscape in 2026. He examines emerging technologies, enterprise adoption patterns, market shifts, and the broader forces influencing AI investment and innovation. Looking beyond today’s headlines, he also offers his forecast for what organizations should expect in 2027 and discusses where leaders should focus their attention as AI continues to evolve.

Hear from leading solution providers about the trends, opportunities, and challenges they are seeing across the enterprise AI market.

The promise of AI is compelling: greater speed, efficiency, precision, and insight. Yet while technical excellence is essential, successful AI adoption ultimately hinges on leadership. The differentiator is not the algorithm — it is the leader’s ability to drive acceptance, alignment, and trust across teams, organizations, and customers. Most AI leaders rise through technical expertise. However, technical mastery alone is insufficient. Through real-time client use cases, Reilly explores how AI leaders must strengthen their emotional intelligence (EQ), communication, and influence skills to translate vision into results. Hear stories of leaders who learned to shift from pushing solutions to pulling stakeholders into shared ownership — and when necessary, pivoting strategically to sustain momentum.

As AI adoption accelerates, organizations face increasing challenges surrounding intellectual property, compliance, governance, and accountability. Jones explores the legal and ethical boundaries of copyright and authorship in the age of generative AI, examining how training data, human creative control, and ownership structures affect the rights of organizations and creators. Levy discusses how organizations can operationalize intellectual property risk through procurement discipline, content licensing, rights-aware workflows, and governance practices that move beyond simply acknowledging risk. Hogue-Spears shares practical patterns for moving compliance, auditability, and policy requirements upstream into product development and release workflows, transforming governance from a bottleneck into operational infrastructure. Attendees gain practical guidance for navigating copyright, ownership, compliance, and enterprise AI governance.

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.

AI is rapidly becoming embedded in enterprise operations, yet many organizations still treat explainability as an optional feature rather than a foundational requirement. Rudden discusses why explainability must be built into AI systems from the beginning and not added later through dashboards or reporting layers. She explores the growing challenges organizations face around transparency, accountability, and trust as AI becomes increasingly involved in business decisions. Drawing on real-world examples, she illustrates how explainable systems support governance, risk management, and organizational confidence while helping enterprises prepare for increasing regulatory scrutiny. Learn why explainability is becoming core infrastructure for enterprise AI and what leaders should be doing today to build systems that can be trusted tomorrow.

Learn how one of the world’s largest decentralized software organizations is approaching enterprise learning, knowledge sharing, and AI adoption at scale. Drawing from Volaris Group, an operating group of Constellation Software and its unique decentralized operating model of more than 240+ businesses in more than 40 different vertical markets across 60+ countries, Ha shares lessons learned building coordination, alignment, and knowledge flow across a highly autonomous enterprise environment.

Modern enterprises operate across increasingly complex environments, where digital platforms must support diverse business models, global operations, and evolving customer expectations. Learn how two leading organizations are designing platforms that balance consistency, flexibility, governance, analytics, and innovation while supporting AI-enabled decision making at scale.

Every organization is discovering AI differently. In this fast-paced practitioner showcase, enterprise leaders share real-world lessons from deploying AI in engineering, security, and executive environments. Venkata explores the verification problem in chained AI systems, examining where AI self-critique, cross-model validation, human review, and formal constraint checking succeed—and where they silently fail. Sachdeva discusses securing Kubernetes at scale and protecting the infrastructure powering enterprise AI as organizations face growing attack surfaces and operational risk. Mobley shares an executive perspective on leading organizations through AI-driven transformation and adapting leadership practices for an AI-enabled future. Gain practical insights, lessons learned, and ideas from our practitioners that can be applied immediately in your organization.

Most organizations are introducing AI tools into processes originally designed for human-only workflows. This session explores what happens when enterprise technology teams begin moving from AI-assisted work toward AI-native operating models. Our speakers discuss how AI is changing software development, requirements discovery, engineering roles, agent orchestration, verification, testing, and production readiness. Learn how leading enterprise teams are rethinking engineering workflows, where AI creates measurable acceleration, and what new risks emerge when AI systems begin participating directly in complex development and decision processes.

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 speakers share how Lemonade started with a simple problem: scattered knowledge and inconsistent answers across a fast-growing insurance operation. They solved it. Then they kept going. Today, Lemonade's teams ask nearly 12,000 questions a month through AI-powered knowledge agents. Now, 96% of searches use AI-generated answers. It’s built custom agents that handle multilingual translation, draft CX responses, and automate claims QA workflows. What began as a knowledgebase became the AI backbone of the entire customer operation. In this fireside conversation, our speakers discus the full arc of that transformation. How did a small L&D team build the governance foundation that made AI deployment possible? What changed when they moved from search to AI-drafted answers? How do you keep humans in the loop when automation handles thousands of interactions? Join this honest conversation about the critical role KM plays in AI transformation.

Organizations continue investing heavily in AI, yet many leaders struggle with two fundamental questions: How do we drive meaningful adoption, and how do we measure business value? Gupta shares the ROI and KPI frameworks developed inside IBM Research to govern investment decisions across a portfolio of generative AI, agentic AI, and emerging technology initiatives. Dickson discusses how organizations are successfully adopting AI within marketing operations, highlighting practical ways teams can streamline workflows, improve campaign performance, and make smarter, data-driven decisions. Alzate explores how organizations can move beyond vanity metrics and align AI initiatives with measurable performance outcomes. Ray examines how AI-powered adversarial sandboxes can automatically generate realistic edge cases, business-rule conflicts, and failure scenarios that traditional testing often misses, helping organizations improve resilience before systems reach production. This fast-paced session provides tips and insights from experienced practitioners.

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.

AI is rapidly evolving from assistance to execution. Howard introduces the concept of the agentic economy, where decision making shifts from humans to autonomous AI systems and agents move beyond assistance to execution. Cannon examines why enterprise AI needs a control plane, providing a framework for connecting organizational knowledge, standards, constraints, and documented decisions so AI tools can operate with enterprise context. Spriggs explores how organizations are designing collaborative workflows where humans and AI agents work together to operate complex infrastructure, balancing automation with governance and operational trust. Together, these speakers provide a practical view of how agentic systems are reshaping enterprise operations and decision making.

Moving AI from experimentation to production remains one of the greatest challenges facing organizations today. Soukup discusses what happens when AI must operate in dynamic, high-stakes environments and take action rather than simply generate insight, sharing lessons learned deploying agentic systems powered by video and sensor data. Pandit explores the visibility gap emerging as AI agents move into production and explains how organizations can build observable systems with high-signal telemetry for stochastic decision making. Puvvadi introduces the Safety Shell architectural pattern, a deterministic layer that helps enterprise AI systems fail gracefully rather than catastrophically when models drift or reasoning breaks down. Bohra examines why context is often the hidden reason agents fail and presents a self-curating multi-agent framework designed to continuously gather, verify, and refine context at enterprise scale. Gain practical strategies for building reliable, observable, and trustworthy AI systems in production environments.

The most significant shift in enterprise leadership over the next decade will not be technological—it will be cognitive. As AI systems take on increasing analytical responsibility, the value of human leadership shifts from providing answers to designing the context in which decisions are made well. This speaker explores what it means to lead in a world where human judgment and machine intelligence must operate as a coherent system and provides a practical model for executives navigating this transition.

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.