Enterprise Search & Discovery 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.

In the age of AI, employees don't just want to find the right document, they want an answer and to automate actions. But delivering that promise requires more than just building or buying a tool; it requires managing enterprise search as a product. Hinduja shares how he shifted from managing a search engine to building a three-pillar search ecosystem, encompassing unified discovery, accelerating knowledge comprehension, and Search-as-a-Service. Learn about a framework to diagnose search maturity, prioritize data sources, and turn search from a source of frustration into a competitive advantage.

AI and knowledge graph technology can improve organizational succession planning by capturing institutional memory and exposing relationship patterns that standard methods overlook. Organizations can find appropriate succession prospects while retaining essential institutional knowledge by building a uniform representation of personnel, skills, projects, and collaborations, as well as deploying community detection algorithms. Our comparison of classical Louvain and quantum-enhanced detection methods reveals that advanced analytical approaches can uncover more nuanced candidate matches than traditional techniques, resulting in a strong framework for talent management and knowledge preservation.

Enterprise search is changing as users expect to ask business questions in natural language and receive trusted, contextual answers across structured and unstructured data. This session explores how conversational BI can extend enterprise search by combining Azure AI, Microsoft Fabric, semantic models, governed data products, and retrieval-based patterns. Rakshit focuses on practical architecture, data discovery, metadata, security, and trust, so business users can find the right data, understand the answers they receive, and resolve common challenges.

Agentic AI—AI systems capable of reasoning, planning, and taking action—combined with modern enterprise search is creating new opportunities to improve workflows, reduce administrative burden, and support better decisions. This session explores how healthcare organizations are using agentic AI and retrieval technologies in real-world settings to move beyond simple chatbots and keyword search. Fried discusses practical implementation patterns, lessons learned from production deployments, and key considerations around trust, explainability, and data quality and looks at where the technology is headed next.

As AI applications multiply, every new product, AI-centric or not, needs strong retrieval. A search team built around one application can't keep up. Walvoord and Pugh share how Moody's moved from a one-app search team to a multi-tenant search platform run by a core team. That shift let Moody's serve more consumers across both AI and traditional applications while cutting the effort spent per application. They cover the team structure that made it work, the rising bar for relevancy, where the journey went well, and where it didn't. Attendees come away with a frank account of what worked and what didn't, which is useful for anyone whose search team is under pressure to support a growing set of AI and traditional retrieval workloads.

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.

Making users find the knowledge they need is essential in every organization. If you want to raise it to the next level, it’s also important to make the knowledge find the user. Knowledge findability does not end with a search box. We need to design the search experience and tailor it to meet the users' needs and expectations, along with in-context suggestions and serendipitous discovery features. Molnar introduces what KM systems need to have to provide good findability features, based on her real-world experiences with various international organizations, and what makes them quickly become a disaster story.

Copilot is rapidly becoming the new front door to enterprise knowledge—but most organizations are only scratching the surface of what it can do. This session goes beyond simple prompts and shows how Copilot functions as an advanced retrieval engine, how it interprets enterprise content, and how search professionals can shape the signals that drive high-quality responses. Patrick explores how Copilot uses grounding, ranking, metadata, permissions, and semantic indexing to deliver answers—and how to optimize each layer for accuracy, security, and mission relevance. Gain a practical road map for evolving from “Copilot curiosity” to a mature, AI-driven enterprise search strategy.

Real-world information retrieval is far messier than academic benchmarks suggest. Documents are not created equal, context is rarely explicit, background knowledge is taken for granted, and a single answer often spans multiple sources. The session opens with Popov stress-testing common false assumptions about retrieval. It moves onto how to build GraphRAG systems and takes a deep dive into the production-ready GraphRAG building blocks. Using case study examples, Farner and Cantrell address attempts to unlock knowledge graph modeling and GraphRAG capabilities while constrained by less mature information architecture infrastructure. Learn how to identify domains in which practitioners can exert the most influence, how to shift conversations to their advantage, and, ultimately, how to build a case for increased resourcing for semantic modeling.

As organizations adopt RAG and agentic AI systems, the quality of enterprise search has become a critical factor in determining the accuracy, trustworthiness, and business value of AI-generated outcomes. Many failures attributed to AI are actually rooted in poor retrieval, incomplete context, irrelevant results, and insufficient validation of enterprise knowledge sources. Yilmaz explores how modern enterprise search architectures are evolving beyond traditional keyword search toward semantic retrieval, contextual memory, retrieval validation, and agent-driven knowledge discovery. Drawing from real-world experience, he provides practical approaches for improving retrieval precision, reducing hallucinations, increasing result relevance, and establishing trust in AI-assisted decision making.

Mark Twain said that reports of his death were greatly exaggerated. So did Steve Jobs after another newspaper obituary snafu. Can we say the same about enterprise search? Have we adopted AI technologies to the point that they’ve obliterated everything we’ve learned about search? For decades, enterprise search has been critical to providing needed knowledge to employees and customers. Today, we confront seismic shifts in how people find, retrieve, and interact with information. Can problems with relevance, data silos, and metadata inconsistencies be solved by LLMs, agents, and conversational interfaces? Join us for a stimulating discussion.

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.

Traditional keyword-based search is no longer sufficient in today's complex enterprise environments. Critical business knowledge is scattered across multiple systems, including knowledgebases, file drives, email, document management platforms, and collaboration tools. Conversational and context-aware search technologies are fundamentally transforming enterprise knowledge discovery. Hecht examines proven approaches for unifying previously siloed data sources into one intelligent, discoverable layer that enables users to ask questions in natural language and receive accurate, relevant answers from across the entire organization.

Managing Amazon OpenSearch Service clusters demands deep domain expertise, from index lifecycle management and shard rebalancing to fine-grained access control and vector search tuning for generative AI workloads. These tasks require repetitive REST API calls, hand-crafted JSON configurations, and constant context-switching across documentation. Nawaz and Bhattarcharyya demonstrate how agentic AI transforms OpenSearch operations. Using Kiro CLI with the open-source OpenSearch MCP (Model Context Protocol) Server, teams can perform cluster health triage and index lifecycle policy management, security configuration, and k-NN vector index optimization through natural language commands.

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.

Shapiro has been deep in the practical realities of AI-powered enterprise search at Organon for several years. He shares what's actually happened during that time: new platforms, generative AI in production, next-gen conversational search on the horizon, and a custom-built analytics tool that cuts through the noise to find real improvement opportunities. Part progress report, part lessons learned, this session is for anyone navigating the gap between what vendors promise and what it takes to make search work at scale. It’s an honest account of the launch of a dedicated scientific search application, the layering of generative AI onto platforms, and the imminent rollout of next-gen conversational AI that goes well beyond basic RAG. Plus, he shares how he used Claude to build a custom search analytics tool capable of synthesizing hundreds of thousands of search events across disparate data sources into coherent, actionable insight.

Getting immersed in the opportunities inherent in multiple AI technologies (think RAG, GraphRAG, semantic layers, etc.), new versions of LLMs and the arrival of small language models, and the proliferation of multimodal search is exciting for enterprise search experts. But it’s important not to lose sight of users. Search teams should strive not to get ahead of what users want or can enthusiastically adopt. Given a distrust of generative and agentic AI, coupled with concerns on many fronts, the user experience takes on added significance, creating a make-or-break environment for AI-driven search.

AI is rapidly transforming the healthcare landscape, offering powerful tools to improve patient outcomes, reduce clinical errors, and streamline operations. By leveraging machine learning, natural language processing, and data-driven insights, AI empowers healthcare professionals to make faster, more informed decisions. Innovations such as AI-assisted diagnostics, real-time patient monitoring, and intelligent drug discovery are not only enhancing the precision of care, but also improving access and affordability, especially in underserved regions. Gudi also addresses the ethical and regulatory considerations critical to building trust and ensuring responsible use of AI technologies.

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.