Enterprise Search & Discovery is part of KMWorld 2024 featuring five co-located events: KMWorld 2024, 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 Enterprise Solutions Showcase Grand Opening reception. Explore the latest products and services from the top companies in the marketplace while enjoying drinks and light bites. Open to all conference attendees, speakers, and sponsors.

We need new frameworks for AI-powered decision making that keep humans in the loop (along with human values, morals, interests, emotions, and sensibilities). Rosenberg discusses an approach toward enabling collective superintelligence that is rooted in hundreds of millions of years of evolution, which is why it so greatly outperforms old-school methods that treat humans as mere datapoints to be aggregated. Humans are not data. Humans are powerful data processors. The most viable pathway to collective superintelligence is to connect people together in real time and allow them to act, react, and interact using AI as the interstitial tissue that empowers us to solve problems together in optimal ways. A lifelong technologist, Rosenberg earned his Ph.D, from Stanford University in the early 1990's, was a professor at California State University in the early 2000's and has been focused on enabling collective superintelligence for the last decade. He shares his insights and ideas for enterprises looking for ways to share knowledge in their organizations.

GenAI retrieval-augmented generation (RAG) uses natural language understanding (NLU) and natural language generation (NLG) capabilities of large language models (LLMs) to securely support conversational search and discovery over enterprise content and data repositories. But GenAI and RAG alone are not enough to ensure the completeness and accuracy of information for many mission-critical enterprise applications. Knowledge graphs (KGs), including enterprise taxonomies and ontologies, can significantly improve the completeness and accuracy of information retrieved and generated by GenAI applications. Taxonomies and ontologies provide GenAI with machine-intelligible context about the domain knowledge and processes of the enterprise. When KGs and GenAI are integrated, taxonomists and ontologists can see and rapidly edit graph structures that explicitly guide RAG decision-making processes. With a simple no-code interface, taxonomists and ontologists are empowered to directly control GenAI dependencies, query refinement, and outcomes, thereby delivering high-quality, high-value business process automation. Using real world applications, our knowledgeable speaker illustrates how using knowledge graphs improves enterprise GenAI.

There is no question that GenAI has reignited interest in KM. Gartner predicts that 100% of GenAI virtual customer assistant and virtual agent assistant projects that lack integration to modern KM systems will fail to meet their CX and operational cost-reduction goals by 2025. As businesses experiment with GenAI, they are realizing that robust KM is foundational to its success. Roy discusses how KM and GenAI can accelerate and ensure mutual success, creating transformational business value at warp speed. He shares stunning success stories from clients. Get insights and ideas for your enterprise.

The world of information is exploding, but finding what you truly need can still feel like searching for a needle in a pile of needles. Probstein explores how search and GenAI are joining forces to revolutionize how we discover information. He delves into the lessons learned from traditional search and how AI is pushing the boundaries. He shares real-world examples and discusses how this powerful synergy is shaping the future of information discovery.

Leading a search team requires more than just technical expertise. It demands effective management, collaboration, and a keen understanding of information governance, management, architecture, and permissions. In this opening session for Enterprise Search & Discovery, longtime search expert Agnes Molnar delves into the essential aspects of search team leadership and provides practical guidance on how to navigate these critical areas. Gain valuable insights and a set of immediate actions to take that can transform your search team's performance, drive better search performance, and obtain greater user satisfaction.

MITRE harnessed the power of ChatGPT in the Azure cloud to develop a range of applications, including chatbots and a feature that allows users to ask questions of their own documents. Rajaram and Lavender explain how they integrated many private MITRE datasets with ChatGPT using advanced vector search retrieval to ground the context for the chat conversation. Finally, they discuss how they implemented their own agentic AI using the GenAI stack.

A few years ago, Microsoft was all about Viva. However, as of last year, we can't read a Microsoft blog without being bombarded with Copilot, which does have some neat features to create content in Word and PowerPoint. From a search point of view, the RAG Copilot offered in Teams is very interesting, particularly in its ability to allow users to ask questions about their documents if they have the proper M365-Copilot license. But not all companies want to (or can) invest in Copilot licenses, or not for all employees. To create an answering engine that can be controlled and tuned, Vanneste extended a custom search application for M365 with GenAI features and building RAG with the (limited, but cheap) search engine of M365 and MS-only building blocks (Azure OpenAI a.o.). How did he do? How do its answers compare to the answers of M365-Copilot? Find out in this session!

Despite the dramatic introduction of AI to deliver information retrieval relevance, enterprise search remains a silent and significant drain on productivity and revenue. Sweeny explores the root causes of this failure and presents a strategic, results-oriented approach to transforming enterprise search into a powerful tool that delivers on the promise of increased productivity and higher morale within the enterprise. Sweeny takes on how to create a road map for success, build a user-focused experience, and drive continuous improvement. Chase turns our attention to revolutionizing enterprise search. She notes that the rise of GenAI has significantly transformed the search landscape and believes that the future of search is multimodal. Limitations encountered today, particularly with PDFs, have caused knowledge managers to change strategies. But search should be tailored for future capabilities.

The best search engine can be helpless if legal rules prevent the return of the most relevant results. Baumgartel provides guidance about how to navigate these challenges and assess pathways to overcome these challenges. He suggests ways to establish common ground among external stakeholders, legal teams, and product executives. The presented results are based on examples he has addressed in ecommerce search, marketplace search, search over user-generated content, and search in areas where the industry is regulated. While potentially a dry subject of legal matters, his examples provide good color and are of general interest.

Today’s business landscape is changing faster than ever before in history. The power of inclusive engagement and collaborative curiosity cannot be overstated. Join leadership strategist and award-winning author Dan Pontefract as he unveils essential techniques to cultivate a culture where every voice is valued and heard. Drawing on global primary research and more than 25 years of experience with leading organizations, he shares actionable insights and transformative strategies that empower KM teams to work better together. Gain expert tips on creating an environment where collaboration prospers, every team member feels genuinely valued, and the opportunity to bloom is open to all.

As organizations integrate AI into their product ecosystems, innovative KM practices are essential to keep information relevant and useful. In the age of GenAI and LLMs, the principle of “garbage in, garbage out” remains true—AI systems are only as effective as the data they process. Chmaj discusses emerging content models, new competencies, advanced authoring techniques, and governance practices that are transforming the KM landscape. It’s vital that companies evolve their technology, resources, and strategies to unlock the full potential of AI-driven KM capabilities. In this dynamic and complex AI landscape, content remains king! Get lots of insights and ideas from our experienced KM leader.

The productivity of knowledge workers is critically important to the growth and profitability of businesses. However, they remain weighted down, spending nearly half their time on mundane tasks, leaving less time for the work that matters most. An organization’s information is the lifeline that provides the insights required to gain collective intelligence. Speakers discuss how organizations can gain a strategic, competitive advantage by leveraging knowledge work automation. They share actionable insights on harnessing the power of automation and AI to eliminate information chaos, improve productivity, and reduce business risk to enable knowledge workers to thrive. 

Generative AI and large language models (LLMs) are reshaping the landscape of corporate knowledge management, heralding a new era where technology speaks our language and enhances our work. Over the past year, retrieval-augmented generation (RAG) has emerged as the premier method for applying LLMs to corporate content, proving its value in real-world applications. However, the journey doesn't end with RAG; in fact, it’s just the beginning. Evernham shows how AI is transforming KM with real-world examples of organizations' use of RAG to unlock the full potential of their knowledge; introduce our new digital colleagues, AI-powered assistants that work alongside knowledge workers, to enhance productivity and decision making; and peeks into the future with a look at multimodal AI and agents poised to perform complex tasks and accelerate innovation.

SharePoint in Microsoft 365 has both a classic and a modern search experience. Microsoft Search in SharePoint is the modern search experience. Both search experiences use the same search index to find results, and you can’t enable or disable either search experience. In this talk, Patrick provides some tips and tricks with working with SharePoint search. He shows how to promote search results in the classic experience and how to create pages that allow you to combine search experiences. He reviews metadata use in search and explains the differences in managed metadata versus regular old choice columns and lookups.

When Merck spun off Organon in 2021, it decided not to provide enterprise search technology to the new company. Over the past 3 years, as Organon matured, it became clear that better search tools were required for multiple business needs. Learn from an experienced enterprise search manager about how Organon evaluated different vendors, selected Coveo, and rolled out multiple successful enterprise search solutions, including GenAI. Discover how Organon monitors and evaluates enterprise search, and get a glimpse into its plans for the future of enterprise search.

GenAI, in little more than a year, has become one of the biggest news topics in IT. GenAI has a discovery component to it, as well as some overlap with “traditional” search, and the use of RAG to fine-tune results is already widely used. What will happen next? Will traditional search applications disappear, to be replaced by AI? Or will there be further integration? This panel discussion, with panelists from The Search Network, a group of search and taxonomy consultants and specialists who represent a wide range of opinion across the industry and academia, provides a view from both the GenAI and the “traditional” search perspectives, outlining the advantages of each.

Vector search algorithms have advanced substantially in recent years, enabling efficient similarity searches in large datasets and powering RAG for GenAI-powered applications. Fried explores how these technologies can be leveraged to improve diagnostic accuracy, personalize patient care plans, streamline clinical workflows, and ultimately contribute to better health outcomes. Although the examples used are all from the healthcare domain, the patterns themselves are applicable horizontally across different domains.

We’ve brought together three very impressive CEOs to present their views on AI and search. SWIRL’s Probstein thinks that every AI company is a search company. He notes that semantic similarity/vector search alone is insufficient for a robust retrieval system. The future of AI companies lies in a multi-pronged approach. Zavrel’s approach at Zeta Alpha relies on his belief that we’re moving from chat to AI agents that have KM superpowers. GenAI systems are evolving to include planning, interplay between multiple LLM-based agents, and using internal APIs of different existing applications. Squirro concentrates on how to apply GenAI to drive better business decisions. Autonomization is the future as GenAI shifts from traditional chat-based interfaces to more autonomous, AI-driven systems.

In today’s landscape, the airwaves resonate with discussions about AI. Embracing emerging technologies has elevated KM efforts. From the early days of portals and expertise location to the collaborative power of wikis and shared spaces, technology has consistently given KM a much-needed boost. This conversation begins with Hubert discussing how experts fit into the AI equation with knowledge creation, capture, and seamless transfer. Given observations from today’s breakneck speed of business, the challenges have only gotten more daunting and the need for tapping into expertise is needed even more. She shares a model for thinking of how to enable AI using the skills of experts. In the dynamic interplay between human expertise and AI, she provides a path to unlock the potential for groundbreaking insights that lead us into uncharted territories of new knowledge. As we navigate this synergistic landscape, both seasoned experts and the KM programs that bolster their efforts must seize the opportunity to capitalize on their collective wisdom. Those who work smarter, fearlessly embracing collaboration with AI, are poised for success. 

Is your enterprise experimenting with generative answering, but facing significant challenges with data cleanliness? Olguin shares best practices and discusses the strategic role KM plays in delivering effective GenAI. Get proven strategies for refining generative outputs and practical insights from enterprise customers including Xero, F5, and Forcepoint. Uncover proven metrics and KPIs to ensure accurate, relevant, and safe generated answers, optimizing your knowledgebases. Enhance productivity, proficiency, and decisionmaking with curated generative answering.

Coleman cuts through the chaos and offers a practical road map for KM teams grappling with the AI revolution. He discusses three key steps to make no-regrets AI investments while safeguarding your hard-earned KM progress: how to separate AI fact from fiction; how to identify AI opportunities that actually complement your existing KM ecosystem; and how to implement a measured, value-driven approach to AI adoption. Get actionable insights to navigate the next phase of KM with confidence.

We have all been wowed by amazing examples of generative AI. But beyond the headlines, things look a little different. While employees are leveraging generative AI to increase productivity on individual and team levels, enterprises are frequently not yet using generative AI in a structured manner across their organizations, mainly due to the lack of private enterprise data. Capturing tacit expert knowledge is where big quantifiable gains can be made. The founders of Sugarwork share a customer case study to highlight the productivity gains the business experienced when they captured and deployed tacit expert knowledge at scale using generative AI.

Every search professional needs data about users’ behavior. Data is fundamental for analyzing user behavior and improving search relevance, both with manual tuning and with machine learning. But until now, collecting user behavior data has been haphazard. Our open-source User Behavior Insights (UBI) system provides a client-side library for instrumenting webpages, a server-side library for collecting data, and analytical tools for understanding it. Critically, it defines a standard schema for behavior data so that the community can contribute additional analytical tools and have it integrated with personalization. With the emergence of even more ways of generating and ranking search results—neural dense search, neural sparse search, model fine-tuning, hybrid search, RAG—choosing the best mix of approaches for your search application becomes even more critical.

Just 2 years into the era of commercial GenAI, it is now trivially simple to send text to an LLM with a prompt to generate a machine-authored response. The quality and accuracy of what you get back is still an open question, however. GenAI has caused content aggregation to rise to the top as the hardest problem to solve. Only by meticulously curating the content from which a GenAI response is created can users have confidence in what the machine reports. And since RAG does not rely on an LLM's training data, getting the right content to the GenAI has to happen on-the-fly every time a user asks a question. Seuss explains how business research using GenAI depends on automated content aggregation and shows solutions available to enterprises to implement such automation for both internal and external sources.

As AI becomes more ubiquitous, enterprise leaders face a challenging dilemma. On the one hand, they need to leverage the power of AI to increase productivity and drive innovation. On the other hand, they must protect their organizations against the risks of open generative AI solutions. Dallhoff details the advancements and risks of generative AI in the workplace, including critical issues like data leakage, hallucination, and legal consequences. He offers insight into how enterprises can safely harness AI by adopting thoughtful approaches, like the ones his company prioritized when building the Pryon platform.

KMWorld magazine is proud to sponsor the 2024 KMWorld awards, KM Promise & KM Reality, which are designed to celebrate the success stories of knowledge management. Information Today, Inc. is excited to introduce and present a new award: KM Community Award recognizes an individual who has made a significant impact in the KM community. The awards will be presented along with Step Two’s Digital Awards, where you get a sneak peek behind the firewall of these organizations. Find out more—kmworld.com/Conference/2024/Awards.aspx

Large organizations often turn to enterprise search to solve the challenges of siloed content management systems and fragmented search experiences, but the outcome depends on the quality and consistency of the associated metadata and taxonomy. Believing that a rising tide lifts all repositories, Dovecot’s Jenkins discusses how to align and enhance metadata and taxonomy ahead of enterprise search. Developing a semantic layer, including GenAI, auto-classification, mapping, and other business logic, can support a processing layer to harmonize and enhance metadata beyond the capabilities of the individual source repositories. Access Innovation’s Hlava provides a case study on search recommendations using taxonomy tags. The McGraw-Hill Access Engineering implementation of search depends on, instead of relevance and co-occurrence, the weighted taxonomy tags applied to the individual pieces of content, the information objects. She outlines the process of taxonomy tagging and the search parameters to achieve amazingly high accuracy and consistency.

The field of information retrieval is evolving at a rapid pace, becoming a cornerstone of the GenAI space and absorbing different, yet familiar, techniques and terminology from previously tangential areas of AI world. But are these all new concepts, and if not, how do we mentally compare them to previous tried-and-true techniques? Pulling from his experience writing (and frequently updating!) the newly released book AI-Powered Search, Grainger provides a survey of the evolving landscape of search and relevance, highlighting how our traditional search toolbox and terminology are expanding in exciting ways, and discusses what’s incoming on the frontier of search and AI.

What’s ahead for enterprise search and discovery? With a plethora of new developments, this panel consults their crystal balls to predict the future, both long and short term. What prognostications will they make that will affect how you do your job and how your job could change? What role will AI play? Come and find out!

This session tackles the crucial role of content preparation for AI in KM. Discover strategies for optimizing content for AI-powered features such as generative search. Hill delves into everything from data structuring to crafting effective prompts equipping you to unlock your knowledgebase's true potential. See AI in action with a compelling case study showcasing a real-world application of generative search and its significant impact. Learn how to transform knowledge boundaries into a springboard for limitless growth.

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? Get inspiration from our practitioners and futurists and be ready for KM in 2025.