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.

Today, enterprises have more tools to create and share information than ever, leading to significant challenges in managing duplicate content. This session explores how a large supply chain organization implements green information management to support sustainability goals. Presenters from Enterprise Knowledge showcase a hybrid AI framework, combining heuristic and LLM-based approaches to effectively analyze and reduce duplicate content across enterprise repositories at scale. This approach identifies duplicates and near-duplicates and enhances content retrieval, ensuring information accuracy and reducing storage costs. By leveraging AI for content analysis, organizations can streamline their data management processes and contribute to their environmental, social, and governance (ESG) goals by minimizing the carbon footprint associated with redundant data. The presentation demonstrates the environmental benefits of reducing duplicate content, focusing on carbon footprint reduction. It also addresses how this information generates aggregate statistics and resultant carbon footprint, pushing for a cultural shift toward greener information management. Attendees gain insights into the practical implementation of AI-driven content analysis frameworks, the environmental impact of effective data management, and the importance of integrating ESG goals into information management strategies. This approach promotes a cultural shift toward sustainable data practices, significantly improving data management efficiency. Attendees learn how modern AI techniques can transform their enterprise’s data practices and support a sustainable future.

This case study outlines the development of an information discovery application that integrates a knowledge graph with an LLM. The presentation focuses on demonstrating how well-established knowledge management techniques can enhance the effectiveness and user-friendliness of contemporary retrieval-augmented generation (RAG) information retrieval systems. Additionally, it highlights how the application meets the stringent production requirement typical for regulated enterprises, ensuring relevance, compliance and security. It focuses on the innovative techniques to overcome traditional obstacles in information retrieval. These challenges arise primarily because users frequently submit (natural language) queries that are poorly constructed, vague, ambiguous, lack clear and adequate context, and/or contain narrow, domain-specific terminology. By addressing these issues, the applied approach is able to measurably improve the discovery of relevant information while at the same time increasing the ease of use. At its conclusion, an overview is shared of the measured business and user benefits from the increased findability, understanding, trust, and ease of use of the assets of the organization and how their increased reuse and repurpose capabilities affect the daily work of both knowledge workers and specialists.

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.