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.

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.

Faceted search provided a fresh new approach in the early 2000s, allowing users to see multiple aspects of search results right away and drill down to relevant content. Daniel Tunkelang wrote The Faceted Search in 2009, a book that was key in sharing this information across the profession. Given the exponential increase in information since then, we need new techniques and analysis to improve the user experience of search, and a new version of the book. Rappoport is currently co-authoring an update to the book to revisit the challenges and solutions involved with faceted search.

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!

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.