Upgrade to a Platinum Pass for your choice of two preconference workshops or access to Taxonomy Boot Camp, a co-located event with Enterprise Search & Discovery 2023. 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.

In today's fast-paced and data-driven business environment, disruptive and innovative technologies like generative AI, automation, and machine learning are playing a crucial role in accelerating digital transformation across all industries. They are changing the way organizations innovate, operate, and work. With proof points like ChatGPT, generative AI will soon enough have a significant competitive impact on revenue as well as the bottom line. With the power of AI that can help people broadly synthesize knowledge, then rapidly use it to create results, businesses can automate complex tasks, accelerate decision making, create high-value insights, and unlock capabilities at scale that were previously impossible to obtain. Most industry research agrees with this, including one major study that recently determined that businesses in countries that widely adopt AI are expected to increase their GDP by 26% by 2035. Moreover, the same study predicts that the global economy will benefit by a staggering $15.7 trillion in both revenue and savings by 2030 thanks to the transformative power of AI. As a knowledge worker and KM leader, embracing generative AI technology can deliver a wide range of new possibilities for an organization, helping it ,to stay competitive in an ever-changing marketplace while achieving greater efficiency, innovation, and growth. Hear what CEOs are saying and get our experienced analyst’s insights on delivering the value of generative AI in the enterprise while navigating the challenges.

Taxonomies have evolved enormously over the past couple of decades, as have the methodologies and best practices for curating taxonomies and deploying them within the enterprise. Our speaker and his team help people organize, categorize, and discover enterprise knowledge. Clarke reviews some of the major challenges that Fortune 500 companies have had over recent years, along with the breakthrough moments with co-designed solutions using now out-of-the-box tools that any taxonomy practitioner can benefit from. The use-cases include challenges of complexity, such as the evolution from term-based thesauri to logic-bearing ontologies, and challenges of scale, such as developing high-performance search and APIs for taxonomies with tens of millions of entities. Clarke also looks at breakthroughs in autocategorization resulting from the challenges of transparency and explainability, as well as the impact that large language models are beginning to have on enterprise language models. When Synaptica started building taxonomies and taxonomy software, the only use case for them was to improve metadata quality and consequently search precision and recall. This use case remains a compelling ROI for building enterprise taxonomies, but today, the highly evolved state of taxonomies also supports totally new use cases that provide additional ROI, such as the automation of business processes and decision making, chat-based search, and the generation of new knowledge by inferencing over content-aware knowledge graphs.

Julius Caesar said, “Experience is the teacher of all things.” New talent is important but the value of experience is priceless. How do you capture institutional and skills-based knowledge to ensure the next employee gets off to the standing start? Minimize the costs of time and training when onboarding? Realize the benefits of new talent faster? The founders of Sugarwork share their vision and solutions for an area scantly addressed in the past, along with a success story, highlighting the transformation of a software company as it shifted the geography of its engineering team and used its structured tool to transfer knowledge from the more experienced team to the newly onboarded team. Time was tight, and Sugarwork needed a solution fast. And engineering knowledge is not so easy to capture and pass along. That’s when you pair the experts and the learners via a technology tool that enables structured sharing of institutional knowledge to capture the essential knowledge of the experts during a compressed period of time. Unlike tools such as intranets and collaboration platforms such as Slack, Microsoft Teams, and other stop-gaps, the Sugarwork platform facilitates the transfer of tacit knowledge—knowledge that lives primarily in the minds of individual employees—and is deeply ingrained in people's experiences. This becomes crucial in moments of transition or for companies facing a silver tsunami of retirements, divesting a business unit, or talent disruptions and that risk experiencing profound knowledge and productivity loss when experienced talent walks out the door.

Generative AI has the potential to transform customer and employee experiences—CX and EX. The question is how you can harness that technology for CX and EX transformation. Why is knowledge management foundational to success with generative AI? How do you integrate it with your current AI portfolio? What are the best practices to reap business benefits and mitigate risk? Roy shares his take on these questions using real world examples.

Managers of search solutions have to grapple with a lot of issues—including determining the quality of the search experience of their users. A seasoned search manager himself, Romero explains the evolution of how Deloitte has approached this problem and how his search team determines which problems require their attention. The session ultimately leads to a proposal for an industry standard way to assess the quality of a whole search solution that could enable more transparency and openness across organizations to better understand how "good" their experience is in comparison to others.

Harman presents this scenario: You integrated a Large Language Model into your search system to interpret, answer questions, and summarize search results. Then, disaster strikes: The LLM shows users a hallucinated, non-factual answer, even though your relevance system worked perfectly and you grounded the model with search results. The trust painstakingly built up by your search stack is gone in an instant. Your generative search story doesn’t need to end like this. Harman reviews generative search as an end-to-end system. Techniques covered include indexing and retrieval patterns, UX, prompting, fact-checking systems, LLM usage patterns, and model tuning. Looking at How a Generative AI Answer Engine Transforms Enterprise Search, Welsh sees shortcomings of the current enterprise search solutions—poor search outcomes, siloed knowledge management, duplicate and inconsistent content, and poor visibility into users’ needs. The result? Lost productivity and inferior user experience. A Generative AI-powered Answer Engine takes a fundamentally different approach by integrating several critical technology components including language embeddings, vector and semantic databases, large language models, GenAI, and analytics on the same platform, ensuring instant delivery of accurate, direct, and trusted answers while facilitating a streamlined knowledge curation and maintenance process.

 What happened to the Information Age? We are now overloaded with information. There are more silos than ever. We still spend far more time looking for information than we do reading it. Probstein thinks the move to the cloud and the rise of AI, however, provide real benefits for the large enterprise. Ubiquitous connectivity and the ability of large language models (LLMs) to contextualize almost any text suddenly makes metasearch—sending queries to existing silos, and then re-ranking the results—a game-changer. Mattu turns his attention to customer service. Agents having knowledge available in the moment is essential to deliver world-class customer service for any modern contact center. But do all of them have all the knowledge they need available in one place? Mattu explains how to get the right answers to the right people at the right time.

In today's fast-paced business environment, quick-and-easy access to knowledge is critical for success. Unfortunately, many organizations and people struggle with finding and accessing information within their intranets. This session reviews several different strategies to improve findability in Microsoft 365—from simple tips to help users be more successful with search to more strategic approaches for intranet navigation and structure using home sites and hubs, information delivery with Viva Connections, and improving knowledge management with Viva Topics and Viva engage. Learn practical and actionable tips and tricks that you can implement immediately as you evolve your employee experiences.

Everybody is talking about machine learning, AI, and, most recently, large language models. Who knows what may take over as the current trend going forward? Klatt steps back to give you some insight about what enterprise search is and which use cases it is intended to solve. He then describes where the current hype can help and where it can't. Gain perspective on how to make such initiatives become successful and sustainable.

Stop by the showcase after a full day of stimulating sessions to mix and mingle with other conference attendees, speakers, and our conference sponsors.

Our organizations have had a roller coaster ride with digital transformation over the last few years and most have embraced online work platforms. An organization's culture is created in the conversations between its members. But how do our enterprises encourage their people to interact, collaborate, and connect as they used to in a corporate building? How do they support learning, encourage deeper conversations and human relationships, and embrace and foster innovation? Speakers address these issues, discuss reclaiming conversations, share insights and experiences, and provide lots of tips and ideas for supporting KM joy, enhancing the flow of information and knowledge, and building stronger, more collaborative teams within the enterprise.

A long time KM practitioner and Deloitte’s Knowledge Capital Practice Lead, Eyal discusses how organizations can move beyond the tactical version of KM as a storage repository and view knowledge as source of growth for the organi- zation and knowledge as its most important asset. He shares how connecting knowledge within the flow of work can drive better outcomes and benefits. Gain insights and ideas into how to get into the mindset to help you capitalize your organization’s knowledge.

Generative AI has real practical implications in search applications. This presentation discusses and demonstrates how generative AI, large language models (LLMs), and vector search can be used to create more natural and conversational search experiences, generate more comprehensive and informative search results, and personalize search results to individual needs and interests.

With the advent of advanced conversational AI, the cycle of learning through dialogue is digitalized. This AI not only serves as an interpreter, bridging the gap between novice queries and expert content, but also ensures continuous accessibility of vital institutional knowledge. Moreover, it offers organizations the desired control and oversight, providing insights about conversation effectiveness. This technology thus opens up opportunities for unlocking and safeguarding knowledge within organizations.

Vectors and generative search have upended the past 40 years of search by letting us make non-obvious connections between a user’s need and the documents we can surface by using the information embedded in long-form text, images, and language models. If you’ve been skating by with classic metrics like LGTM (Looks Good To Me), you are about to discover that these powerful new techniques also bring new risks to your search quality and therefore to your business. The evidence is building that search teams who embrace these new techniques are seeing both more “Remarkably Right” results but also “Remarkably Wrong” results. Middle-of-the-road “good enough” results are much less common. Pugh shares some case studies using vector search to demonstrate the pitfalls and lays out what metrics you need to be paying attention to. Raytion believes that bringing context to large language models (LLMs), leveraging the latest innovations in LLM-based generative AI for your business, requires that such solutions know what your organization knows. This talk gives insight into how such context can be provided, no matter in what technical systems your knowledge investments are stored—while assuring enterprise-grade requirements such as freshness, scalability, and security.

For the past 10 years, Majer’s team at Merck has been using Fast and SharePoint Online Search as their main search engine. As time moved on and technologies changed, the team decided to move into using Azure Cognitive Search to better support the users and their growing need for more advanced search technologies. Majer describes the journey, the advantages and disadvantages of this decision, and how they tackled it in a big enterprise environment.

Two acronyms, GPT and LLM, sprung to prominence in the past year. Pundits decided that they either spelled the end of search or presaged a new and wonderful era for search. The focus of interest was more on their effect on open web searching rather than on enterprise search. This panel discussion examines how GPT, LLMs, NLP, AI, and related technologies affect organizations, sorting out the wheat from the chaff.

Although many companies have Office 365 and use MS Teams, people struggle to find information. Within the MS Stack, there are many search technologies and many search experiences. One solution is to build an enterprise search app that offers a "Google-like" search experience. Using a large engineering company as his case study, Vanneste describes researching the different options, weighing the pros and cons, and selecting technology components. He concentrates on the technology choice, the search experience in Teams; the lessons learned regarding Graph Search API, MS Search, and Azure cognitive services; and takes a look toward the future. What's next for the search app? How can Azure OpenAI impact/improve this application? The story is not done yet!

Switching from one search platform to another is a major decision. This session covers two case studies in the migration process. At EY, the company re-platformed Discover, its global knowledge platform, moving from SharePoint to Elasticsearch. Simek and Michels discuss the pitfalls and lessons learned, as well as how EY tackled relevancy tuning in the new environment. Arce describes the involvement of Pureinsights in the context of work done for an existing customer, for whom it did a large search engine migration project, moving from Elasticsearch to OpenSearch. He presents a comparison of the different approaches that can be taken to store extremely large numbers of documents and summarizes the benefits and detriments of each. With all the buzz around ChatGPT and AI tools, it's worth revisiting the state-of-the-art of semantic search. Instead of previous generation NLP tools, the latest AI tools driven by large language models (LLMs) promise to make natural language query interpretation and results generation more powerful than ever—AI-driven semantic search.

AI and the internet are transforming our understanding of how the future happens, enabling us to acknowledge the chaotic unknowability of our everyday world. Back when we humans were the only ones writing programs, data looked like the oil fueling those programs. But now that machines are programming themselves, data looks more like the engine than its fuel. This is changing how we think about the world from which data arises, and that data is now shaping as never before. We’ve accepted that the intelligence of machine intelligence resides in its data, not just its algorithms—particularly in the countless, complex, contingent, and multidimensional interrelationships of data. But where does the intelligence of data come from? It comes from the world that the data reflects. That's why machine learning models can be so complex, we can't always understand them. The world is the ultimate black box. Weinberger looks at the implications of this for people who work with data, those who share knowledge and insights inside and outside the enterprise, and those looking for ways AI can assist their organizations in future success.

How do companies deliver AI capabilities across their organization? How can an organization build and leverage AI tools without having to develop multiple intelligent technologies for different applications? What’s the best way for organizations to build and evolve the large datasets needed to drive the most powerful, emerging AI tools? Centralizing AI capabilities into an enterprise data hub with reporting tools and advanced integration capabilities allows companies to leverage their investments more fully, bringing new and evolving AI capabilities into play quickly by leveraging a common data hub to build rich machine learning models. For KM, this means organizations can build powerful new capabilities. Hear how organizations are doing this today!

Hoeffel delves into the realm of KM and explores how generative AI can revolutionize the way organizations capture, organize, and utilize information. Discover how LLMs and AI can enhance knowledge discovery, automate content generation, and improve decision-making processes. Through compelling demos and practical guidance, gain insights into leveraging generative AI to unleash the full potential of search and KM efforts. Discover how this powerful combination can drive innovation and propel your business forward.

Discovery is a key factor for knowledge management, and helps people to find the right information at the right time. Knowledge graphs enable simple, intuitive discovery, but can be time-consuming to create and manage. Today, we are at the cusp of a new era in improving content discovery with automation. Traditional automation tools—such as auto-tagging, auto-classification, and inference-based rules—can now be used together with LLMs to make knowledge graphs easier to create and more powerful in powering content discovery. Get insights into how these new approaches can provide information of higher quality, accuracy and reliability, powering better content discovery and ultimately providing more effective use of organizational knowledge.

For all the power and sizzle of ChatGPT, the software in its current commercial form isn't an appropriate generative AI solution for enterprise search. Why? Because an enterprise's knowledge repository is not Wikipedia and the web (the training set for ChatGPT). Rather, enterprise knowledge is derived from proprietary internal research, customers, credible industry sources, and subject matter experts steeped in the markets, products, and technologies a company cares about. Seuss reveals three keys to how large language models (LLMs) can be fine-tuned for enterprise KM applications, such as conducting competitive intelligence and finding insights into customer behavior. How do they aid in answering product marketing questions, monitoring competitor strategies, and influencing decision making? What competitive edge can these AI-based tools provide to you?

Digital search is fundamental to the digital world as it exists today—the vital connection between users and data. Despite being a core component of the user’s experience, it often lacks expressiveness, providing answers that fail to align with the user’s true desires. With semantic technology, the gap between human intention and machine understanding can be eliminated, not only providing relevant results but also allowing the user to ask more complex questions that go beyond the scope of conventional search. Crocker describes the creation of an application that runs on a knowledge graph and applies semantic reasoning with rules and an ontology. Having ingested all the data available on the BBC GoodFood website, it parallels the real experience provided by semantic search.

GenAI is the next make-or-break moment for KM leaders. However, GenAI alone won't reduce the significant challenges associated with content governance. Join this session to learn how search augments AI’s capabilities. Uncover 11 cutting-edge machine learning models (including generative answering) used by over 600 leading enterprises to deliver world-class digital experiences every day.

KMWorld magazine is proud to sponsor the 2023 KM Awards, KM Promise and 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.

There is one crucial feature on every website that is not evolving quickly enough to keep up—the search bar, which has trained end users to spend precious time sifting through results without real relevance, creating bad experiences. Traditional keyword searches have limitations that LLM-powered solutions are uniquely adept at addressing. The combination of traditional keyword search and LLM-powered search, now called hybrid search, is the key enabling capability of generative AI, as generative results are more accurate when working from a strong hybrid search core. This enables a shift from search engines to action engines, allowing end users to have full relevance and accuracy to answers with “grounded generation.” Awadallah discusses how hybrid search is making generative AI a reality and how product builders can effectively leverage AI with a safe entry point.

To solve the pain points of information retrieval caused by the complexity and vastness of data at PETRONAS, ENIGMA was launched in 2022. This analytical project is equipped with industry-leading technology stacks, such as Azure Cognitive Services, OpenAI, and Neo4J knowledge graph, and is combined with the power of internal and external data for predictive and prescriptive analytics. ENIGMA is embedded with EKIH, an in-house cognitive search solution that derives contextual information from multiple databases. It also utilizes advanced ML and AI-enabled capabilities to connect and crawl across disparate data sources, processing different types of complex unstructured data. It incorporates semantics to enhance meaning to the connected data by using ontology, taxonomy, and knowledge graphs, coupled with natural language understanding. This results in users being able to pose human-like queries and retrieve relevant search results.

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? Come and find out!

The age of generative AI is changing the nature of work, and nobody is more impacted than the knowledge manager. While this transformation may seem overwhelming at first, GenAI offers knowledge managers an opportunity to play a mission-critical role in the automated future. Former knowledge manager and AI leader Sedarius Tekara Perrotta explains why knowledge management is evolving into a critical and strategic role over the next 2–3 years and provides specific advice on how to position KM expertise to take advantage of one of the biggest business trends in decades.

Join members of the KMWorld community as they provide insights, inspiration, key ideas, and innovations shared at this year’s conferences as well as what our panelists are seeing within the rapidly changing field of KM.