In the last 2 years, we have witnessed explosive growth in Generative AI adoption, driven by the early success of ChatGPT. Large language models (LLMs) suddenly became a topic of public interest, nearly 5 years after the introduction of the transformer architecture, the bedrock of modern language models. This wave has compelled enterprises to rethink their business strategy as many aim to integrate AI into their operations and generate the most value with it.

The early use cases of LLMs involved chat interfaces, though these applications were prone to hallucination. To address this problem, 2 key solutions became popular - finetuning and retrieval augmented generation (RAG). RAG was more handy as it was not only cost-effective but also allowed LLMs to augment their knowledge using additional context data at inference time, making it easy to fact-check answers and evaluate key metrics like context relevance, faithfulness, and generation accuracy. This enabled Talk to Your Document use cases and supporting both internal search-discovery needs and customer-facing virtual assistant interfaces.

The potential of generative AI goes beyond talking to documents but can also be a lever for business agility and data-driven transformation. Most enterprises want to do more with generative AI, to integrate it across all business processes, and to interact seamlessly with their data no matter what or where it is. In this session, we will explore how generative AI drives these opportunities. Starting with the current state of generative analytics and conversational business intelligence, we will "delve" into practical ideas and emerging trends such as LLM-enriched semantic layers, intelligent metric generation, RAG over semantic layers, RAG over databases, function calling, and agentic workflows.

Participants will discover how generative AI enables analysts to deliver data requests faster, speeding up time-to-insights for decision makers. They will also learn how advances with RAG and Agents improve upon traditional NLQ and search-based BI tools, fostering interactive experiences for both analysts and non-technical users. The session will advance into novel ideas for going beyond interactive analytics to seamless knowledge discovery at scale.

This session is designed for participants across all levels, technical or non-technical, and will be especially beneficial for enterprise leaders and non-technical stakeholders. Attendees will gain understanding of the state-of-the-art in applied generative AI and insights into the future of enterprise knowledge discovery.