In the factory-driven Industrial Revolution, we began to view and measure work as a process. Now, in the AI Revolution, we will need to adopt a different model, where we view and measure work as a story. Building on the neuroscience that makes us wired for story patterns, storytelling uses “story” as a communication strategy, while story thinking uses “story” as an operational strategy. The volume, velocity, and variety of data will be connected to processes but also to the organization’s overall narrative intelligence. Lewis discusses the implications of data visualization through the lens of story visualization, which requires understanding human beliefs and commitments, and provides examples for leadership, change, innovation, healthcare, and organizational design.

As organizations race to implement AI initiatives, many discover that their fragmented data infrastructure is holding them back. Learn how unifying enterprise data in real time with CrateDB not only simplifies your architecture but also creates the foundation for successful AI deployment. Pullepu explores practical approaches to breaking down data silos and building a unified data foundation that's ready for today's AI demands and tomorrow's innovations.

No, GenAI didn't "kill" business intelligence (BI). Rather, it's transforming it drastically. Companies that adapt by incorporating AI capabilities will thrive, while those that remain static will struggle to remain relevant. AI is fundamentally changing how we become data-driven as we move from passive dashboards to proactive, conversational insights. A larger audience can access insights democratizing data for nontechnical users. And changing data infrastructure requires that embedding intelligence into workflows, agents, and applications is a critical shift in where analytics happen. The emerging generative capabilities in machine learning and AI will reshape analytics and the way companies move forward in their journey to become data-driven.

Knowledge graphs are key to unlocking the power of retrieval-augmented generation.

AI’s "disillusionment" phase isn’t an AI problem—it’s a data problem, one that knowledge graphs can solve. They guide AI with precision and context, ensuring a clear path toward trustworthy AI. They prevent wrong turns by organizing and linking data in semantically contextual ways and ensure models don’t just process data, but do it accurately, reliably, and contextually with relevance to limit hallucinations. Pal discusses how knowledge graphs help improve data quality, mitigate AI risks, reduce costs, and prepare enterprises to be AI-ready to reap ROAI (Return on AI Investments).

Supercharging customer experiences is one aspect of GenAI that holds real promise.

Gudla looks at two innovative approaches designed to improve grocery search results by enhancing both relevance and discoverability, with a focus on the development and application of a new product relevance classification model, alongside the strategic integration of LLMs to improve discoverability of novel products. By leveraging the precise categorization capabilities of the ESCI model and the contextual understanding provided by LLMs, Instacart could anticipate and meet consumer needs more effectively. This ultimately led to increased engagement and incremental revenue.

Important components in gaining trust in GenAI models and implementations involves compute orchestration and RAG.

As AI projects blossom, organizations must balance compute costs and performance. During this presentation, we introduce and explain the concept of compute orchestration, which allows deployment of any model on any environment, using any hardware accelerator. A unified control plane allows you to orchestrate all your AI workloads, optimizing your compute efficiency automatically based on inference.

The possibilities inherent in introducing GenAI into organizations are exciting but may not address every issue.

GenAI is an exciting and useful technology that is adding value to many enterprise applications. Compelling as it is, GenAI is not always the correct solution for analyzing unstructured data. Sometimes other forms of AI and ML are better-suited to the job. For example, GenAI is great for summarizing the findings of a collection of research documents, but non-generative AI can surface and recommend other documents related to topics of interest. Seuss describes and demonstrates how AI in all its various forms can be combined to analyze unstructured data.

It's tempting to think that GenAI will sell itself, but making the business case for it is still required.

In the modern business landscape, AI and data strategies can no longer operate in isolation. To drive meaningful outcomes, organizations must align these critical components within a unified framework tied to overarching business objectives. Crolene explores the necessity of integrating AI and data strategies, emphasizing the importance of high-quality data, scalable architectures, and robust governance. He outlines three essential steps: recognizing that AI requires the right data to succeed, prioritizing data quality and architecture, and establishing strong governance practices. He provides specific case examples highlighting the importance of a solid foundation and strategy.