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.

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.

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).

An important component in gaining trust in GenAI models and implementations is retrieval-augmented generation.

RAG is an important tool not only for overcoming the limitations of LLMs but also to optimize GenAI itself. Zeiler explains the architectural and technical components required for production-grade RAG systems, addressing critical considerations such as embedding generation, vector store capabilities, and LLM selection. Learn how input/output modalities influence design decisions and how to navigate edge cases as you scale complexity. Discover practical insights and innovative applications of RAG, along with lessons learned from real-world implementations.

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.