More than eight years into the modern era of AI, the industry has moved past the awe of GenAI 1.0. The novelty is gone and in its place is scrutiny. Enterprises are less impressed by demos and more impatient about outcomes. Market watchers are increasingly skeptical about vague narratives; and the commentary has shifted from “look what AI can do” to “show me the money, give me visibility and control.” This dynamic is backstopped by our central premise that the linchpin of AI is data; and specifically data in context. The modern data stack of the 2010s - i.e. cloud-centric, separation of compute and storage, pipelines, dashboards, etc.- now feels trivial. The target has moved to enabling agentic systems that can act, coordinate, and learn across enterprises with a mess of structured and unstructured data, complex workflows, conflicting policies, multiple identities, and the nuanced semantics that live inside business processes. The industry remains excited and at the same time conflicted. To tap an oft-cited baseball analogy - the first inning was academic discovery in and around 2017 - papers explaining transformers and diffusion models. But most people weren’t paying attention. The second inning was the ChatGPT moment and the AI heard ‘round the world, which has brought excitement and plenty of hype.