Our meeting theme is “The Role of Statistics in an AI-augmented World,” reflecting the crossroads that we are at in our discipline. In a seminal paper in 1950, pioneering computer scientist Alan Turing asked the question “Can Machines Think?” Seventy-five years later, generative AI and Large Language Models are revolutionizing how humans are making decisions in real and virtual environments. With this permeation of new machinery and techniques in quantitative data sciences and domain sciences (particularly in biology and medicine), it is important to reflect on how our roles as statisticians have changed. This moment is also underscoring the importance of classical concepts such as sampling, study design, representativeness, selection bias, information bias, causality, generalizability. Who are in the training corpora these models are being built on and who is being left out? Are the performances of AI tools across specific vulnerable sub-groups being assessed? New theories of prediction-powered inference, privacy preserving methods and assumption-lean causal inference are emerging that leverage the beauty of both stochastic and algorithmic modeling. The ENAR 2026 education program and a significant part of the invited program will give us an immersive learning experience of the intersection and infusion of modern machine learning, AI and statistics. However, we will have many sessions that are foundational to the discipline of Biostatistics. So, this ENAR is for everyone, whether you are actively working on AI, want to learn AI or want to learn more about the broader field of Biostatistics.