Artificial intelligence (AI) is rapidly reshaping the business landscape, offering unprecedented opportunities to accelerate growth, optimize operations, and unlock new forms of value. Yet as organizations race to adopt AI, many struggle to translate experimentation into measurable return of investment (ROI), sustainable outcomes, and meaningful impact. This round table brings together leaders, practitioners, and innovators to explore how AI can advance business performance in ways that are both strategic and responsible. Participants will examine the evolving relationship between AI capabilities and business objectives, discussing how organizations can move beyond hype to build AI initiatives grounded in clear value propositions. The conversation will highlight practical approaches for aligning AI investments with operational efficiency, customer experience, workforce enablement, and long‑term competitiveness. The speakers will explore the broader implications of AI adoption, including governance, ethical considerations, change management, and the cultural shifts required to ensure that AI augments, rather than disrupts, human-centric potential. By sharing real‑world examples, lessons learned, and emerging best practices, this session aims to equip attendees with actionable insights for designing AI strategies that deliver measurable returns while driving positive organizational and societal impact. The speaker will illustrate digital health to show its value, adoption, and challenges. 

During Fall 2026, from September through December 2026, the series will focus on Trustworthy AI and the statistical, methodological, and governance foundations needed to develop, evaluate, and deploy AI systems responsibly and effectively. As AI becomes increasingly embedded in scientific research, business operations, public services, and societal decision-making, establishing confidence in the reliability, fairness, transparency, and accountability of these systems is essential. The series will examine approaches to measuring and mitigating bias, quantifying uncertainty and risk, evaluating robustness under changing conditions, and developing interpretable models and transparent evaluation frameworks that support informed decision-making. Emphasis will be placed on reproducibility, responsible data practices, privacy and security considerations, human oversight, and lifecycle monitoring to ensure that AI systems continue to perform as intended after deployment. By grounding discussions of AI development and governance in sound statistical reasoning and rigorous empirical evaluation, the series aims to promote AI systems that are not only accurate and innovative, but also trustworthy, equitable, and aligned with societal values.