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Organizations have more dashboards, reports, and AI-generated insights than ever before. Yet many teams still face the same challenge: people understand what happened, but remain uncertain about what to do next. This session introduces a practical framework for understanding why analytics often stops short of influencing decisions. Through the concept of the Decision Stack, we will explore the progression from information to insight, recommendation, and ultimately decision support. Along the way, we'll examine why many analytics systems succeed at delivering information but fail to reduce the cognitive effort required to take action. Drawing from real-world examples across analytics, AI-assisted workflows, and decision-support systems, the session will explore when deterministic analytics is essential, where AI can provide meaningful value, and how trust, accountability, and human judgment shape successful outcomes. We will also discuss practical approaches for evaluating whether a system is merely generating insights or genuinely helping people make better decisions. Attendees will leave with a framework they can immediately apply to dashboards, reporting workflows, and AI-enabled analytics products, helping them identify the often-overlooked layer between insight and action and design systems that people actually use.
Organizations have more dashboards, reports, and AI-generated insights than ever before. Yet many teams still face the same challenge: people understand what happened, but remain uncertain about what to do next. This session introduces a practical framework for understanding why analytics often stops short of influencing decisions. Through the concept of the Decision Stack, we will explore the progression from information to insight, recommendation, and ultimately decision support. Along the way, we'll examine why many analytics systems succeed at delivering information but fail to reduce the cognitive effort required to take action. Drawing from real-world examples across analytics, AI-assisted workflows, and decision-support systems, the session will explore when deterministic analytics is essential, where AI can provide meaningful value, and how trust, accountability, and human judgment shape successful outcomes. We will also discuss practical approaches for evaluating whether a system is merely generating insights or genuinely helping people make better decisions. Attendees will leave with a framework they can immediately apply to dashboards, reporting workflows, and AI-enabled analytics products, helping them identify the often-overlooked layer between insight and action and design systems that people actually use.
Samridhi Vats is a Business Intelligence Engineer at Amazon, where she designs analytics and AI-enabled systems that help transform complex operational data into meaningful, actionable decisions. Her work spans business intelligence, experimentation, statistical process control, decision-support systems, and the practical application of AI in enterprise workflows.She holds a Master's degree in Business Analytics and Information Management from Purdue University and is a Certified Analytics Professional (CAP). Beyond her industry work, Samridhi is an active researcher, speaker, and mentor who has presented at international conferences including INFORMS, Women in Data Science (WiDS), Future World Alliance, and the International Workshop in Management Science.Passionate about bridging the gap between technical innovation and real-world impact, Samridhi enjoys exploring how deterministic analytics, human judgment, and AI can work together to build systems people trust and use. Through her talks, she shares practical frameworks, lessons learned from building real-world products, and approaches that help practitioners move beyond generating insights toward enabling better decisions.
