Artificial Intelligence has rapidly advanced in model sophistication, accuracy, and scale. Yet in many real-world environments, the true measure of intelligence is not the quality of prediction it is the speed and effectiveness of decision-making. This talk challenges the prevailing model-centric view of AI and introduces a decision-centric perspective grounded in real systems that operate continuously, where delays can carry operational, financial, and safety consequences.nnDrawing from experience in large-scale, always-on environments, this session explores the gap between AI outputs and real-world outcomes. Attendees will examine why highly accurate models often fail to deliver value when decisions are delayed, disconnected, or poorly integrated into operational workflows. The talk introduces the concept of decision latency the time between signal, insight, and action and positions it as a critical but often overlooked risk in applied AI systems.nnParticipants will learn how to move from isolated AI capabilities to integrated decision systems that align data, models, and execution. The session will present a practical framework for designing AI-enabled environments that prioritize responsiveness, accountability, and system-level thinking over model performance alone. It will also address the evolving role of humans in AI-driven systems, emphasizing trust, judgment, and responsibility in high-stakes decision contexts.nnRather than focusing on tools or algorithms, this talk provides a real-world lens on how AI must function within complex, interconnected systems. Attendees will leave with a clearer understanding of how to design, evaluate, and lead AI initiatives that deliver measurable outcomes where intelligence is not defined by models, but by decisions made at the right time.