Ruchi Mangharamani
Georgia State University
Atlanta, GA, 30302, United States
Dr. Shruti Saxena
Assistant Professor
Savitribai Phule Pune University
Pune, India
Abstract
AI-powered decision intelligence represents a paradigm shift in how organizations analyze data and make informed decisions. By integrating autonomous analytics into business operations, healthcare diagnostics, and public policy formulation, this technology is enhancing the speed and accuracy of decision-making processes. In the business sector, AI-driven systems streamline operations by automating complex data analyses, identifying trends, and providing actionable insights that support strategic planning and risk management. In healthcare, the use of AI enables early detection of diseases, personalized treatment plans, and improved patient outcomes through predictive analytics and real-time data monitoring. Similarly, public policy benefits from these advancements by employing data-driven approaches to evaluate social programs, forecast economic trends, and design more effective governance strategies. The convergence of AI and autonomous analytics not only mitigates human error but also uncovers hidden patterns within vast datasets, thereby optimizing resource allocation and fostering innovation. This integration challenges traditional decision-making frameworks, encouraging a shift towards systems that learn and adapt autonomously. Despite concerns about data privacy and algorithmic bias, the potential benefits of AI-powered decision intelligence are profound, promising significant improvements in efficiency and service delivery across various sectors. As organizations continue to evolve in a data-rich environment, leveraging autonomous analytics will be crucial for maintaining competitive advantage and ensuring responsive governance in an increasingly complex world.
Keywords
AI, Autonomous Analytics, Decision Intelligence, Business, Healthcare, Public Policy, Data-Driven Decisions, Predictive Analytics, Innovation
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