Prescriptive Analytics with AI
A Practical Guide to Optimization, Decisions, and Intelligent Automation
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Prescriptive Analytics with AI represents a new generation of decision-analytics education. It blends mathematical rigor with conversational modeling and real-time guidance from generative AI systems. Traditionally, optimization and decision modeling have been perceived as highly technical fields that require advanced mathematical fluency. This book removes those barriers by showing how artificial intelligence can act as a true collaborator and by turning complex analytical thinking into an accessible and intuitive process. The book builds on the author's earlier work, Business Analytics with ...
Prescriptive Analytics with AI represents a new generation of decision-analytics education. It blends mathematical rigor with conversational modeling and real-time guidance from generative AI systems. Traditionally, optimization and decision modeling have been perceived as highly technical fields that require advanced mathematical fluency. This book removes those barriers by showing how artificial intelligence can act as a true collaborator and by turning complex analytical thinking into an accessible and intuitive process. The book builds on the author's earlier work, Business Analytics with Management Science Models and Methods (Pearson/FT Press), while fully modernizing the approach for the AI era. Earlier methods relied on spreadsheets, manual formulations, and classical algorithms. Today's analysts can describe a problem conversationally and receive a working model in seconds. With the right prompts, generative AI helps formulate constraints, create Python code, test scenarios, and highlight decision insights. Each chapter follows a consistent learning framework: understand the problem context, define the decision elements, use prompts to generate or refine the model, solve and interpret results, and reflect on the managerial implications. Readers encounter a rich blend of linear, integer, nonlinear, and goal programming, as well as decision and sensitivity analysis, forecasting, and simulation. Each topic is supported by AI-driven examples that accelerate learning while strengthening conceptual understanding. A hallmark of this book is its commitment to making prescriptive analytics, mathematical expressions, and formulas more accessible. For many learners, mathematics has been a barrier to mastering optimization. Through prompt-based instruction, clear conceptual explanations, and AI-generated scaffolding, the book shows how complex models can be understood without sacrificing accuracy or analytical depth. To reinforce learning, each chapter includes fully worked examples, AI insights, review questions, and practice exercises. A companion website at PromptJet.ai/prescriptive offers complete Python source code, prompt templates, additional practice materials, and step-by-step solutions to end-of-chapter exercises. Whether you are a student, instructor, analyst, or manager, this book provides an engaging, intuitive, and practical way to master prescriptive analytics in the age of intelligent systems. It demonstrates that AI's highest value is not to replace human judgment but to amplify it. By combining human reasoning with intelligent automation, prescriptive analytics becomes more transparent, collaborative, and effective.
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