Most pricing decisions are still made on a calendar. Your competitors aren't.
Every quarter, companies leave money on the table - not from bad strategy, but from pricing systems too slow to keep pace with markets that move in hours, not months. AI-Driven Dynamic Pricing: A Practical Guide to Profit Optimization is the executive playbook for closing that gap.
Written by Dr. Salih A. Osman - Principal Data Scientist and Senior Economist at Boeing, with a career spanning Fortune 500 companies, U.S. federal agencies, and international government work, and a Ph.D. in Economics and Applied Statistics from Howard University - this book translates the economics of pricing into a real-time, AI-driven decision discipline.
For business decision-makers: This is not a theoretical treatise. Each chapter pairs rigorous economic frameworks - elasticity, marginal revenue, game theory, auction design - with practical formulas, boardroom dialogues, and industry playbooks for retail, travel, telecom, B2B, and platform markets. You'll learn how to architect a pricing system that links data pipelines, machine learning models, and governance controls to measurable contribution margin, ROI, and customer lifetime value - and how to avoid the common failure modes that turn promising pricing AI into a credibility risk.
For policymakers and regulators: A dedicated chapter addresses the ethical, fairness, and compliance dimensions of algorithmic pricing - including GDPR and CCPA considerations, price discrimination versus personalization, and the governance architecture needed to make AI pricing transparent, auditable, and defensible. This book gives policy audiences a clear, non-partisan framework for understanding what AI pricing systems can and should be held accountable for.
For educators preparing the AI-driven workforce: Structured around classical microeconomic principles reactivated for an AI economy, this book bridges theory and practice in a way that's directly usable in business, economics, and data science curricula. Students and practitioners alike will gain a working vocabulary spanning economics, machine learning, and governance - exactly the cross-disciplinary fluency the next generation of analysts and managers will need.
What you'll find inside:
- The economic theory behind dynamic pricing - elasticity, consumer surplus, game theory, and auction models - reactivated for real-time AI systems
- The AI technology stack: machine learning, reinforcement learning, real-time data pipelines, and explainability controls.
