AI-DRIVEN DYNAMIC PRICING A Practical Guide to Profit Optimization

By Dr. Salih Osman

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 systemsThe AI technology stack: machine learning, reinforcement learning, real-time data pipelines, and explainability controlsA blueprint for enterprise pricing architecture, from data foundations to governanceSector-specific playbooks for e-commerce, travel, telecom, and emerging marketsA practical framework for measuring ROI, contribution margin, and CLV impactEthical and regulatory guardrails for responsible AI pricingA maturity roadmap for scaling from pilot to enterprise capability

Whether you're setting strategy in the boardroom, shaping policy in government, or preparing students for the AI-driven economy, AI-Driven Dynamic Pricing gives you the frameworks, formulas, and governance discipline to turn pricing from a static number into a strategic advantage.

• Format: Paperback
• Language: English
• ISBN: B0H74G21RC
• ISBN13: 9798183666373
• Release Date: June 2026
• Publisher: Independently Published
• Length: 86 Pages
• Weight: 0.49 lbs.
• Dimensions: 0.2" x 8.5" x 11.0"