
The Rise of Artificial Intelligence
Description
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Few terms have captured our imagination in recent times like "Artificial Intelligence," and it now seems that everyone "know" about AI; that everyone has an opinion. And yet, few people actually understand how the technology can be harnessed to generate commercial outcomes.
Written for the modern-day business manager, this book examines Artificial Intelligence from the perspective of decision-making, because the quality of the decisions we make defines the quality of the future we create for ourselves and our organizations. In the same way that calculators have improved our ability to make better decisions-followed by spreadsheets, reporting tools, and countless other software applications-Artificial Intelligence is fast becoming the latest "calculator" to assist us, and one that happens to be particularly well-suited for the speed, noise, and complexity of the modern world ...
Internationally renowned new technologies expert, Dr Zbigniew Michalewicz has published over 200 articles and 15 books on the subjects of business intelligence, predictive data mining, and optimisation. Leonardo Arantes is a dynamic sales and marketing professional, with a reputation for using creative problem-solving to take products to market and deliver on customer needs. Matt Michalewicz has more than 20 years of experience in starting and running high-growth tech companies, especially in the areas of machine learning, predictive analytics, and decision optimisation.
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Content
- Intro
- Copyright
- Dedication
- Contents
- Preface: What This Book is About and How to Read It
- PART I: Artificial Intelligence as Applied to Decision Making
- Chapter 1. What is Artificial Intelligence?
- 1.1 Artificial Intelligence at a Glance
- 1.2 Branches of Artificial Intelligence
- 1.3 Artificial Intelligence "Algorithms"
- 1.4 Why Now? Why Important?
- Chapter 2. Complex Business Problems
- 2.1 Decision Making for Complex Business Problems
- 2.2 The Problem-to-Decision Pyramid
- 2.3 AI for Bridging the Gap between Past & Future
- Chapter 3. An Extended Example: Promotional Planning and Pricing
- 3.1 The Problem: Promotional Planning in FMCG
- 3.2 Applying the Problem-to-Decision Pyramid
- 3.3 Competitor Aspects of Promotional Planning
- PART II: Prediction, Optimization, and Learning
- Overview
- Chapter 4. Data
- 4.1 Modeling Considerations
- 4.2 Data Preparation
- 4.3 Less Data, More Complexity
- Chapter 5. Prediction
- 5.1 Classical Prediction Methods
- 5.2 Random Forests
- 5.3 Genetic Programming
- 5.4 Fuzzy Systems
- 5.5 Artificial Neural Networks
- 5.6 Ensemble Models
- 5.7 Evaluation of Models
- 5.8 Closing Remarks
- Chapter 6. Optimization
- 6.1 Two Optimization Puzzles
- 6.2 Promotional Planning Optimization
- 6.3 Local Optimization Methods
- 6.4 Stochastic Hill Climber
- 6.5 Simulated Annealing
- 6.6 Tabu Search
- 6.7 Evolutionary Algorithms
- 6.8 Constraint Handling
- 6.9 Additional Aspects of Optimization
- 6.10 Global Optimization
- Chapter 7. Learning
- 7.1 Learning
- 7.2 Prediction and Learning
- 7.3 Optimization and Learning
- 7.4 Adaptability
- PART III: Application Areas for Revenue and Margin Growth
- Overview
- Chapter 8. Sales
- 8.1 Call Cycle Optimization for Reduced Customer Churn and Increased Share of Wallet
- 8.2 Quoting Optimization for Improved Basket Size and Margin
- 8.3 Optimizing Digital Sales for Improved Basket Size and Margin
- 8.4 Sales Structure and Territory Optimization for Improved Market Coverage and Organizational Efficiency
- 8.5 Customer Segmentation and Sales Channel Optimization for Improved Growth and Reduced Cost to Serve
- Chapter 9. Marketing
- 9.1 Dynamic Pricing for Improved Profitability and Share of Wallet
- 9.2 Tiered-Pricing Optimization and Automated Compliance Monitoring for Improved Margin
- 9.3 Marketing Spend Optimization for Improved Return on Investment
- 9.4 Promotional Planning and Trade Spend Optimization
- Chapter 10. Supply Chain
- 10.1 Demand Forecasting and Inventory Optimization
- 10.2 Scheduling Optimization for Improved Asset Utilization, Throughput, and DIFOT
- 10.3 Logistics and Distribution Optimization
- PART IV: Implementing AI in Your Organization
- Overview
- Chapter 11. The Business Case for AI
- 11.1 Selecting the Right Problem
- 11.2 Starting Large or Small
- 11.3 Executive Sponsorship
- 11.4 Return on Investment and Payback
- 11.5 Technology Partner Alignment
- Chapter 12. Getting the Foundations Right
- 12.1 Data Quality
- 12.2 Digitalization
- 12.3 Change Management
- 12.4 Requirements Validation
- 12.5 Closing Thoughts
- Index
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