Artificial Intelligence for Managers: Leverage the Power of AI to Transform Organizations & Reshape Your Career

Length: 180 pages
Edition: 1
Language: English
Publisher: BPB Publications
Publication Date: 2020-09-17
ISBN-10: 9389898382
ISBN-13: 9789389898385
Sales Rank: #4844076 (See Top 100 Books)

Understand how to adopt and implement AI in your organization

Key Features

7 Principles of an AI Journey
The TUSCANE Approach to Become Data Ready
The FAB-4 Model to Choose the Right AI Solution
Major AI Techniques & their Applications:
CART & Ensemble Learning
Clustering, Association Rules & Search
Reinforcement Learning
Natural Language Processing
Image Recognition

Description
Most AI initiatives in organizations fail today not because of a lack of good AI solutions, but because of a lack of understanding of AI among its end users, decision makers and investors. Today, organizations need managers who can leverage AI to solve business problems and provide a competitive advantage. This book is designed to enable you to fill that need, and create an edge for your career.

The chapters offer unique managerial frameworks to guide an organization’s AI journey. The first section looks at what AI is; and how you can prepare for it, decide when to use it, and avoid pitfalls on the way. The second section dives into the different AI techniques and shows you where to apply them in business. The final section then prepares you from a strategic AI leadership perspective to lead the future of organizations.

By the end of the book, you will be ready to offer any organization the capability to use AI successfully and responsibly – a need that is fast becoming a necessity.

What will you learn

Understand the major AI techniques & how they are used in business.
Determine which AI technique(s) can solve your business problem.
Decide whether to build or buy an AI solution.
Estimate the financial value of an AI solution or company.
Frame a robust policy to guide the responsible use of AI.

Who this book is for
This book is for Executives, Managers and Students on both Business and Technical teams who would like to use Artificial Intelligence effectively to solve business problems or get an edge in their careers.

Table of Contents
1. Preface
2. Acknowledgement
3. About the Author
4. Section 1: Beginning an AI Journey
5. Section 2: Choosing the Right AI Techniques
6. Section 3: Using AI Successfully & Responsibly
7. Epilogue

About the Authors
Malay A. Upadhyay is a Customer Journey executive, certified in Machine Learning. Over the course of his role heading the function at a N. American AI SaaS firm in Toronto, Malay trained 150+ N. American managers on the basics of AI and its successful adoption, held executive thought leadership sessions for CEOs and CHROs on AI strategy & IT modernization roadmaps, and worked as the primary liaison to realize AI value on unique customer datasets. It was here that he learnt the growing need for greater knowledge and awareness of how to use AI both responsibly and successfully.

Malay was also one of 25 individuals chosen globally to envision the industrial future for the Marzotto Group, Italy, on its 175th anniversary. He holds an MBA, M.Sc., and B.E., with experiences across India, UAE, Italy, and Canada.

A Duke of Edinburgh awardee, Malay has been driving the subject of responsible AI management as an advisor, author, online instructor and member of the European AI Alliance that informed the HLEG on the European Commission’s AI policy. At other times, he remains a Fly that loves to travel and blog with Mrs. Fly.

Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Reviewer
What Some Students Have To Say
Acknowledgement
Preface
Errata
Table of Contents
Section I: Beginning An AI Journey
    1. AI Fundamentals
        Structure
        Objective
        1.1 Understanding AI
        1.2 Growth of AI
        1.3 AI versus ML versus DL versus data science vs. BI
        Conclusion
        Questions
            Answers
    2. 7 Principles of an AI Journey
        Structure
        Objective
        2.1 The big problem
        2.2 Initiating an AI journey
        2.3 Influencing the AI effectiveness
        Conclusion
        Questions
            Answers
    3. Getting Ready to Use AI
        Structure
        Objective
        3.1 Becoming data ready
            3.1.1 The TUSCANE approach
            3.1.2 Data Dictionary
            3.1.3 Data Preprocessing
        3.2 Choosing the right solution
            3.2.1 The FAB-4 model
            3.2.2 Other issues to consider
        Conclusion
        Questions
            Answers
Section II: Choosing the Right AI Techniques
    4. Inside the AI Laboratory
        Structure
        Objective
        4.1 Data and models
        4.2 AI Modeling
        4.3 Techniques overview
        Conclusion
        Questions
            Answers
    5. How AI Predicts Values and Categories
        Structure
        Objective
        5.1 Classification
            5.1.1 K-nearest neighbor
            5.1.2 Support vector machine
            5.1.3 The business value of Classification
        5.2 Regression
            5.2.1 The business value of Regression
        5.3 Decision Trees and Ensemble Learning
            5.3.1 Decision Trees
            5.3.2 Ensemble Learning
            5.3.3 The business value of Decision Trees and Ensemble Learning
        Conclusion
        Questions
            Answers
    6. How AI Learns and Predicts Behaviors and Scenarios
        Structure
        Objective
        6.1 Clustering
            6.1.1 K-means Clustering
            6.1.2 Hierarchical Clustering
            6.1.3 The business value of Clustering
        6.2 Association Rules
            6.2.1 Apriori
            6.2.2 Eclat
            6.2.3 The business value of Association Rules
        6.3 Search Algorithms and Monte Carlo Simulation
            6.3.1 The business value of Search Algorithms
        Conclusion
        Questions
            Answers
    7. How AI Communicates and Learns from Mistakes
        Structure
        Objective
        7.1 Reinforcement Learning
            7.1.1 Upper Confidence Bound
            7.1.2 Thompson Sampling
        7.1.3 The business value of Reinforcement Learning
        7.2 Natural Language Processing
            7.2.1 Bag of Words
            7.2.2 Accuracy versus F1 Score
            7.2.3 The business value of Natural Language Processing
        Conclusion
        Questions
            Answers
    8. How AI Starts to Think Like Humans
        Structure
        Objective
        8.1 The rise of Deep Learning
        8.2 Artificial Neural Networks (ANN)
        8.3 Convolutional Neural Networks (CNN)
        8.4 The business value of Deep Learning
        Conclusion
        Questions
            Answers
Section III: Using AI Successfully and Responsibly
    9. AI Adoption and Valuation
        Structure
        Objective
        9.1 Phases of AI deployment
            9.1.1 Phase 1 – Pre-deployment
            9.1.2 Phase 2 – Deployment
            9.1.3 Phase 3 – Post-deployment
        9.2 AI Investment and Valuation
            9.2.1 Nature of the offering
            9.2.2 Business scalability
            9.2.3 The phase of technology evolution
        Conclusion
        Questions
            Answers
    10. AI Strategy, Policy and Risk Management
        Structure
        Objective
        10.1 Strategy formulation
        10.2 7 Principles of Human-AI Work Policy
        10.3 Risks with AI
            10.3.1 Systems and socioeconomic risk
            10.3.2 Privacy and security risk
            10.3.3 Legal and financial risk
        Conclusion
        Questions
            Answers
Epilogue
References