AI for People and Business: A Framework for Better Human Experiences and Business Success
- Length: 316 pages
- Edition: 1
- Language: English
- Publisher: O'Reilly Media
- Publication Date: 2019-07-30
- ISBN-10: 1492036579
- ISBN-13: 9781492036579
- Sales Rank: #584319 (See Top 100 Books)
If you’re an executive, manager, or anyone interested in leveraging AI within your organization, this is your guide. You’ll understand exactly what AI is, learn how to identify AI opportunities, and develop and execute a successful AI vision and strategy. Alex Castrounis, business consultant and former IndyCar engineer and race strategist, examines the value of AI and shows you how to develop an AI vision and strategy that benefits both people and business.
AI is exciting, powerful, and game changing–but too many AI initiatives end in failure. With this book, you’ll explore the risks, considerations, trade-offs, and constraints for pursuing an AI initiative. You’ll learn how to create better human experiences and greater business success through winning AI solutions and human-centered products.
- Use the book’s AIPB Framework to conduct end-to-end, goal-driven innovation and value creation with AI
- Define a goal-aligned AI vision and strategy for stakeholders, including businesses, customers, and users
- Leverage AI successfully by focusing on concepts such as scientific innovation and AI readiness and maturity
- Understand the importance of executive leadership for pursuing AI initiatives
Preface The Motivation Behind This Framework and Book Navigating This Book O’Reilly Online Learning How to Contact Us Acknowledgments I. The AI for People and Business Framework 1. Success with AI Racing to Business Success Why Do AI Initiatives Fail? Why Do AI Initiatives Succeed? Harnessing the Power of AI for the Win 2. An Introduction to the AI for People and Business Framework A General Framework for Innovation The AIPB Benefits Pseudocomponent Existing Frameworks and the Missing Pieces of the Puzzle AIPB Benefits Why Focused People and Business Focused Unified and Holistic Focused Explainable Focused Science Focused Summary 3. AIPB Core Components An Agile Analogy Experts Component AIPB Process Categories and Recommended Methods Assessment Component AI Readiness and Maturity Methodology Component Assess Vision Strategy Deliver Optimize The Flipped Classroom Summary 4. AI and Machine Learning: A Nontechnical Overview What Is Data Science, and What Does a Data Scientist Do? Machine Learning Definition and Key Characteristics Ways Machines Learn AI Definition and Concepts AI Types Learning Like Humans AGI, Killer Robots, and the One-Trick Pony The Data Powering AI Big Data Data Structure and Format For AI Applications Data Storage and Sourcing Specific Data Sources Data Readiness and Quality (the “Right” Data) Adequate Data Amount Adequate Data Depth Well-Balanced Data Highly Representative and Unbiased Data Complete Data Clean Data A Note on Cause and Effect Summary 5. Real-World Applications and Opportunities AI Opportunities How Can I Apply AI to Real-World Applications? Real-World Applications and Examples Predictive Analytics Regression Classification Personalization and Recommender Systems Computer Vision Pattern Recognition Clustering and Anomaly Detection Natural Language NLP NLG NLU Time-Series and Sequence-Based Data Search, Information Extraction, Ranking, and Scoring Reinforcement Learning Hybrid, Automation, and Miscellaneous Summary II. Developing an AI Vision 6. The Importance of Why Start with Why Product Leadership and Perspective Leadership and Generating a Shared Vision and Understanding Summary 7. Defining Goals for People and Business Defining Stakeholders and Introducing Their Goals Goals by Stakeholder Goals and the Purpose of AI for Business Deep Actionable Insights Augment Human Intelligence Create New and Innovative Business Models, Products, and Services Capture new markets or expand TAMs Influence new and optimized processes Drive differentiation and competitive advantage Transform business and disrupt industries Goals and the Purpose of AI for People Better health and health-related outcomes Better personal safety and security Better financial performance, savings, and insights Better UX, convenience, and delight Better and easier planning and decisions Better productivity, efficiency, and enjoyment Better learning and entertainment Summary 8. What Makes a Product Great Importance versus Satisfaction The Four Ingredients of a Great Product Products That Just Work Ability to Meet Human Needs, Wants, and Likes Maslow’s Hierarchy of Needs The difference between needs, wants, and likes Human-centered over business-centered products and features Design and Usability Delight and Stickiness Netflix and the Focus on What Matters Most Lean and Agile Product Development Summary 9. AI for Better Human Experiences Experience Defined The Impact of AI on Human Experiences Better health and health-related outcomes Physical health Mental health Better personal safety and security Better financial performance, savings, and insights Better UX, convenience, and delight Better and easier planning and decisions Better productivity, efficiency, and enjoyment Better learning and entertainment Experience Interfaces The Experience Economy Design Thinking Summary 10. An AI Vision Example Spatial–Temporal Sensing and Perception AI-Driven Taste Our AIPB Vision Statement III. Developing an AI Strategy 11. Scientific Innovation for AI Success AI as Science The TCPR Model A TCPR Model Analogy Time and Cost Performance Requirements A Data Dependency Analogy Summary 12. AI Readiness and Maturity AI Readiness Organizational Organizational structure, leadership, and talent Vision and strategy Adoption and alignment Sponsorship and support Technological Infrastructure and technologies Support and maintain Data readiness and quality (the “right” data) Financial Budgeting Competing investments and prioritization Cultural Scientific innovation and disruption Gut-to-data driven Action ready Data democratization AI Maturity Summary 13. AI Key Considerations AI Hype versus Reality Testing Risky Assumptions Assess Technical Feasibility Acquire, Retain, and Train Talent Build Versus Buy Mitigate Liabilities Mitigating Bias and Prioritizing Inclusion Managing Employee Expectations Managing Customer Expectations Quality Assurance Measure Success Stay Current AI in Production Summary 14. An AI Strategy Example Podcast Example Introduction AIPB Strategy Phase Recap Creating An AIPB Solution Strategy Creating an AIPB Prioritized Roadmap Aligned Goals, Initiatives, Themes, and Features IV. Final Thoughts 15. The Impact of AI on Jobs AI, Job Replacement, and the Skills Gap The Skills Gap and New Job Roles The Skills of Tomorrow The Future of Automation, Jobs, and the Economy Summary 16. The Future of AI AI and Executive Leadership What to Expect and Watch For Increased AI Understanding, Adoption, and Proliferation Advancements in Research, Software, and Hardware Research Software Hardware Advancements in Computing Architecture Technology Convergence, Integration, and Speech Dominance Societal Impact AGI, Superintelligence, and the Technological Singularity The AI Effect Summary A. AI and Machine Learning Algorithms Parametric versus Nonparametric Machine Learning How Machine Learning Models Are Learned Biological Neural Networks Overview An Introduction to ANNs An Introduction to Deep Learning Deep Learning Applications Summary B. The AI Process The GABDO Model Goals Identify Goals Identify Opportunities Create Hypothesis Example Acquire Identify Data Acquire Data Prepare Data Example continued Build Explore Select Train, Validate, Test Improve Example continued Deliver Present Insights Take Action Make Decisions Deploy Solutions Example continued Optimize Monitor Analyze Improve Example continued Summary C. AI in Production Production versus Development Environments Local versus Remote Development Production Scalability Learning and Solution Maintenance Bibliography Index
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