AI Ladder

Accelerate Your Journey to AI
O'Reilly (Verlag)
  • erschienen am 30. April 2020
  • |
  • 226 Seiten
E-Book | ePUB mit Adobe-DRM | Systemvoraussetzungen
978-1-4920-7338-3 (ISBN)
AI may be the greatest opportunity of our time, with the potential to add nearly $16 trillion to the global economy over the next decade. But so far, adoption has been much slower than anticipated, or so headlines may lead you to believe. With this practical guide, business leaders will discover where they are in their AI journey and learn the steps necessary to successfully scale AI throughout their organization.Authors Rob Thomas and Paul Zikopoulos from IBM introduce C-suite executives and business professionals to the AI Laddera unified, prescriptive approach to help them understand and accelerate the AI journey. Complete with real-world examples and real-life experiences, this book explores AI drivers, value, and opportunity, as well as the adoption challenges organizations face. Understand why you cant have AI without an information architecture (IA)Appreciate how AI is as much a cultural change as it is a technological oneCollect data and make it simple and accessible, regardless of where it livesOrganize data to create a business-ready analytics foundationAnalyze data, and build and scale AI with trust and transparencyInfuse AI throughout your entire business and create intelligent workflows
  • Englisch
  • Sebastopol
  • |
  • USA
  • 8,35 MB
978-1-4920-7338-3 (9781492073383)
weitere Ausgaben werden ermittelt
  • Intro
  • Preface
  • Who This Book Is For
  • O'Reilly Online Learning
  • How to Contact Us
  • Acknowledgments from the Authors
  • Acknowledgments from Paul Zikopoulos
  • 1. What in the AI? How Did We Get Here?
  • Collecting Data in Real Time, but Understanding It in Stale Time
  • The Modality of Everything and the Data Collection Curve
  • Even Steeper: The Future of the Data Collection Curve
  • Where We Are Now-Haystacks, Needles, and More Data
  • How to Displace Today's Disruptors
  • Let's Get Ready for a Climb!
  • 2. The Journey to AI
  • What Is Artificial Intelligence, Anyway?
  • Types of AI
  • Data
  • Models
  • Where AI Has Been
  • What Does AI Mean for Business?
  • The Journey to AI
  • All Radically New Technologies Face Resistance
  • Where Are We Now? And Where Are We Going?
  • Moving Forward
  • 3. How to Overcome AI Failures and Challenges
  • AI's Emergence in Business Today
  • Data
  • Computing Power
  • Investment
  • Early Examples of AI Success
  • Example: Vodafone's TOBi Transforms the Customer Experience
  • Example: How a French Bank Built on Its Strength of Quality Customer Service
  • Early AI Failures
  • AI Challenges: Data, Talent, Trust
  • AI Challenge: Data
  • AI Challenge: Talent
  • High demand, low supply for potential employees
  • Culture inhibitors
  • Siloed people and departments
  • AI Challenge: Trust
  • Fairness
  • Explainability
  • Robustness
  • Transparency and accountability
  • Value alignment
  • Overcoming Challenges with Advanced Research and Products
  • Overcoming Challenges with the Right Partner
  • 4. The AI Ladder: A Path to Organizational Transformation
  • Suitability of AI
  • Determining the Right Business Problems to Solve with AI
  • Building a Data Team
  • Putting the Budget in Place
  • Developing an Approach
  • There Is No AI Without IA
  • The AI Ladder
  • Collect
  • Organize
  • Analyze
  • Infuse
  • Simplify, Automate, and Transform
  • 5. Modernize Your Information Architecture
  • A Modern Infrastructure for AI
  • All Parts Are Visible
  • Legacy Systems Are Made Accessible or Eliminated
  • Example: Network Rail uses AI to modernize its infrastructure
  • All Parts of the System Are Continuously Monitored
  • Inefficiencies Are Identified and Removed
  • New Architectures for IT
  • Data: The Fuel
  • Cloud: The Means
  • To the Cloud, and Beyond: Cloud as Capability
  • Fuel for the Fire
  • From Databases to Data Warehouses, Data Marts, and Data Lakes
  • Example: Wireless Carrier Architects a Solution Using Both a Data Lake and a Data Warehouse
  • Data Virtualization
  • Unifying Access to Data Through Big SQL
  • Object Storage as the Preferred Fabric
  • Open Data Stores and Open Data Formats
  • Next-Generation Databases
  • The Power of an AI Database
  • Streaming Data
  • Get the Right Tools
  • The Importance of Open Source Technologies
  • Community Thinking and Culture
  • High Code and Component Quality
  • Real Examples of Modernizing IT Infrastructure
  • Example: Siemens Looks to the Cloud to Unify Its Data Processes
  • Example: Fannie Mae Transforms with a Governed and Centralized Data Environment
  • Don't Neglect the Foundation!
  • 6. Collect Your Data
  • What Needs to Happen on the Collect Rung
  • Example: EMC Develops a Data Collection Strategy
  • Start with a Data Census: Learn What's Out There
  • Understand Data in a Business Context, and Partner with SMEs
  • Getting Beyond Transactional Data
  • The Challenges of Collecting New Sources of High-Volume Unstructured Data
  • Organizational Aspects of Data Access
  • Example: Procter & Gamble Avoid Data Silos Using a Central Data Warehouse
  • Example: eBay Eliminates Data Silos by Publishing Business Processes as APIs
  • Ownership, Stewardship, Regulatory Compliance, and Discipline
  • Example: Owens-Illinois
  • Collecting Data: You Can Win This Battle!
  • 7. Organize Your Data
  • Poor Data Leads to Poor AI
  • Regulation Demands Quality Data
  • What Needs to Happen on the Organize Rung
  • Cleaning Data
  • Documenting and Cataloging Data
  • Understanding Data: The "Seller" Gong Show
  • Metadata for Models
  • Maintaining the Catalog
  • Governing Data
  • Enterprise Performance Management
  • Example: ANZ Banking Group Embeds Sound Data Management and Governance Policies
  • DataOps
  • Now That Your Data Is Trustworthy, on to Analysis!
  • 8. Analyze Your Data
  • Why Organizations Need an End-to-End AI Lifecycle
  • Build
  • Example: Using Machine Learning, an Insurer Cuts Costs and Boosts Productivity
  • Run
  • Manage
  • Aligning Model Output with Business Metrics
  • Learning, Iterating, Learning
  • Example: Risk Management Company Gets Creative to Offset the Expense of Training Models
  • Automating the AI Lifecycle
  • AutoAI
  • Example: Wunderman Thompson delivers new prospective customers through AutoAI
  • Example: Bank uses humans, machine learning, and deep learning to measure model risk
  • NeuNetS
  • Incorporating AI into DevOps Processes
  • Emphasizing Trust and Transparency
  • Example: By Shining Light on Data Attributes, a Bank's AI System Demonstrates Integrity, Fairness, Explainability, and Resiliency
  • Example: Avoiding the "Black Box" Dilemma
  • Avoiding the Piecemeal Approach
  • Example: SaaS Company Gleans New Insights by Applying AI to Historical Data
  • Ready to Infuse...
  • 9. Infuse AI Throughout the Business
  • Customer Service
  • Financial Operations
  • Risk and Compliance
  • IT Operations
  • Business Operations
  • Themes Across All Intelligent Workflows
  • Building the Next-Generation C-Suite
  • 10. Tips and Best Practices on How to Get Started
  • Manage Organization-Wide Change
  • Change in Daily Tasks
  • Change in Overall Business Processes
  • Example: Pharmacy uses automation to modernize laborious and error-prone processes
  • Change in Thinking About Data
  • Make Data a Team Sport (And Some Cool History About Car Racing)
  • Subject Matter Experts
  • Data Scientists
  • Data Operations (DataOps) Specialists
  • Data Engineers
  • Training for Career Development
  • Embrace AI Centers of Excellence
  • Example: Honda Sets Up Knowledge Hubs to Build Minimum Viable Products, Organize Training, Share Data
  • Build Ethics Into Your Process
  • Privacy
  • Safety
  • Fairness
  • Building Trust in AI
  • Choose Projects Selectively, and Embrace Failure
  • Example: Insurer Tracks Metrics to Communicate Success of Its Model
  • Beware of False Negatives
  • 11. The Future of AI
  • AI Themes to Take Us Through the Next Five Years
  • Theme #1: AI Is Not a Fad
  • Theme #2: Data-Generating Sensors Will Proliferate
  • Theme #3: Data Will Be Processed at the Edge
  • Theme #4: AI Will Spread Everywhere
  • Theme #5: AI Will Disappear into the Background and Become Boring
  • Future AI Use Cases for Business
  • Cybersecurity
  • Autonomous Driving, Autonomous Everything
  • Conversational Digital Agents and Personal Assistants
  • Real Estate
  • Retail
  • Insurance
  • Customer Service
  • The Future of Work in an AI-Driven World
  • A Deeper Dive into AI and Edge Computing
  • Using the Edge and AI for Good
  • Conclusion
  • Index

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