Book description
Artificial intelligence (AI) in its various forms –– machine learning, chatbots, robots, agents, etc. –– is increasingly being seen as a core component of enterprise business workflow and information management systems. The current promise and hype around AI are being driven by software vendors, academic research projects, and startups. However, we posit that the greatest promise and potential for AI lies in the enterprise with its applications touching all organizational facets.
With increasing business process and workflow maturity, coupled with recent trends in cloud computing, datafication, IoT, cybersecurity, and advanced analytics, there is an understanding that the challenges of tomorrow cannot be solely addressed by today’s people, processes, and products.
There is still considerable mystery, hype, and fear about AI in today’s world. A considerable amount of current discourse focuses on a dystopian future that could adversely affect humanity. Such opinions, with understandable fear of the unknown, don’t consider the history of human innovation, the current state of business and technology, or the primarily augmentative nature of tomorrow’s AI.
This book demystifies AI for the enterprise. It takes readers from the basics (definitions, state-of-the-art, etc.) to a multi-industry journey, and concludes with expert advice on everything an organization must do to succeed. Along the way, we debunk myths, provide practical pointers, and include best practices with applicable vignettes.
AI brings to enterprise the capabilities that promise new ways by which professionals can address both mundane and interesting challenges more efficiently, effectively, and collaboratively (with humans). The opportunity for tomorrow’s enterprise is to augment existing teams and resources with the power of AI in order to gain competitive advantage, discover new business models, establish or optimize new revenues, and achieve better customer and user satisfaction.
Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Dedication
- Contents
- Preface
- Acknowledgements
- Author Bios
- Chapter 1 AI Strategy for the Executive
-
Chapter 2 Learning Algorithms, Machine/Deep Learning, and Applied AI: A Conceptual Framework
-
Introduction
- Chapter Overview
- A Brief History of AI-ML
- What’s Different about AI-ML Today?
- What Is Machine Learning?
- How Do Machines Reason and Learn: A Crash Course in Learning Algorithms
- A Guided Tour of Learning Algorithms
-
Best Practices for Successful Machine Learning and AI Applications in Your Enterprise
- Ask a Specific Question
- Start Simple
- Try Many Algorithms
- Treat Your Data with Suspicion
- Normalize Your Inputs
- Validate Your Model
- Ensure the Quality of Your Training Data
- Set Up a Feedback Loop
- Don’t Trust Black Boxes
- Correlation Is Not Causation
- Monitor Ongoing Performance
- Keep Track Of Your Model Changes
- Don’t be Fooled by “Accuracy”
- Acknowledgments
- Notes
-
Introduction
-
Chapter 3 AI for Supply Chain Management
- Introduction
- Understand
- Automate
- Predict
- Optimize
- Plan: How AI Can Improve the Life of a Planner
- Buy: How Buyers Can Leverage AI for Better Pricing and Availability
- Make: AI Helps Manufacturing Make More, Better, Faster, and Cheaper
- Sell: How AI Can Improve Marketing, Promotion, and Operations Planning in the Supply Chain
- Deliver: AI Automates and Streamlines Logistics
- Supply Chain Control Towers
- Supply Chain Staffing in an AI-Enhanced Enterprise
- Conclusions
- Notes
-
Chapter 4 HR and Talent Management
- Introduction
- Workforce Planning and Hiring
- Helping Employees Succeed In the Workplace
-
Retention: Keeping Employees
- Category 1: Employees Who Are Already Thinking of Leaving
- Category 2: Bad Bosses
- Category 3: Corporate Culture and the Importance of the Work (Clarity, Meaning, Influence, and Feedback)
- Category 4: Compensation, but not Just Salary
- Category 5: Employees in Highly Competitive Roles such as Data Science
- Minimizing Risk
- Measurement in HR: Statistics, Metrics, and Analytics
- Privacy and Ethics
- Use of Information
- Conclusions
- Notes
-
Chapter 5 Customer Experience Management
- Introduction
- Customer Experience
- AI powering the Digital Marketing Funnel
-
AI powering the 5 E’s of Experience (Connected to the Marketing Funnel through Maslow’s Hierarchy of Needs)
- Encounter (Create Awareness Among Your Stakeholders)
- Expectations (Identify Your Stakeholders’ Needs)
- Empathy (Meet Your Stakeholders at Their Place of Need)
- Engagement (Generate Interest and Curiosity Via Data-Informed Experiences)
- Emotion (Apply Sentiment Analysis to Discover How the Experience Made Your Stakeholders Feel)
- Recommender Engines and Personalization
- AI for Workforce Automation (Employees)
- AI for Competitive Intelligence & Business Development (Executives and Strategists)
- The New Hyper-Personalization
- Contextual: IoT = The Internet of Context?
- Geospatial: Location Analytics
- Cognitive Analytics: Next-best Action, Based on a 360 View of the Customer
- The Growing Role of AI in Customer Relations
- Big Data Is the Fuel (The Input) That Informs the Enterprise About the Customer: Sources (Digital Devices, IoT, Data Lake,…)
-
Machine Learning Is the Tool (The Value-Creation Lever) to Gain Insights from the Customer, in 3 Ways
- Supervised Learning (Predictive Analytics): Forecasting Customer Needs
- Unsupervised Learning (Discovery Analytics): Segment / Pattern / Trend Discovery in Customer Behaviors and Experiences
- Reinforcement Learning for Prescriptive Behavioral Analytics: Adapting, Improving, Optimizing Customer Experience
- Analytics as the Outcome (i.e., the Business Product)
- Notes
-
Chapter 6 AI in Financial Services
- Introduction
- Why AI Should Be Used to Create a Competitive Advantage in Financial Services
- Surviving and Thriving with AI
- AI Use Cases in Financial Services
- How to Identify Your Best Use Cases
- Building AI at Scale in Financial Services
-
Getting to AI Adoption at Scale
-
Avoiding Common Traps is Key to Success
- Trap 1: Lack of Business Buy-in and Understanding of the Analytics Roadmap
- Trap 2: Forgetting to Train the Rest of the Business in Analytics
- Trap 3: Treating the Analytics Team(s) As an Internal Consultancy
- Trap 4: Being Stuck in the Pilot Stage
- Trap 5: Giving up After False Starts
- Trap 6: Building Huge Data Infrastructure Without the End in Mind
- Don’t Go Alone, Use Partnerships
- A Note on Responsible AI
- AI-Driven Banking: A Peek into Capital One’s Journey
-
Avoiding Common Traps is Key to Success
- The Future of Financial Services
- Notes
-
Chapter 7 Artificial Intelligence in Retail
- Introduction
- Chapter Overview
- The Retail Industry Landscape and Challenges
- Importance of Data
- Retail Use Cases
- Responsible Retailing
- Future of AI/ML in Retail
- Acknowledgements
- Notes
- Chapter 8 Visualization
-
Chapter 9 Solution Architectures
- Introduction
- AI Inference Architecture Taxonomy
- Popular Design Patterns for Deploying AI
- Lab vs. Production
- On Debt
- Some Design Considerations for the Production Use of Machine Learning
- The Machine-Learning Lifecycle
- Training Models and Hardware Selection
- Inference and Hardware
- Monitoring Machine Learning in production
- Security and the Design of Machine-Learning-Based Services
- Summary
- Notes
-
Chapter 10 AI and Corporate Social Responsibility
- Introduction
-
Things That Keep Us Up At Night
- Privacy
- The Surveillance Society
- Technology That Helps Protect Our Privacy
- Federated Learning
- Differential Privacy
- Inference Privacy
- Keeping Your Model Private
- Privacy-Preserving Machine Learning in Practice and Business
- Bias
- Transparency, Explainability, and Interpretability in AI
- Does This Process Comply with Applicable Regulations?
- Something That Helps Us Sleep At Night: Building Good AI
- A Guide to Using AI to Effect Positive Social Change
- A Technical Contributor’s Experience
- An Entrepreneur’s Experience
- Summary
- Notes
- Chapter 11 Future of Enterprise AI
-
Appendix
- Case Study 1: Get More Value from Your Banking Data—How to Turn Your Analytics Team into a Profit Centre
- Case Study 2: AI in Financial Services: WeBank Practices—A Large Gap in China’s SME Financing Landscape
- Case Study 3: How Orchestrated Intelligence Inc. (Oii) Is Utilizing Artificial Intelligence to Model a Transformation in Supply Chain Performance
- Case Study 4: 7-Eleven and Cashierless Stores
- Case Study 5: Paper Quality at Georgia-Pacific
- Case Study 6: GE Healthcare: 1st FDA Clearance for an AI-Enabled X-Ray Device
-
Case Study 7: UCSF Health and H2O.ai—Applying Document AI to Automate Workflows in Healthcare
- Problem Overview
- UCSF: –H2O.ai Partnership
- H2O.ai’s Document AI
- Data Science Considerations/Design
- Last Mile: How Do You Enable Workflows with AI?
- Feedback Loop: Retraining Models with User Input
- What Did We Learn?
- What Do We Go Next?
- Conclusions
- About UCSF Health
- About UCSF CDHI
- About H2O.ai
- About the H2O AI Hybrid Cloud
- Notes
- Index
Product information
- Title: Demystifying AI for the Enterprise
- Author(s):
- Release date: December 2021
- Publisher(s): Productivity Press
- ISBN: 9781351032926
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