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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Tech Today
- Sprache: Englisch
Embrace emerging AI trends and integrate your operations with cutting-edge solutions
Enterprise AI in the Cloud: A Practical Guide to Deploying End-to-End Machine Learning and ChatGPT Solutions is an indispensable resource for professionals and companies who want to bring new AI technologies like generative AI, ChatGPT, and machine learning (ML) into their suite of cloud-based solutions. If you want to set up AI platforms in the cloud quickly and confidently and drive your business forward with the power of AI, this book is the ultimate go-to guide. The author shows you how to start an enterprise-wide AI transformation effort, taking you all the way through to implementation, with clearly defined processes, numerous examples, and hands-on exercises. You'll also discover best practices on optimizing cloud infrastructure for scalability and automation.
Enterprise AI in the Cloud helps you gain a solid understanding of:
- AI-First Strategy: Adopt a comprehensive approach to implementing corporate AI systems in the cloud and at scale, using an AI-First strategy to drive innovation
- State-of-the-Art Use Cases: Learn from emerging AI/ML use cases, such as ChatGPT, VR/AR, blockchain, metaverse, hyper-automation, generative AI, transformer models, Keras, TensorFlow in the cloud, and quantum machine learning
- Platform Scalability and MLOps (ML Operations): Select the ideal cloud platform and adopt best practices on optimizing cloud infrastructure for scalability and automation
- AWS, Azure, Google ML: Understand the machine learning lifecycle, from framing problems to deploying models and beyond, leveraging the full power of Azure, AWS, and Google Cloud platforms
- AI-Driven Innovation Excellence: Get practical advice on identifying potential use cases, developing a winning AI strategy and portfolio, and driving an innovation culture
- Ethical and Trustworthy AI Mastery: Implement Responsible AI by avoiding common risks while maintaining transparency and ethics
- Scaling AI Enterprise-Wide: Scale your AI implementation using Strategic Change Management, AI Maturity Models, AI Center of Excellence, and AI Operating Model
Whether you're a beginner or an experienced AI or MLOps engineer, business or technology leader, or an AI student or enthusiast, this comprehensive resource empowers you to confidently build and use AI models in production, bridging the gap between proof-of-concept projects and real-world AI deployments.
With over 300 review questions, 50 hands-on exercises, templates, and hundreds of best practice tips to guide you through every step of the way, this book is a must-read for anyone seeking to accelerate AI transformation across their enterprise.
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Seitenzahl: 749
Veröffentlichungsjahr: 2023
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Table of Contents
COVER
TABLE OF CONTENTS
TTILE PAGE
Introduction
HOW THIS BOOK IS ORGANIZED
WHO SHOULD READ THIS BOOK?
WHY YOU SHOULD READ THIS BOOK
UNIQUE FEATURES
PART I: Introduction
1 Enterprise Transformation with AI in the Cloud
UNDERSTANDING ENTERPRISE AI TRANSFORMATION
LEVERAGING ENTERPRISE AI OPPORTUNITIES
WORKBOOK TEMPLATE -
ENTERPRISE AI TRANSFORMATION CHECKLIST
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
2 Case Studies of Enterprise AI in the Cloud
CASE STUDY 1: THE U.S. GOVERNMENT AND THE POWER OF HUMANS AND MACHINES WORKING TOGETHER TO SOLVE PROBLEMS AT SCALE
CASE STUDY 2: CAPITAL ONE AND HOW IT BECAME A LEADING TECHNOLOGY ORGANIZATION IN A HIGHLY REGULATED ENVIRONMENT
CASE STUDY 3: NETFLIX AND THE PATH COMPANIES TAKE TO BECOME WORLD-CLASS
WORKBOOK TEMPLATE - AI CASE STUDY
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART II: Strategizing and Assessing for AI
3 Addressing the Challenges with Enterprise AI
CHALLENGES FACED BY COMPANIES IMPLEMENTING ENTERPRISE-WIDE AI
HOW DIGITAL NATIVES TACKLE AI ADOPTION
GET READY: AI TRANSFORMATION IS MORE CHALLENGING THAN DIGITAL TRANSFORMATION
CHOOSING BETWEEN SMALLER PoC POINT SOLUTIONS AND LARGE-SCALE AI INITIATIVES
WORKBOOK TEMPLATE:
AI CHALLENGES ASSESSMENT
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
4 Designing AI Systems Responsibly
THE PILLARS OF RESPONSIBLE AI
WORKBOOK TEMPLATE:
RESPONSIBLE AI DESIGN TEMPLATE
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
5 Envisioning and Aligning Your AI Strategy
STEP-BY-STEP METHODOLOGY FOR ENTERPRISE-WIDE AI
WORKBOOK TEMPLATE:
VISION ALIGNMENT WORKSHEET
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
6 Developing an AI Strategy and Portfolio
LEVERAGING YOUR ORGANIZATIONAL CAPABILITIES FOR COMPETITIVE ADVANTAGE
INITIATING YOUR STRATEGY AND PLAN TO KICKSTART ENTERPRISE AI
WORKBOOK TEMPLATE:
BUSINESS CASE AND AI STRATEGY
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
7 Managing Strategic Change
ACCELERATING YOUR AI ADOPTION WITH STRATEGIC CHANGE MANAGEMENT
WORKBOOK TEMPLATE:
STRATEGIC CHANGE MANAGEMENT PLAN
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART III: Planning and Launching a Pilot Project
8 Identifying Use Cases for Your AI/ML Project
THE USE CASE IDENTIFICATION PROCESS FLOW
PRIORITIZING YOUR USE CASES
USE CASES TO CHOOSE FROM
WORKBOOK TEMPLATE: USE CASE IDENTIFICATION SHEET
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
9 Evaluating AI/ML Platforms and Services
BENEFITS AND FACTORS TO CONSIDER WHEN CHOOSING AN AI/ML SERVICE
AWS AI AND ML SERVICES
CORE AI SERVICES
SPECIALIZED AI SERVICES
MACHINE LEARNING SERVICES
THE GOOGLE AI/ML SERVICES STACK
THE MICROSOFT AI/ ML SERVICES STACK
OTHER ENTERPRISE CLOUD AI PLATFORMS
WORKBOOK TEMPLATE:
AI/ML PLATFORM EVALUATION SHEET
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
10 Launching Your Pilot Project
LAUNCHING YOUR PILOT
FOLLOWING THE MACHINE LEARNING LIFECYCLE
WORKBOOK TEMPLATE: AI/ML PILOT LAUNCH CHECKLIST
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART IV: Building and Governing Your Team
11 Empowering Your People Through Org Change Management
SUCCEEDING THROUGH A PEOPLE-CENTRIC APPROACH
ALIGNING YOUR ORGANIZATION AROUND AI ADOPTION TO ACHIEVE BUSINESS OUTCOMES
WORKBOOK TEMPLATE: ORG CHANGE MANAGEMENT PLAN
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
NOTE
12 Building Your Team
UNDERSTANDING THE ROLES AND RESPONSIBILITIES IN AN ML PROJECT
WORKBOOK TEMPLATE: TEAM BUILDING MATRIX
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART V: Setting Up Infrastructure and Managing Operations
13 Setting Up an Enterprise AI Cloud Platform Infrastructure
REFERENCE ARCHITECTURE PATTERNS FOR TYPICAL USE CASES
FACTORS TO CONSIDER WHEN BUILDING AN ML PLATFORM
KEY COMPONENTS OF AN ML AND DL PLATFORM
KEY COMPONENTS OF AN ENTERPRISE AI/ML HEALTHCARE PLATFORM
WORKBOOK TEMPLATE:
ENTERPRISE AI CLOUD PLATFORM SETUP CHECKLIST
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
14 Operating Your AI Platform with MLOps Best Practices
CENTRAL ROLE OF MLOps IN BRIDGING INFRASTRUCTURE, DATA, AND MODELS
MODEL OPERATIONALIZATION
WORKBOOK TEMPLATE:
ML OPERATIONS AUTOMATION GUIDE
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART VI: Processing Data and Modeling
15 Process Data and Engineer Features in the Cloud
UNDERSTANDING YOUR DATA NEEDS
BENEFITS AND CHALLENGES OF CLOUD-BASED DATA PROCESSING
THE DATA PROCESSING PHASES OF THE ML LIFECYCLE
UNDERSTANDING THE DATA EXPLORATION AND PREPROCESSING STAGE
FEATURE ENGINEERING
WORKBOOK TEMPLATE:
DATA PROCESSING & FEATURE ENGINEERING WORKFLOW
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
16 Choosing Your AI/ML Algorithms
BACK TO THE BASICS: WHAT IS ARTIFICIAL INTELLIGENCE?
FACTORS TO CONSIDER WHEN CHOOSING A MACHINE LEARNING ALGORITHM
DATA-DRIVEN PREDICTIONS USING MACHINE LEARNING
THE AI/ML FRAMEWORK
WORKBOOK TEMPLATE:
AI/ML ALGORITHM SELECTION GUIDE
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
17 Training, Tuning, and Evaluating Models
MODEL BUILDING
MODEL TRAINING
MODEL TUNING
MODEL VALIDATION
MODEL EVALUATION
BEST PRACTICES
WORKBOOK TEMPLATE:
MODEL TRAINING AND EVALUATION SHEET
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART VII: Deploying and Monitoring Models
18 Deploying Your Models Into Production
STANDARDIZING MODEL DEPLOYMENT, MONITORING, AND GOVERNANCE
DEPLOYING YOUR MODELS
SYNCHRONIZING ARCHITECTURE AND CONFIGURATION ACROSS ENVIRONMENTS
MLOps AUTOMATION: IMPLEMENTING CI/CD FOR MODELS
WORKBOOK TEMPLATE:
MODEL DEPLOYMENT PLAN
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
19 Monitoring Models
MONITORING MODELS
KEY STRATEGIES FOR MONITORING ML MODELS
TRACKING KEY MODEL PERFORMANCE METRICS
REAL-TIME VS. BATCH MONITORING
TOOLS FOR MONITORING MODELS
BUILDING A MODEL MONITORING SYSTEM
MONITORING MODEL ENDPOINTS
OPTIMIZING MODEL PERFORMANCE
WORKBOOK TEMPLATE:
MODEL MONITORING TRACKING SHEET
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
20 Governing Models for Bias and Ethics
IMPORTANCE OF MODEL GOVERNANCE
STRATEGIES FOR FAIRNESS
OPERATIONALIZING GOVERNANCE
WORKBOOK TEMPLATE:
MODEL GOVERNANCE FOR BIAS & ETHICS CHECKLIST
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART VIII: Scaling and Transforming AI
21 Using the AI Maturity Framework to Transform Your Business
SCALING AI TO BECOME AN AI-FIRST COMPANY
THE AI MATURITY FRAMEWORK
WORKBOOK TEMPLATE:
AI MATURITY ASSESSMENT TOOL
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
22 Setting Up Your AI COE
SCALING AI TO BECOME AN AI-FIRST COMPANY
ESTABLISHING AN AI CENTER OF EXCELLENCE
WORKBOOK TEMPLATE:
AI CENTER OF EXCELLENCE (AICOE) SETUP CHECKLIST
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
23 Building Your AI Operating Model and Transformation Plan
UNDERSTANDING THE AI OPERATING MODEL
IMPLEMENTING YOUR AI OPERATING MODEL
WORKBOOK TEMPLATE:
AI OPERATING MODEL AND TRANSFORMATION PLAN
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART IX: Evolving and Maturing AI
24 Implementing Generative AI Use Cases with ChatGPT for the Enterprise
THE RISE AND REACH OF GENERATIVE AI
THE POWER OF GENERATIVE AI/ChatGPT FOR BUSINESS TRANSFORMATION AND INNOVATION
IMPLEMENTING GENERATIVE AI AND ChatGPT
BEST PRACTICES WHEN IMPLEMENTING GENERATIVE AI AND ChatGPT
GENERATIVE AI CLOUD PLATFORMS
WORKBOOK TEMPLATE:
GENERATIVE AI USE CASE PLANNER
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
25 Planning for the Future of AI
EMERGING AI TRENDS
THE PRODUCTIVITY REVOLUTION
CRITICAL ENABLERS
EMERGING TRENDS IN DATA MANAGEMENT
WORKBOOK TEMPLATE:
FUTURE OF AI ROADMAP
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
26 Continuing Your AI Journey
REFLECTING ON YOUR PROGRESS
PLANNING FOR THE FUTURE: BUILDING A ROADMAP
ENSURING RESPONSIBLE AI/ML IMPLEMENTATION
PREPARING FOR THE CHALLENGES AHEAD
INDEX
COPYRIGHT
DEDICATION
ACKNOWLEDGMENTS
ABOUT THE AUTHOR
ABOUT THE TECHNICAL EDITOR
END USER LICENSE AGREEMENT
List of Tables
Chapter 1
TABLE 1.1: Companies Leading the Way in Adopting AI
Chapter 3
TABLE 3.1: AI Transformation Challenges
Chapter 6
TABLE 6.1: AI Strategy and Portfolio Deliverables
Chapter 9
TABLE 9.1: Differences Between Machine Learning Algorithms, Models, and Serv...
TABLE 9.2: Comparison of Amazon Forecast Models for Various Use Cases
TABLE 9.3: Google AI/ML Services for Developers
Chapter 16
TABLE 16.1: Supervised and Unsupervised Machine Learning Algorithms
TABLE 16.2: Use Cases for Linear Regression Algorithms
TABLE 16.3: Use Cases for Logistic Regression
TABLE 16.4: Use Cases for Decision Tree Algorithms
TABLE 16.5: Use Cases for Support Vector Machines
TABLE 16.6: Use Cases for Autoencoders
TABLE 16.7: TensorFlow vs. PyTorch ML Frameworks
Chapter 17
TABLE 17.1: Sample Confusion Matrix
TABLE 17.2: Sample Results Predicted by the Model
TABLE 17.3: Confusion Matrix with Values
Chapter 21
TABLE 21.1: The Stages of AI Maturity for Strategy and Planning
TABLE 21.2: The Stages of AI Maturity for People
TABLE 21.3: The Stages of AI Maturity for Platform & Operations
TABLE 21.4: The Stages of AI Maturity for Data/Models
Chapter 24
TABLE 24.1: Artifacts That Can Be Created by Generative AI
TABLE 24.2: Discriminative vs. Generative Models
TABLE 24.3: The Differences Between Generative Language and Generative Image...
TABLE 24.4: Difference Between Foundation and Language Models
TABLE 24.5: Differences Between GANs, VAEs, and Diffusion Models
TABLE 24.6: Base Models Grouped by Family and Capability in Azure
List of Illustrations
Chapter 1
FIGURE 1.1: Comparison of enterprise-wide AI adoption by leaders and others...
FIGURE 1.2: An AI-first strategy leads to an enterprise transformation.
FIGURE 1.3: The triumvirate of AI: cloud computing, big data, and software a...
FIGURE 1.4: The network effect
FIGURE 1.5: Enterprise AI opportunities
Chapter 2
FIGURE 2.1: Challenges, benefits, and solutions adopted by the U.S. governme...
FIGURE 2.2: Challenges, benefits, and solutions adopted by Capital One
FIGURE 2.3: Capital One's transformation journey to AI-first status
FIGURE 2.4: The path to becoming world-class
Chapter 3
FIGURE 3.1: STRATEGIZE AND PREPARE: Address the challenges with AI
FIGURE 3.2: Skill sets required in a typical AI/ML project
FIGURE 3.3: A typical data infrastructure pipeline
Chapter 4
FIGURE 4.1: STRATEGIZE AND PREPARE: Design AI systems responsibly
FIGURE 4.2: Key pillars of Responsible AI
FIGURE 4.3: Collaborative AI: Enhancing human capacity with AI
FIGURE 4.4: Key elements of trustworthy AI
FIGURE 4.5: Scalable AI: Key considerations and case study
FIGURE 4.6: Building human-centric AI systems with human values at the foref...
Chapter 5
FIGURE 5.1: STRATEGIZE AND PREPARE: Envision and align
FIGURE 5.2: Steps to implement enterprise AI
FIGURE 5.3: Envision phase: tasks and deliverables
FIGURE 5.4: Align phase: tasks and deliverables
Chapter 6
FIGURE 6.1: STRATEGIZE AND PREPARE: Develop business case and AI strategy
FIGURE 6.2: Capability focus areas for enterprise AI
FIGURE 6.3: Business focus areas for strategy and planning
FIGURE 6.4: Examples of business strategy, AI strategy, business goals, and ...
FIGURE 6.5: AI strategy: phases and capabilities
FIGURE 6.6: AI execution: phases and capabilities
Chapter 7
FIGURE 7.1: STRATEGIZE AND PREPARE: Manage strategic change
FIGURE 7.2: AI change acceleration strategy: phases and capabilities
FIGURE 7.3: Develop AI acceleration charter and governance mechanisms
FIGURE 7.4: Transform your leadership: Envision and Align phases
FIGURE 7.5: Transform your leadership: Launch and Scale phases
FIGURE 7.6: Transform your workspace: tasks and deliverables
FIGURE 7.7: Ensuring leadership alignment for AI, including generative AI in...
FIGURE 7.8: AI strategy: phases and capabilities
Chapter 8
FIGURE 8.1: PLAN AND LAUNCH: Identify Use Cases for Your AI/ML & Gen AI Proj...
FIGURE 8.2: Use case identification process flow
FIGURE 8.3: Defining business objectives for your AI initiative
FIGURE 8.4: Define success metrics for your AI initiative
FIGURE 8.5: Business value and feasibility analysis to prioritize use cases...
FIGURE 8.6: Digital twin of jet airplane engine
FIGURE 8.7: The benefits of intelligent search
FIGURE 8.8: Machine learning modernization framework, benefits, and technolo...
FIGURE 8.9: Applications and technologies behind computer vision
FIGURE 8.10: Data types currently supported by generative AI
Chapter 9
FIGURE 9.1: PLAN AND LAUNCH: Evaluate AI/ML Platforms & Services
FIGURE 9.2: AWS AI/ML stack
FIGURE 9.3: AWS core AI services
FIGURE 9.4: Amazon Textract use cases
FIGURE 9.5: Amazon Lex: Conversational interfaces using voice and text
FIGURE 9.6: AWS specialized AI services
FIGURE 9.7: How Amazon Forecast works
FIGURE 9.8: How Amazon Kendra works
FIGURE 9.9: Amazon CodeGuru uses machine learning to improve app code.
FIGURE 9.10: Amazon industrial AI solutions
FIGURE 9.11: AWS ML services
FIGURE 9.12: SageMaker's capabilities
FIGURE 9.13: Google AI/ML stack for data scientists
FIGURE 9.14: Azure-applied AI services
FIGURE 9.15: Using Azure Video Indexer tool
Chapter 10
FIGURE 10.1: PLAN AND LAUNCH: Launch your AI pilot
FIGURE 10.2: Activities to move from Envision to Align to launching a pilot...
FIGURE 10.3: Machine learning phases
Chapter 11
FIGURE 11.1: BUILD AND GOVERN YOUR TEAM: Empower Your People Through Org Cha...
FIGURE 11.2: Change management focus areas for enterprise AI
FIGURE 11.3: Percentage of employees experiencing cultural tension due to ch...
FIGURE 11.4: Evolve your culture: phases and tasks
FIGURE 11.5: Evolve your culture: phases and deliverables
FIGURE 11.6: Redesign your organization: tasks and deliverables
FIGURE 11.7: Organizational alignment
Chapter 12
FIGURE 12.1: BUILD AND GOVERN YOUR TEAM: Building your team
FIGURE 12.2: Core and auxiliary roles in a machine learning project
Chapter 13
FIGURE 13.1: Setting up the enterprise AI cloud platform infrastructure
FIGURE 13.2: Customer 360-degree architecture
FIGURE 13.3: Customer 360-degree architecture using AWS components (AWS comp...
FIGURE 13.4: Event-driven near real-time predictive analytics using IoT data...
FIGURE 13.5: IoT-based event-driven predictive analytics using AWS component...
FIGURE 13.6: Personalized recommendation architecture
FIGURE 13.7: Personalized recommendation architecture using AWS components (...
FIGURE 13.8: Real-time customer engagement architecture
FIGURE 13.9: Real-time customer engagement architecture on AWS (AWS componen...
FIGURE 13.10: Real-time customer engagement architecture on Azure
FIGURE 13.11: Fraud detection architecture
FIGURE 13.12: Fraud detection architecture on AWS (AWS components shown in b...
FIGURE 13.13: Basic components and their integrations in an AI/ML platform
FIGURE 13.14: Data management architecture
FIGURE 13.15: Components of an ML experimentation platform
FIGURE 13.16: Hybrid and edge computing in machine learning
Chapter 14
FIGURE 14.1: SETUP INFRASTRUCTURE AND MANAGE OPERATIONS: Automate AI platfor...
FIGURE 14.2: CI/CD flow for model training and deployment
FIGURE 14.3: CI/CD pipeline for ML training and deployment on AWS
FIGURE 14.4: Code deployment pipeline
FIGURE 14.5: Centralized model inventory management
FIGURE 14.6: Logging and auditing architecture
FIGURE 14.7: Data and artifacts lineage tracking
FIGURE 14.8: Using tags to track resource usage, cost management, billing, a...
Chapter 15
FIGURE 15.1: PROCESS DATA AND MODELING: Process data and engineer features i...
FIGURE 15.2: Data types
FIGURE 15.3: Data collection process
FIGURE 15.4: Average time spent in ML tasks
FIGURE 15.5: Data processing workflow
FIGURE 15.6: Data preprocessing strategies
FIGURE 15.7: Feature engineering components
FIGURE 15.8: Feature extraction techniques
FIGURE 15.9: Feature imputation techniques
Chapter 16
FIGURE 16.1: PROCESS DATA AND MODELING: Choose your AI/ML algorithm
FIGURE 16.2: Umbrella of AI technologies
FIGURE 16.3: Use cases of supervised and unsupervised learning tasks
FIGURE 16.4: How supervised learning works
FIGURE 16.5: How supervised learning worksSource: (a) Kate / Adobe Systems I...
FIGURE 16.6: Types of supervised learning
FIGURE 16.7: Example of linear regression with one independent variable
FIGURE 16.8: Example logistic regression graph showing the probability of cu...
FIGURE 16.9: Decision tree that shows the survival of passengers on the
Tita
...
FIGURE 16.10: A simplified view of a random forest
FIGURE 16.11: Support vector machine trained from two samples
FIGURE 16.12: Example of K-NN classification
FIGURE 16.13: How unsupervised learning works
FIGURE 16.14: K-means clusters created from an Iris flower dataset
FIGURE 16.15: Interpreting the PCA: The start of the bend indicates that thr...
FIGURE 16.16: Schema of a basic autoencoder
FIGURE 16.17: Collaborative filtering based on a rating system
FIGURE 16.18: Reinforcement learning is when an agent takes action in an env...
FIGURE 16.19: Deep learning is different from machine learning and tradition...
FIGURE 16.20: Typical CNN architecture
FIGURE 16.21: GAN model
Chapter 17
FIGURE 17.1: Training, tuning, and evaluating models
FIGURE 17.2: Phases of a model development lifecycle
FIGURE 17.3: Model training and tuning components
FIGURE 17.4: Problems faced when training models
FIGURE 17.5: Model artifacts outputted during model training
FIGURE 17.6: Tuning hyperparameters for optimal model performance
FIGURE 17.7: Grid versus random searches
FIGURE 17.8: Validating machine learning models
FIGURE 17.9: Validation across ML model development phases
FIGURE 17.10: Validation metrics for classification problems
FIGURE 17.11: Performance evaluation pipeline
FIGURE 17.12: Machine learning security best practices
FIGURE 17.13: Reliability best practices
FIGURE 17.14: Cost optimization best practices
Chapter 18
FIGURE 18.1: DEPLOY AND MONITOR MODELS: Deploy your models into production....
FIGURE 18.2: Model deployment process
FIGURE 18.3: Model deployment options
FIGURE 18.4: Choosing a deployment strategy
FIGURE 18.5: Different steps in an inference pipeline
FIGURE 18.6: Implementing CI/CD for models
Chapter 19
FIGURE 19.1: Monitoring models
FIGURE 19.2: Key strategies of monitoring
FIGURE 19.3: Choosing model performance metrics
FIGURE 19.4: Monitoring the health of your endpoints
FIGURE 19.5: Implementing a recoverable endpoint
FIGURE 19.6: ML lifecycle
Chapter 20
FIGURE 20.1: DEPLOY AND GOVERN MODELS: Govern models for bias and ethics
FIGURE 20.2: Strategies to ensure fairness in models
FIGURE 20.3: Ethical considerations when deploying models
FIGURE 20.4: Different benefits of artifacts management
FIGURE 20.5: Central repository of various machine learning artifacts
FIGURE 20.6: Setting up a model governance framework
Chapter 21
FIGURE 21.1: SCALE AND TRANSFORM: Use the AI Maturity Framework to transform...
FIGURE 21.2: Strategic pillars for an AI-first strategy
FIGURE 21.3: Sample AI Maturity Framework result
FIGURE 21.4: Five stages of the AI Maturity Framework
FIGURE 21.5: Key elements of the Optimizing stage
FIGURE 21.6: Maturity levels for the people dimension
Chapter 22
FIGURE 22.1: SCALE AND TRANSFORM: Set up your AI COE
FIGURE 22.2: AI core team responsibilities
FIGURE 22.3: Evolution of an AI COE
Chapter 23
FIGURE 23.1: SCALE AND TRANSFORM: Build your AI operating model and transfor...
FIGURE 23.2: Components of an AI operating model
FIGURE 23.3: Six transformational ways to build an AI operating model
FIGURE 23.4: Customer-centric AI strategy to drive innovation
FIGURE 23.5: A product-centric approach to building AI solutions
FIGURE 23.6: Organizing teams around the product
FIGURE 23.7: Start small and build iteratively with cross-functional teams
FIGURE 23.8: AI product testing and measurement framework
FIGURE 23.9: Aligning operating model with strategic value and establishing ...
FIGURE 23.10: Different components of an AI transformation plan
Chapter 24
FIGURE 24.1: EVOLVE AND MATURE: Generative AI and ChatGPT use cases for your...
FIGURE 24.2: Generative AI is a subset of deep learning
FIGURE 24.3: Structure of artificial neural networks
FIGURE 24.4: Inputs and outputs of a foundation model
FIGURE 24.5: The AI-powered content generation use case
FIGURE 24.6: Generative AI applications
FIGURE 24.7: Long-term workforce transformation with AI as a partner
FIGURE 24.8: Components of a transformer model
FIGURE 24.9: Build versus buy options when implementing generative AI
FIGURE 24.10: Using prompts to train LLMs
FIGURE 24.11: Retrieval augmented generation architecture
FIGURE 24.12: Strategy best practices for implementing Generative AI
FIGURE 24.13: Challenges of implementing generative AI
FIGURE 24.14: Mitigating generative AI risks
FIGURE 24.15: Foundation models in the model garden
FIGURE 24.16: AWS Generative AI cloud platform tools
FIGURE 24.17: AWS CodeWhisperer workflow
FIGURE 24.18: Features of AWS Inferentia and Trainium
FIGURE 24.19: Provisioning an OpenAI service in Azure
FIGURE 24.20: Azure OpenAI Studio playgrounds for model testing
FIGURE 24.21: GitHub Copilot's features and benefits
FIGURE 24.22: Additional gen AI tools and platforms
Chapter 25
FIGURE 25.1: EVOLVE AND MATURE: Plan for the future of AI
FIGURE 25.2: Intelligent apps provide intelligent services to users.
FIGURE 25.3: Critical enablers for enterprise AI
FIGURE 25.4: Knowledge graph reflecting complex real-world data as meaningf...
Chapter 26
FIGURE 26.1: Continue your AI journey
Guide
Cover
Title Page
Copyright
Dedication
Acknowledgments
About the Author
About the Technical Editor
Introduction
Table of Contents
Begin Reading
Index
End User License Agreement
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ENTERPRISE AI IN THE CLOUD
Practical Guide to Deploying End-to-End Machine Learning and ChatGPT™ Solutions
Rabi Jay
Introduction
WELCOME TOEnterprise AI in the Cloud: A Practical Guide to Deploying End-to-End Machine Learning and ChatGPT Solutions. This book is the definitive guide to equip readers with the methodology and tools necessary to implement artificial intelligence (AI), machine learning (ML), and generative AI technologies. You have in your hands a powerful guide to potentially transform your company and your own career.
In this book, you learn how to
Develop AI strategy, solve challenges, and drive change
Identify and prioritize AI use cases, evaluate AI/ML platforms, and launch a pilot project
Build a dream team, empower people, and manage projects effectively
Set up an AI infrastructure using the major cloud platforms and scale your operations using MLOps
Process and engineer data and deploy, operate, and monitor AI models in production
Use govern models and implement AI ethically and responsibly
Scale your AI effort by setting up an AI center of excellence (AI COE), an AI operating model, and an enterprise transformation plan
Evolve your company using generative AI such as ChatGPT, plan for the future, and continuously innovate with AI
From real-world AI implementation, AI/ML use cases, and hands-on labs to nontechnical aspects such as team development and AI-first strategy, this book has it all.
In a nutshell, this book is a comprehensive guide that bridges the gap between theory and real-world AI deployments. It's a blend of strategy and tactics, challenges, and solutions that make it an indispensable resource for those interested in building and operating AI systems for their enterprise.
HOW THIS BOOK IS ORGANIZED
This book is not just a theoretical guide but a practical, hands-on manual to transform your business through AI in the cloud. My aim is to provide you with the tools and knowledge you need to harness the power of AI for your enterprise with a methodology that is comprehensive and deep.
Part I
: Introduction:
In
Part I
, I explain how enterprises are undergoing transformation through the adoption of AI using cloud technologies. I cover industry use cases for AI in the cloud and its benefits, as well as the current state of AI transformation. I also discuss various case studies of successful AI implementations, including the U.S. government, Capital One, and Netflix.
Part II
: Strategizing and Assessing for AI:
In this part, I discuss the nitty-gritty of AI, such as the challenges you may face during the AI journey, along with the ethical concerns, and the four phases that you can adopt to build your AI capabilities. I then discuss using a roadmap to develop an AI strategy, finding the best use cases for your project, and evaluating the AI/ML platforms and services from various cloud providers. It's like a step-by-step guide to your AI adventure.
Part III
: Planning and Launching a Pilot Project:
This part covers all the challenges and tasks centered on planning and launching a pilot project, including identifying use cases for your project, evaluating appropriate platforms and services, and launching the actual project.
Part IV
: Building and Governing Your Team:
People make magic happen!
Part IV
explores the organizational changes required to empower your workforce. I guide you through the steps to launching your pilot and assembling your dream team. It's all about nurturing the human side of things.
Part V
: Setting Up Infrastructure and Managing Operations:
In this part, you roll up your sleeves and get technical.
Part V
is like your DIY guide to building your own AI/ML platform. Here, I discuss the technical requirements and the daily operations of the platform with a focus on automation and scale. This part is a hands-on toolkit for those who are hungry to get geeky.
Part VI
: Processing Data and Modeling:
Data is the lifeblood of AI.
Part VI
is where you get your hands dirty with data and modeling. I teach you how to process data in the cloud, choose the right AI/ML algorithm based on your use case, and get your models trained, tuned, and evaluated. It is where the science meets the art.
Part VII
: Deploying and Monitoring Models:
Yay! It is launching time.
Part VII
guides you through the process of deploying the model into production for consumption. I also discuss the nuances of monitoring, securing, and governing models so they are working smoothly, safely, and securely.
Part VIII
: Scaling and Transforming AI:
You have built it, so now you can make it even bigger! In
Part VIII
, I present a roadmap to scale your AI transformation. I discuss how to take your game to the next level by introducing the AI maturity framework and establishing an AI COE. I also guide you through the process of building an AI operating model and transformation plan. This is where AI transitions from the project level to an enterprise-level powerhouse.
Part IX
: Evolving and Maturing AI:
This is where you peek into a crystal ball. I delve into the exciting world of generative AI, discuss where the AI space is headed, and provide guidance on how to continue your AI journey.
WHO SHOULD READ THIS BOOK?
This book is primarily meant for those serious about implementing AI at the enterprise level. It is for those focused on understanding and implementing AI within the context of enabling and executing upon an enterprise-wide AI strategy. It is a substantive, content-rich resource that requires focused reading and note-taking, with takeaways that you can go back to your company and start implementing. Below are some examples of roles that will benefit.
Data Scientists and AI Teams
This includes data scientists, ML engineers, data engineers, AI architects, and AI/ML project managers interested in learning about the technical and operational aspects of implementing AI in the cloud.
Data scientists and AI teams often struggle with scaling AI/ML models. This book provides comprehensive guidance on processing large volumes of data, choosing the right AI/ML algorithms, and building and deploying models in the cloud.
IT Leaders and Teams
This includes IT managers, cloud architects, IT consultants, system administrators, and solution architects who are responsible for the deployment and operation of AI systems and workloads using the cloud.
This book can help you build a scalable and robust AI infrastructure using cloud components. After reading this book, you can confidently choose the right cloud provider, manage the entire machine learning lifecycle, and integrate with the backend systems.
Students and Academia
This book helps students and people in academia learn about AI and its practical application beyond just theory. It is of particular relevance to those studying business, data science, computer science, and AI-related subjects. If you are looking for a comprehensive and up-to-date treatment of AI implementation, this book is for you.
I encourage you to read the entire book and supplement your reading with additional resources when needed.
Consultants and Advisors
This book will be of great help to consultants and professionals who advise executives, business and technology professionals about implementing AI for their companies. They are looking for best practices and a structured methodology.
Business Strategists and Leaders
These are people who may not be technically savvy but are interested in improving their business processes and strategies using AI.
C-Level Executives
This is a great book for C-level executives who want to learn about the strategic impact, business case, and execution of AI transformation at scale across the enterprise.
One of their major struggles is applying AI practically and profitably into their business processes and strategies. This book provides a detailed list of AI use cases and guides them to develop an AI strategy and business case, thus helping them make intelligent and informed decisions.
WHY YOU SHOULD READ THIS BOOK
We are living in a world where technology is changing rapidly and is impacting both our personal and business lives. In particular, AI and cloud technologies are moving at the speed of light, and businesses are racing to keep up.
For companies, AI and cloud together form the secret weapon to staying competitive, driving cost efficiency, innovating, and delivering outstanding customer experiences.
However, implementing these technologies is not easy. These technologies are constantly evolving with countless tools, platforms, strategies, and the added complexity of ethical considerations. Therefore, you need a comprehensive, practical guide that helps you to adopt these AI and cloud technologies successfully.
You need a guide that is not just theoretical but is also practical so that you can easily understand, access, and implement these technologies successfully. This book will act as your roadmap and as a co-pilot to drive the engine, namely, the cloud and AI technologies.
UNIQUE FEATURES
This book is a toolbox with tools to achieve end-to-end AI transformation. This isn't your regular technical book, and here's why.
Comprehensive Coverage of All Aspects of Enterprise-wide AI Transformation
Most books on AI and ML focus on one or two aspects of AI transformation, such as strategy, architecture, or operations. Most of them are focused on building and deploying machine learning models. However, this book covers the entire end-to-end process, from defining an AI strategy to identifying use cases, initiating projects, scaling your operations, and deploying and operating AI models in production. It elevates pet, PoC ML projects to real-world production-grade deployments.
Case Study Approach
I take a case study approach, using real-world examples to illustrate the concepts I discuss. I include a number of detailed strategies, step-by-step processes, templates, checklists, best practice tips, and hands-on exercises.
Coverage of All Major Cloud Platforms
I cover all the major cloud platforms, meaning AWS, Azure, and Google Cloud Platform. Most books focus on only one cloud provider or a specific aspect of AI and ML. But I cover them all, thus making this book a comprehensive guide to AI transformation.
Discussion of Nontechnical Aspects of AI
In addition to the technical aspects of AI and ML, I cover the nontechnical aspects, such as AI growth potential, team development, and AI-first strategy. I believe that it is important to understand the business implications of AI, as well as the technical aspects.
Best Practices for MLOps and AI Governance
I discuss best practices for MLOps and AI governance. MLOps is the practice of bringing together machine learning and DevOps, and AI governance is the process of ensuring that AI systems are used responsibly and ethically.
Up-to-Date Content
I believe that my book is the most comprehensive and up-to-date guide to AI transformation available. It is a must-read for anyone who wants to use AI to transform their business.
I wish you the best on your enterprise AI transformation journey. You can reach me at www.linkedin.com/in/rabijay1. You can also keep in touch with my latest progress via rabiml.com.
Hands-on Approach
The chapters in this book have been aligned to the various steps in an enterprise AI implementation initiative, starting all the way from Strategy to Execution to Post go-live operations. And each Chapter contains a number of review questions to cement the understanding of the topics covered in the book. In addition to that, I have added a number of hands-on exercises, best practice tips, and downloadable templates with examples to guide you in your enterprise-wide AI implementation.
PART IIntroduction
In this section, we dive into how enterprises are undergoing transformation through the adoption of AI using cloud technologies. I cover industry use cases for AI in the cloud and its benefits, as well as the current state of AI transformation. I also discuss various case studies of successful AI implementations, including the U.S. Government, Capital One, and Netflix.
1Enterprise Transformation with AI in the Cloud
The future of computing is at the edge, powered by AI and the cloud.
—Satya Nadella
Welcome to the exciting journey of enterprise transformation with AI in the cloud! This chapter is designed for anyone eager to understand the power and potential of AI in today's business landscape. You're probably here because you sense that AI isn't just another buzzword, but a game-changer. But how exactly can it transform your business? That's the question explored in this chapter.
UNDERSTANDING ENTERPRISE AI TRANSFORMATION
Enterprise transformation with AI in the cloud is about more than just adopting the latest technology; it's a holistic approach that redefines how your business operates, competes, and delivers value in the modern world. Through the integration of AI and machine learning with cloud computing, your business can revolutionize its processes, creating efficiencies, personalizing customer experiences, and fostering innovation like never before.
This chapter sets the stage for the entire book, explaining why some companies succeed with AI, and others fail, and how you can leverage AI to become a world-class, responsive, and innovative enterprise. It's not just about the technology; it's about reimagining what's possible in your business. Whether you're looking to automate processes, enhance customer experiences, or drive innovation, you'll find insights and practical exercises here to help you embark on this transformation.
If you're planning to follow the approach outlined in this book, the deliverables from the hands-on exercises and the downloadable templates in the accompanying website will become crucial building blocks as you progress through the later stages of implementing AI in your organization. Let's get started on this exciting journey!
NOTE ML stands for machine learning and includes generative AI.
Why Some Companies Succeed at Implementing AI and ML While Others Fail
Implementing AI and ML solutions has not been easy. A 2022 PWC Research report shows that only about 20 to 25 percent of the companies have been able to implement AI across the enterprise in a widespread manner (see Figure 1.1). The research also shows that the majority of the companies have launched limited AI use cases or pilots but have not been able to scale AI enterprise-wide. Implementing AI comes with several challenges around data preparation, building models, scalability, and ethical concerns. Companies need a systematic methodology to leverage several foundational capabilities across business and technology to implement AI and ML technologies across the enterprise. In this book, you learn how to adopt a step-by-step, practical approach to achieving enterprise-wide AI transformation.
FIGURE 1.1: Comparison of enterprise-wide AI adoption by leaders and others
Source: Adapted from www.pwc.com/us/en/tech-effect/ai-analytics/ai-business-survey.html
Transform Your Company by Integrating AI, ML and Gen AI into Your Business Processes
Companies now have in their hands a fantastic opportunity to change the way they operate. It is now possible to integrate AI, ML, and Generative AI in many of your business processes to attain outstanding results. For example, you can detect quality issues in production, translate documents, and even analyze the sentiments of your clients in a Twitter feed.
NOTE Enterprise AI transformation involves the implementation of end-to-end AI, ML, and Gen AI systems to drive business outcomes.
HANDS-ON CONCEPTUAL EXERCISE: UNDERSTANDING ENTERPRISE AI TRANSFORMATION
Define enterprise AI transformation and the importance of adopting AI and ML, including generative AI technologies for enterprises.
Adopt AI-First to Become World-Class
This chapter covers several crucial factors to consider for enterprise AI transformation, what it implies to be an AI-first company, and why cloud computing is a game-changer in implementing robust, scalable, and ethical AI. In the next chapter, I present three case studies and introduce the path these businesses took to become world-class AI-first organizations. The book's focus is to help you leverage these best practices across business, technology, process, and people domains so you, too, can excel as a professional and propel your company to become world-class, no matter what your role is. As you learn in subsequent chapters, everyone has a role to play.
Importance of an AI-First Strategy
The AI-first mindset introduces a new way to do business. When implementing AI transformation across the enterprise, it is essential to adopt this AI-first strategy and an AI-first approach. See Figure 1.2.
FIGURE 1.2: An AI-first strategy leads to an enterprise transformation.
An AI-first strategy is first a change in mindset that seeks to embrace AI to achieve business objectives. It involves identifying use cases to adopt AI, planning for proof of concepts (PoCs), and coming up with a plan to integrate AI into various aspects of an organization, such as customer service, product development, and decision-making. It includes building the necessary infrastructure, tools, and skills; building a commitment to learning; partnering with experts and vendors to be at the forefront of AI; and embracing AI for business opportunities.
Prioritize AI and Data Initiatives
Having this AI-first strategy helps organizations prioritize AI and data initiatives ahead of other projects, embrace AI technologies to stay at the forefront of innovation, and identify new business opportunities with the help of data.
It also promotes greater collaboration between IT and business and gives companies a competitive advantage to drive innovation, growth, and profitability.
If you do not adopt the AI-first strategy, you run the risk of losing your competitive advantage. You may not use the resources effectively, continue to live in silos between the business and IT, and, more importantly, lose out on opportunities to drive innovation, growth, and increased profits.
Adopting an AI-first strategy is the scope of this book. By following the steps outlined in this book, you learn to implement an AI-first strategy in your company.
HANDS-ON EXERCISE: AI-FIRST STRATEGY FROM CONCEPTS TO APPLICATIONS
Explain what AI-first strategy means and mention at least three benefits of an AI-first strategy.
Using Netflix or any other case study, explain how the adoption of cloud and AI technologies led to process transformation.
Explain the role of cloud computing in implementing robust, scalable, and ethical AI.
LEVERAGING ENTERPRISE AI OPPORTUNITIES
The rise of AI and the recent developments in generative AI have been incredible. Let's discuss the reasons behind such a surge.
One of the main reasons it has taken off is the cloud computing factor. The other two pillars of this phenomenal growth in AI are the growth of big data and the power of computer algorithms to build models (see Figure 1.3). However, the common denominator contributing to this growth is cloud computing.
FIGURE 1.3: The triumvirate of AI: cloud computing, big data, and software algorithms
Source: Regormark/Adobe Stock
Enable One-to-One, Personalized, Real-Time Service for Customers at Scale
We are currently seeing two trends in the modern world. One is that customers constantly demand immediacy and relevancy in service. Moreover, they want to be able to communicate with their service providers in real time. This, combined with the desire of companies to leverage data to constantly improve their products and services and renovate to build new business models, has been an exciting trend.
To provide this level of personalized service that scales in real time with such large amounts of data, it is no longer possible to rely on traditional rule-based programming. This is where the increased, scalable power of cloud computing powers with large amounts of data streaming from sensors to create new possibilities.
ML algorithms can crunch the same datasets in just a few minutes, something that took thousands of hours before. This capability makes it possible to provide one-to-one personalized service to your customers rather than segment-based marketing.
Companies are no longer tethered to their physical data centers. Thanks to Amazon, Google, and Microsoft's cloud services, companies can now set up a lot of computing and networking power in minutes.
The cloud provides machine learning frameworks (such as MXNet, TensorFlow, and PyTorch) and AI services (such as transcription, image recognition, and speech recognition) that anyone can leverage to do intense machine learning computations to predict, classify, and enable quick actions.
Recently, generative AI has caught the attention of many. It was made possible by the underlying computing power that the cloud technologies provide, along with the data infrastructure to process petabytes of data to train these models.
The Network Effect: How AI-Powered Services Create a Virtuous Cycle of Value Generation
Another reason for this explosive growth of AI is the network effect, which comes from the fact that you are quickly providing value to the customers, who, in turn, are generating more data and generating more insights. The generation of new insights allows you to provide better services, generating more data and creating a virtuous cycle (see Figure 1.4).
FIGURE 1.4: The network effect
Leverage the Benefits of Machine Learning for Business
Companies have realized that machine learning can help their businesses reduce costs, increase revenue, and drive innovation. It does create demand for large amounts of computing network and storage power that can be provided only through cloud computing. Moreover, 90 percent of their costs come from the inference stage of their machine learning lifecycle.
Cloud Computing Meets Unique ML Hardware Demands
Different machine learning use cases, such as forecasting and image recognition, place different demands on the underlying hardware, which make it challenging to meet the workload demands with just a standard type of underlying infrastructure.
NOTE The need for different architectures for different use cases has forced companies to rely increasingly on cloud computing, which can meet these unique demands on infrastructure.
Cloud Computing: An Accelerator for the Growth of AI
Cloud computing is scalable, easily accessible from anywhere, and cost-effective. Cloud infrastructure can be easily integrated with backend systems, allowing models to be hosted in on-prem, cloud, or hybrid mode.
All these factors make cloud computing a significant disruptor and a powerful force that all companies must leverage.
HANDS-ON RESEARCH PRESENTATION EXERCISE: EXPLORING THE CLOUD AND ITS IMPACT ON AI
Objective: Exploring the cloud and its impact on AI.
Step 1:
Divide the students into groups and have them research the following:
Relationship between cloud computing and big data on AI
Popular cloud-based machine learning frameworks such as PyTorch, MXNet, TensorFlow
Cloud and AI services
Network effects
Cloud computing and its impact on various use cases
Step 2:
Prepare a paper on their findings with real-world examples and future trends. Discuss how it could impact their careers.
Enterprise-wide AI Opportunities
Speaking of AI opportunities, you can categorize AI opportunities as enterprise-wide and industry-specific opportunities. Let's discuss enterprise AI opportunities first.
Automate Processes for Increased Efficiency and Cost Reduction
You can integrate AI into existing processes to increase operational efficiency, reduce costs, and free up human time through optimization and automation.
PROCESS AUTOMATION USE CASE EXAMPLES
Customer complaint categorization
Some examples of automation include sorting out and categorizing incoming customer complaints using natural language processing to understand the content and classify it as technical issues, shipping issues, or billing issues.
Computer vision systems in quality control
Computer vision systems can be used to analyze videos and images to spot defects in products in a car assembly or computer manufacturing plant.
Optimize Business Processes with AI
AI can also be used to optimize business processes, which is much broader than automation. It includes analyzing current processes to identify improvement opportunities, such as predicting demand, forecasting resource needs, and improving team communication. See Figure 1.5.
AI Applications for Improving Customer Experience
AI can help improve customer experience through the following:
CUSTOMER SERVICE USE CASE EXAMPLES
Personalization
Using predictive analytics to analyze past customer interactions, AI can better understand customers' needs and behavior and recommend personalized offers and product solutions to problems.
Improved customer service via chatbots
Better customer service through chatbots using NLP to answer queries 24/7, route to the right person, and schedule appointments.
Targeted marketing campaigns
Targeted marketing campaigns by analyzing customer data to generate relevant messages and thus increase customer loyalty.
Customer recognition with computer vision and speech recognition
Using computer vision and speech recognition to recognize customers on the website and contact centers and to tailor the experience accordingly.
Sentiment analysis
Conduct sentiment analysis of reviews, complaints, and surveys to recommend solutions to problems.
Speech recognition for call and chat analysis
Use speech recognition to analyze phone calls and chat to identify areas of improvement as well as to train agents.
FIGURE 1.5: Enterprise AI opportunities
Identify New Customer Needs and Develop Innovative Products and Services
AI can identify new customer needs to develop new products and services by analyzing social media, purchase history, reviews, complaints, and browsing behavior.
Use Case Example
A financial company may find that customers are paying a third-party service higher fees to move money overseas, in which case the company can create a money transfer service that's cheaper and better quality.
INNOVATION USE CASES
Cross-selling and bundling products
Companies can use AI algorithms such as recommendation systems to discover that customers who buy certain products also buy other related products. Hence, this gives the company the opportunity to cross-sell or bundle the products for sale.
Competitor analysis
Companies can analyze competitors' products, pricing, and customer reviews to identify customer pain points to meet a need in the marketplace.
Network analysis for business expansion
Companies can use network analysis of internal and external partners and individuals to identify influential clusters in gaining access to more business ideas and new customers and contact them via social media.
Enhance Employee Collaboration and Decision-Making with AI-Powered Tools and Workflows
Consider the following use cases that promote employee collaboration and decision-making:
EMPLOYEE COLLABORATION AND DECISION-MAKING USE CASES
Enhanced collaboration via chatbots and predictive insights
Using chatbots, better decision-making, and predictive insights, employees can collaborate more effectively to drive better business outcomes.
AI-powered workflows for complex projects
Employees can use AI-powered workflows to collaborate more effectively on complex projects. This can help automate routine tasks so employees can work more strategically.
Analysis of project management and collaboration tools
By analyzing project management tools, collaboration tools, and productivity apps, AI can explain why breakdowns happen along with solutions. AI can recommend which employee is best suited for a job.
Videoconferencing for remote collaboration
Employees can use AI-powered videoconferencing to work remotely and collaborate virtually from anywhere. AI can recommend ideal times to have calls.
Transform Compliance Processes by Streamlining Documentation, Risk Assessment, and Audit
Here are some compliance-related use cases:
COMPLIANCE-RELATED USE CASES
Automating compliance processes
AI can automate compliance processes such as documentation, audits, and risk assessment to reduce human errors and help employees use time more strategically.
Predictive analytics for proactive issue identification
You can use predictive analytics to identify issues proactively before they happen and deal with individuals and departments accordingly.
Chatbots and virtual assistants for compliance
You can also use chatbots and virtual assistants to answer compliance questions from employees.
Changing compliance requirements
AI can help keep pace with changing compliance requirements by staying up-to-date and proposing improvement areas in compliance programs.
CASE STUDY ANALYSIS: EVALUATING AI OPPORTUNITIES ACROSS INDUSTRIES
Objective: Evaluate the practical AI opportunities across industries.
Materials: HBR case collection, Stanford Graduate School of Business case collection, and so on.
Step 1:
Assign a student or group of students to an industry.
Step 2:
Choose a real-world company from an industry and assess the following:
Specific AI opportunities, technologies, and applications used by the company and the purpose or problem they solve
AI's impact on people, processes, products, services, and customer experiences
Challenges faced by the company during the implementation
Measurable outcomes or benefits from the AI implementation
Growing Industry Adoption of AI
Different industries are at different stages of adoption of AI depending upon the availability of the data, the complexity of the industry, and the compliance requirements. Table 1.1 shows the leading companies across pure play AI, leading pioneers, visionaries, industry leads, and enterprise majors, as well as leaders in generative AI.
TABLE 1.1: Companies Leading the Way in Adopting AI
AI GIANTS
PIONEERS
VISIONARIES
GENERATIVE AI
ENTERPRISE MAJORS
INDUSTRY
Microsoft Amazon AWS Google IBM Baidu Oracle Alibaba NVIDIA
OpenAI c3.ai H2O.ai DataRobot Snowflake Dataiku RapidMiner Databricks Alteryx Cloudera
Adept Synthesia Cohere
Abacus.ai
Runway Anthropic
Rephrase.ai
Midjourney
Infinity.ai
Podcast.ai
Hugging Face Stability AI Jasper
Salesforce BMC Software HPE Dell SAP ServiceNow Broadcom SAS Informatica
Medtronic GE Healthcare Capital One Carnegie Learning Century Tech Duolingo Crowdstrike Palo Alto Shelf Engine McDonald's
Fraud Detection, Risk Management, and Customer Service in the Finance and Insurance Industries
Here are some finance and insurance industry–related use cases:
FINANCE AND INSURANCE INDUSTRY–RELATED USE CASES
Fraud detection, risk management, and customer service
AI is widely adopted in the finance industry for use cases such as fraud detection, risk management, and customer service.
Loan processing by smart robots
Human agents are now being replaced by smart robots that can process loans in just a few seconds.
Robo-advisors for investment decisions
Financial advisors are being replaced by robots that can process large amounts of data to make the right investment decisions for customers. These robots are smart enough to analyze data from social media, emails, and other personal data.
Claims processing and product recommendation in insurance
AI is used in the insurance industry to reduce claims processing time and recommend insurance plans and products based on customer data.
Revolutionizing Healthcare with AI: From Diagnoses to Robotic Surgery
The healthcare industry was an early adopter of AI.
AI is used for medical image analysis, personalized medicine, and drug discovery.
AI is also used for diagnosing diseases and treatment, medication management, and robotic surgeries.
NOTE Adoption of AI can enable better patient outcomes such as better health due to better diagnosis and treatment, reduced costs due to reduced patient re-admissions, and increased operational efficiency.
HEALTHCARE USE CASES
Medical image analysis, personalized medicine, and drug discovery
AI is used for medical image analysis, personalized medicine, and drug discovery.
Disease diagnosis, treatment, and robotic surgeries
AI is also used for diagnosing diseases and treatment, medication management, and robotic surgeries.
Improving patient outcomes
Adoption of AI can enable better patient outcomes such as better health due to better diagnosis and treatment, reduced costs due to reduced patient re-admissions, and increased operational efficiency.
Transforming Manufacturing with Predictive Maintenance, Quality Control, and Collaborative Robots
In the manufacturing industry, AI is quite widely used.
MANUFACTURING USE CASES
Predictive maintenance, quality control, and supply chain optimization
AI is used for predictive maintenance, quality control, and supply chain optimization.
Improving production operations
It is used to improve production operations ranging from workforce planning to product design to defect management and employee safety.
Collaborative robots (cobots)
This industry is also seeing the advent of
cobots
, which are robots that work collaboratively with humans.
Revolutionizing Retail: AI-Powered Customer Service, Personalized Marketing, and Inventory Control
In the retail industry, AI is used for customer service chatbots, personalized marketing, inventory control, and forecasting demand.
Use Case Example
Amazon is a classic example of how AI can be used to provide product recommendations based on the user's browsing behavior.
Autonomous Vehicles, Predictive Maintenance, and Traffic Management in the Transportation Industry
In the transportation industry, AI is used for autonomous vehicles, predictive maintenance, and traffic route management.
TRANSPORTATION USE CASES
Self-driving cars
Active research is being carried out by Tesla, Volvo, Uber, and Volkswagen, and though it's used in controlled situations, pretty soon we will be seeing self-driving cars more commonly on the road.
Navigational and driver assistance systems
Some of the practical applications of AI are navigating obstacles and roads, driver assistance systems such as adaptive cruise control, automatic emergency braking, lane departure warning, radars, and cameras to enable safer driving.
Warehouse automation and shipment optimization
AI-powered robots are sorting out products in warehouses for delivery, moving cases within warehouses, delivering for pallet building, and so on. AI algorithms are used for shipment route determination and optimization for cost minimization.
Public transport management
Even in public transport, AI is used for traffic light management, transportation scheduling, and routing.
From Pizza Quality to Smart Farming in Food and Agriculture
AI is also used in food tech in the following ways:
FOOD AND AGRICULTURE USE CASES
Taking orders and serving food
AI is used to take orders and serve food.
Quality control in food preparation
It is even used to ensure the quality of pizzas.
Crop management in agriculture
In agriculture, it is used to raise crops by analyzing salinity, heat, UV light, and water.
Smart machinery in farming
Smart tractors and plucking machines are also being used in the farming sector.
Predicting Prices, Automating Processes, and Enhancing Customer Experience in Real Estate
Here are some AI examples in real estate:
REAL ESTATE USE CASES
Price prediction, buyer-seller matching, and market analysis
Predict prices and rental income, match buyers with sellers, analyze market conditions and trends, process real estate documents automatically, and use chatbots to answer customer queries
Automated document processing and customer support
Smart home technology for increased security and energy efficiency in the home
Smart home technology
Voice assistants like Alexa, Google Assistant, and Siri; smart appliances like refrigerators, washing machines, thermostats, smart lighting, home security systems
AI-Powered Personalization in Entertainment: Netflix, Amazon, and the Gaming Industry
Here are some use cases in the entertainment industry:
ENTERTAINMENT AND GAMING INDUSTRY
Recommendation systems in streaming platforms
