Master the application of artificial intelligence in your enterprise with the book series trusted by millions
In Enterprise AI For Dummies , author Zachary Jarvinen simplifies and explains to readers the complicated world of artificial intelligence for business. Using practical examples, concrete applications, and straightforward prose, the author breaks down the fundamental and advanced topics that form the core of business AI.
Written for executives, managers, employees, consultants, and students with an interest in the business applications of artificial intelligence, Enterprise AI For Dummies demystifies the sometimes confusing topic of artificial intelligence. No longer will you lag behind your colleagues and friends when discussing the benefits of AI and business.
The book includes discussions of AI applications, including
- Streamlining business operations
- Improving decision making
- Increasing automation
- Maximizing revenue
The For Dummies series makes topics understandable, and as such, this book is written in an easily understood style that's perfect for anyone who seeks an introduction to a usually unforgiving topic.
Introduction 1
About This Book 2
Strong, Weak, General, and Narrow 2
Foolish Assumptions 3
Icons Used in This Book 4
Beyond the Book 4
Where to Go from Here 5
Part 1: Exploring Practical AI and How It Works 7
Chapter 1: Demystifying Artificial Intelligence 9
Understanding the Demand for AI 11
Converting big data into actionable information 11
Relieving global cost pressure 13
Accelerating product development and delivery 14
Facilitating mass customization 14
Identifying the Enabling Technology 14
Processing 15
Algorithms 15
Data 16
Storage 18
Discovering How It Works 18
Semantic networks and symbolic reasoning 19
Text and data mining 20
Machine learning 22
Auto-classification 24
Predictive analysis 25
Deep learning 26
Sentiment analysis 27
Chapter 2: Looking at Uses for Practical AI 29
Recognizing AI When You See It 30
ELIZA 30
Grammar check 30
Virtual assistants 30
Chatbots 31
Recommendations 31
Medical diagnosis 32
Network intrusion detection and prevention 33
Fraud protection and prevention 34
Benefits of AI for Your Enterprise 34
Healthcare 35
Manufacturing 36
Energy 36
Banking and investments 37
Insurance 37
Retail 38
Legal 39
Human resources 39
Supply chain 40
Transportation and travel 40
Telecom 41
Public sector 41
Professional services 42
Marketing 43
Media and entertainment 43
Chapter 3: Preparing for Practical AI 45
Democratizing AI 46
Visualizing Results 46
Comparison 46
Composition 47
Distribution 48
Relationship 48
Digesting Data 50
Identifying data sources 52
Cleaning the data 52
Defining Use Cases 54
A B 55
Good use cases 55
Bad use cases 56
Reducing bias 58
Choosing a Model 59
Unsupervised learning 59
Supervised learning 60
Deep learning 60
Reinforcement learning 61
Chapter 4: Implementing Practical AI 63
The AI Competency Hierarchy 63
Data collection 63
Data flow 64
Explore and transform 64
Business intelligence and analytics 64
Machine learning and benchmarking 65
Artificial intelligence 65
Scoping, Setting Up, and Running an Enterprise AI Project 65
Define the task 67
Collect the data 68
Prepare the data 69
Build the model 70
Test and evaluate the model 72
Deploy and integrate the model 72
Maintain the model 72
Creating a High-Performing Data Science Team 73
The Critical Role of Internal and External Partnerships 74
Internal partnerships 74
External partnerships 75
The importance of executive buy-in 75
Weighing Your Options: Build versus Buy 75
When you should do it yourself 75
When you should partner with a provider 77
Hosting in the Cloud versus On Premises 77
What the cloud providers say 78
What the hardware vendors say 78
The truth in the middle 78
Part 2: Exploring Vertical Market Applications 81
Chapter 5: Healthcare/HMOs: Streamlining Operations 83
Surfing the Data Tsunami 84
Breaking the Iron Triangle with Data 84
Matching Algorithms to Benefits 86
Examining the Use Cases 87
Delivering lab documents electronically 87
Taming fax 88
Automating redaction 88
Improving patient outcomes 89
Optimizing for a consumer mindset 89
Chapter 6: Biotech/Pharma: Taming the Complexity 91
Navigating the Compliance Minefield 92
Weaponizing the Medical, Legal, and Regulatory Review 93
MLR review for product development 93
MLR review for sales and marketing 94
Enlisting Algorithms for the Cause 95
Examining the Use Cases 96
Product discovery 96
Clinical trials 96
Product development 96
Quality control 97
Predictive maintenance 97
Manufacturing logistics 97
Regulatory compliance 98
Product commercialization 98
Accounting and finance 98
Chapter 7: Manufacturing: Maximizing Visibility 99
Peering through the Data Fog 100
Finding ways to reduce costs 100
Handling zettabytes of data 101
Clearing the Fog 101
Connected supply chain 102
Proactive replenishment 103
Predictive maintenance 104
Pervasive visibility 104
Clarifying the Connection to the Code 106
Optimize inventory 106
Optimize maintenance 106
Optimize supply chain 106
Improve quality 106
Automate repetitive tasks 107
Examining the Use Cases 107
Minimize risk 107
Maintain product quality 107
Streamline database queries 108
Outsource predictive maintenance 108
Customize products 109
Expand revenue streams 109
Save the planet 109
Delegate design 110
Chapter 8: Oil and Gas: Finding Opportunity in Chaos 111
Wrestling with Volatility 111
Pouring Data on Troubled Waters 112
Deriving meaningful insights 113
Regaining control over your data 113
Wrangling Algorithms for Fun and Profit 114
Examining the Use Cases 115
Achieving predictive maintenance 115
Enhancing maintenance instructions 115
Optimizing asset performance 116
Exploring new projects 116
Chapter 9: Government and Nonprofits: Doing Well by Doing Good 119
Battling the Budget 120
Government 120
Nonprofit 122
Fraud 122
Optimizing Past the Obstacles 123
Digital transformation 123
The future of work 124
Data security 125
Operational costs 125
Fraud 125
Engagement 126
Connecting the Tools to the Job 128
Examining the Use Cases 129
Enhance citizen services 129
Provide a global voice of the citizen 130
Make your city smarter 130
Boost employee productivity and engagement 131
Find the right employees (and volunteers) 131
Improve cybersecurity 132
Chapter 10: Utilities: Renewing the Business 133
Coping with the Consumer Mindset 134
Utilizing Big Data 135
The smart grid 135
Empowering the organization 136
Connecting Algorithms to Goals 136
Examining the Use Cases 137
Optimizing equipment performance and maintenance 137
Enhancing the customer experience 137
Providing better support 138
Streamlining back-office operations 138
Managing demand 139
Chapter 11: Banking and Financial Services: Making It Personal 141
Finding the Bottom Line in the Data 142
Moving to "open banking" 142
Dealing with regulation and privacy 143
Offering speedier service 144
Leveraging Big Data 144
Restructuring with Algorithms 145
Examining the Use Cases 146
Improving personalization 146
Enhancing customer service 146
Strengthening compliance and security 147
Chapter 12: Retail: Reading the Customer's Mind 149
Looking for a Crystal Ball 150
Omnichanneling 150
Personalizing 151
Reading the Customer's Mail 152
A fluid omnichannel experience 153
Enhanced personalization 153
Accurate forecasting 153
Looking Behind the Curtain 154
Examining the Use Cases 155
Voice of the customer 155
Personalized recommendations 155
AI-powered inventory 156
Chapter 13: Transportation and Travel: Tuning Up Your Ride 157
Avoiding the Bumps in the Road 158
Planning the Route 159
Checking Your Tools 161
Examining the Use Cases 162
Autonomous vehicles 162
Predictive maintenance 162
Asset performance optimization 163
Enhanced driver and passenger experiences 164
Chapter 14: Telecommunications: Connecting with Your Customers 167
Listening Past the Static 168
Finding the Signal in the Noise 168
Looking Inside the Box 169
Examining the Use Cases 170
Achieve predictive maintenance and network optimization 170
Enhance customer service with chatbots 170
Improve business decisions 171
Chapter 15: Legal Services: Cutting Through the Red Tape 173
Climbing the Paper Mountain 173
Reading and writing 174
And arithmetic 175
Foot in mouth disease 175
Planting Your Flag at the Summit 175
Linking Algorithms with Results 177
Examining the Use Cases 178
Discovery and review 178
Predicting cost and fit 179
Analyzing data to support litigation 180
Automating patent and trademark searches 180
Analyzing costs for competitive billing 180
Chapter 16: Professional Services: Increasing Value to the Customer 181
Exploring the AI Pyramid 182
Climbing the AI Pyramid 183
Unearthing the Algorithmic Treasures 184
Healthcare 184
Content management 184
Compliance 185
Law 185
Manufacturing 186
Oil and gas 186
Utilities 186
Examining the Use Cases 187
Document intake, acceptance, digitization, maintenance, and management 187
Auditing, fraud detection, and prevention 187
Risk analysis and mitigation 187
Regulatory compliance management 188
Claims processing 188
Inventory management 188
Resume processing and candidate evaluation 188
Chapter 17: Media and Entertainment: Beating the Gold Rush 189
Mining for Content 190
Asset management 190
Metadata 191
Distribution 191
Silos 192
Content compliance 192
Striking It Rich 193
Metadata 193
Digital distribution 193
Digital asset management 194
Assaying the Algorithms 194
Examining the Use Cases 195
Search optimization 195
Workflow optimization 196
Globalization 196
Part 3: Exploring Horizontal Market Applications 197
Chapter 18: Voice of the Customer/Citizen: Finding Coherence in the Cacophony 199
Hearing the Message in the Media 200
Delivering What They Really Want 201
Answering the Right Questions 203
Examining Key Industries 204
Consumer packaged goods 205
Public and nonprofit organizations 205
Chapter 19: Asset Performance Optimization: Increasing Value by Extending Lifespans 207
Spying on Your Machines 208
Fixing It Before It Breaks 209
Learning from the Future 210
Data collection 210
Analysis 211
Putting insights to use 212
Examining the Use Cases 212
Production automation and quality control 213
Preventive maintenance 213
Process optimization 215
Chapter 20: Intelligent Recommendations: Getting Personal 217
Making Friends by the Millions 218
Listening to social media 218
Mining data exhaust 219
Reading Minds 219
Knowing Which Buttons to Push 219
Popular product recommendation 220
Market-basket analysis 220
Propensity modeling 220
Data and text mining 222
Collaborative filtering (CF) 223
Content-based filtering (CBF) 224
Cross-validation 224
Data visualization 225
Examining Key Industries 226
Finance 226
Credit card offers 227
Retail 228
Chapter 21: Content Management: Finding What You Want, When You Want It 231
Introducing the Square Peg to the Round Hole 232
Categorizing and organizing content 232
Automating with AI 233
Finding Content at the Speed of AI 233
Expanding Your Toolbox 235
Access the content 235
Extract concepts and entities 235
Categorize and classify content 236
Automate or recommend next best actions 236
Examining the Use Cases 236
Legal discovery process 237
Content migration 237
PII detection 237
Chapter 22: AI-Enhanced Content Capture: Gathering All Your Eggs into the Same Basket 239
Counting All the Chickens, Hatched and Otherwise 240
Tracing the history of capture technology 240
Moving capture technology forward 241
Monetizing All the Piggies, Little and Otherwise 241
Streamline back-office operations 242
Improve compliance 242
Reduce risk of human error 243
Support business transformation 243
Improve operational knowledge 243
Getting All Your Ducks in a Row 244
Capture 244
Digitize where needed 244
Process, classify, and extract 244
Validate edge cases 245
Manage 246
Visualize 246
Examining Key Industries 246
Financial services 246
State government 247
Healthcare 247
Chapter 23: Regulatory Compliance and Legal Risk Reduction: Hitting the Bullseye on a Moving Target 249
Dodging Bullets 250
Fines 250
Increasing regulation 252
Data privacy 254
Strategy 254
Shooting Back 255
Make better decisions 255
Increase customer confidence 256
Win more business 257
Boost the bottom line 257
Building an Arsenal 258
Examining the Use Cases 259
Manage third-party risk 259
Manage operational risk 259
Monitor compliance risk 260
Monitor changes in regulations 261
Maintain data privacy 261
Maintain data security 262
Detect fraud and money laundering 262
Optimize workflow 263
Chapter 24: Knowledge Assistants and Chatbots: Monetizing the Needle in the Haystack 265
Missing the Trees for the Forest 266
Recognizing the problem 266
Defining terms 267
Hearing the Tree Fall 268
Making Trees from Acorns 269
Examining the Use Cases 270
Customer support 270
Legal practice 271
Enterprise search 272
Compliance management 272
Academic research 272
Fact checking 273
Chapter 25: AI-Enhanced Security: Staying Ahead by Watching Your Back 275
Closing the Barn Door 276
The story in the statistics 276
The state of current solutions 278
Locking the Barn Door 279
Knowing Which Key to Use 281
Examining the Use Cases 283
Detecting threats by matching a known threat marker 284
Detecting breaches by identifying suspicious behavior 284
Remediating attacks 286
Part 4: The Part of Tens 287
Chapter 26: Ten Ways AI Will Influence the Next Decade 289
Proliferation of AI in the Enterprise 290
AI Will Reach Across Functions 291
AI R&D Will Span the Globe 291
The Data Privacy Iceberg Will Emerge 292
More Transparency in AI Applications 292
Augmented Analytics Will Make It Easier 293
Rise of Intelligent Text Mining 293
Chatbots for Everyone 294
Ethics Will Emerge for the AI Generation 294
Rise of Smart Cities through AI 294
Chapter 27: Ten Reasons Why AI is Not a Panacea 297
AI is Not Human 298
Pattern Recognition is Not the Same As Understanding 299
AI Cannot Anticipate Black Swan Events 300
AI Might Be Democratized, but Data is Not 302
AI is Susceptible to Inherent Bias in the Data 302
#RacialBias 303
#GenderBias 303
#EthnicBias 303
Collection bias 304
Proxy bias 304
AI is Susceptible to Poor Problem Framing 305
AI is Blind to Data Ambiguity 306
AI Will Not, or Cannot, Explain Its Own Results 307
AI sends you to jail 307
AI cuts your medical benefits 308
AI and the black box 308
AI diagnoses your latent schizophrenia 309
AI can be fooled 310
AI is Not Immune to the Law of Unintended Consequences 311
Index 313 Zachary Jarvinen, MBA/MSc is a product & marketing executive and sought-after author and speaker in the Enterprise AI space. Over the course of his career, he's headed up Technology Strategy for Artificial Intelligence and Analytics at OpenText, expanded markets for Epson, worked at the U.S. State Department, and was a member of the 2008 Obama Campaign Digital Team. Presently, Zachary is focused on helping organizations get tangible benefits from AI.
