Ai in the Enterprise for Dummies

  • ISBN13:


  • ISBN10:


  • Format: Paperback
  • Copyright: 2020-08-25
  • Publisher: For Dummies

Note: Supplemental materials are not guaranteed with Rental or Used book purchases.

Purchase Benefits

  • Free Shipping Icon Free Shipping On Orders Over $35!
    Your order must be $35 or more to qualify for free economy shipping. Bulk sales, PO's, Marketplace items, eBooks and apparel do not qualify for this offer.
  • eCampus.com Logo Get Rewarded for Ordering Your Textbooks! Enroll Now
List Price: $34.99 Save up to $5.07
  • Rent Book $29.92
    Add to Cart Free Shipping Icon Free Shipping

    *This item is part of an exclusive publisher rental program and requires an additional convenience fee. This fee will be reflected in the shopping cart.

Supplemental Materials

What is included with this book?


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.

Author Biography

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.

Table of Contents

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


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

Rewards Program

Reviews for Ai in the Enterprise for Dummies (9781119696292)