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[REPORT PREVIEW] AI in the Enterprise

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[REPORT PREVIEW] AI in the Enterprise

  1. 1. AI in the Enterprise Real Strategies for Artificial Intelligence JUNE 12, 2018 BY SUSAN ETLINGER RESEARCH REPORT PREVIEW VERSION
  2. 2. 1 Table of Contents 2 Executive Summary 3 AI: Where We Are Now 5 Use Cases for Artificial Intelligence 8 Stripe Case Study: Improving Customer Experience While Preventing Fraud 14 DBS Case Study: AI Supporting Digital Transformation 17 Recommendations 20 Endnotes 22 Methodology 23 About Us 23 How to Work With Us
  3. 3. 2 Executive Summary For enterprise companies considering investing in AI and implementing AI applications, the current landscape can seem overwhelming. Companies like Amazon, Facebook, Google, Apple, and Microsoft dominate the news, but how applicable are their strategies to companies with vastly different business models? This report examines the real use cases, challenges, and opportunities of AI for organizations. It includes interviews with executives from large, well-known companies and start-up entrepreneurs who are envisioning the many ways that machine intelligence can fuel innovation and growth. Finally, the report offers recommendations for companies thinking about where to focus, how to build their partnership ecosystem, and how to measure value in the short and long term as AI becomes a critical driver of digital transformation.
  4. 4. 3 AI: Where We Are Now Progress toward developing and utilizing Artificial Intelligence (AI) has been uneven in the years since Alan Turing asked the question, “Can machines think?” But after seven decades of research and sporadic progress, AI is finally coming into its own. The availability of massive amounts of data, relatively inexpensive parallel processing, and improved algorithms have sparked technology and market momentum that is different from the critical yet discrete breakthroughs of the past. Against this backdrop, investment in AI continues to escalate, with explosive growth expected during the next several years. Industry research firm IDC expects global spending on cognitive and AI solutions to achieve a Compound Annual Growth Rate (CAGR) of 54.4% through 2020, when revenues will exceed $46 billion. Statista forecasts that the global AI market will reach $59.75B by 2025.1 This combination of innovation, experience, and investments means that AI is poised to gain momentum and become a core driver of growth in the enterprise.
  5. 5. 4 DEFINING ARTIFICIAL INTELLIGENCE Defining exactly which technologies comprise “AI” can be a contentious process. This report defines AI in a business context as follows: Artificial intelligence refers to a set of technologies that enable machines to reproduce certain types of human capabilities; for example, the ability to see, listen, speak, move, reason, decide, predict, act, and — most importantly — learn from past experience. Today, AI is used in text messaging, search, eCommerce, social media, and in vertical industries from heavy manufacturing to financial services, healthcare and retail. Sometimes we are aware of its presence — as with Alexa or an autonomous car — and other times we aren’t, because it’s working behind the scenes in websites, apps, messaging, search, and a range of other tools and services. RECENT TECHNOLOGY ADVANCEMENTS Driven by research and experimentation in academia, startups, and large companies, advancements in AI continue to accelerate in many notable ways, including the following: These advancements hint at the ways that AI will evolve further in the future. As part of this evolution, it is important to understand the different types of AI and how they are being implemented across industries globally. • • • Language Understanding and Translation In 2016, researchers at Microsoft announced that their speech recognition technology had achieved human parity; that is, the ability to transcribe speech about as well as a human. In March 2017, Microsoft announced a similar breakthrough in Chinese-to-English translation.2 In May 2018, Google demonstrated its “Duplex” technology, which enables a voice agent to conduct a naturalistic conversation over the phone. AI- based systems will eventually be able to recognize, translate, and speak or chat in any language at any time on any device. Strategy and Decision-Making DeepMind unveiled AlphaGo Zero, the first computer program to defeat a world champion at the ancient Chinese game of Go.3 Maturing Deep Learning Frameworks Deep learning frameworks, such as TensorFlow, which enable developers to build AI-enabled systems more quickly, consistently, and scalably, are becoming more accessible and user-friendly. This lowers barriers to entry for programmers to experiment with and use AI for a broader range of business use cases.4
  6. 6. 5 Use Cases for Artificial Intelligence Although artificial intelligence enables machines to reproduce certain types of human capabilities or “intelligences,” it’s critical to remember how machines fundamentally differ from human beings. Today, at least, machines excel at certain things (computation and pattern matching, for example), but lack the attributes we think of as innately human (feelings, context, values). To approximate these attributes in machines, we must “train” them with massive amounts of data so they can recognize objects, languages, and relationships and understand things that humans take for granted. While some futurists believe in the idea of a “technological singularity” in which machines will overtake the capabilities of humans, the question today is more pragmatic: How can we use AI? What is it good for and not as good for? Where are the real opportunities and use cases? What are the moon shots? How will it impact customers, employees, and shareholders? To assess the types of use cases that AI is best equipped to handle, it’s important to think about how human intelligence and computer intelligence compare to each other. “The Theory of Multiple Intelligences,” proposed in 1983 by developmental psychologist Howard Gardner, offers a useful guide. Rather than thinking of intelligence as a single attribute, it proposes nine specific types of intelligence that humans possess:5 1. Logical-mathematical (number/reasoning smart) 2. Naturalist (nature smart) 3. Musical (sound smart) 4. Bodily-kinesthetic (body smart) 5. Linguistic (word smart) 6. Spatial (picture smart) 7. Interpersonal (people smart) 8. Intra-personal (self smart) 9. Existential (life smart)
  7. 7. 6 What Gardner refers to as “logical-mathematical” intelligence is the first thing we think of when we think about machine intelligence: the ability to classify data, process it, draw inferences from it, and even make decisions. But machines can also “see” (using computer vision); listen (using Natural Language Understanding [NLU] technologies); walk, run, jump, and fly (using robotics); communicate (using audio and NLU); detect and interpret environmental changes (using sensors and analytics); and a host of other things. The key is to use these capabilities in a way that is valuable to people and businesses. Shivon Zilis’ “Machine Intelligence 3.0” is an excellent framework for understanding the AI technology landscape (see Figure 1).6 On the left are applications enabled by AI and, on the right, the continuously evolving technology stack. Figure 1: Machine Intelligence 3.0, by Shivon Zilis, Bloomberg BETA As one would expect with such a new and dynamic market, these applications are maturing and proving value in different ways and at different rates. From an enterprise point of view, however, there are three primary areas that are broadly applicable to enterprise-class companies today:7 Enterprise Intelligence: AI that can classify, predict, analyze, recommend, act, and learn based on visual, audio, sensor, internal (enterprise), and market (macro) data. Computer Vision: AI that can “see” and, in conjunction with enterprise intelligence, be used to interpret and extract insights from images. Conversational AI: AI that can “listen,” understand, and communicate using natural language.
  8. 8. 7 The following section describes these technologies and use cases in more detail. ENTERPRISE INTELLIGENCE One of the greatest challenges in the enterprise is the difficulty in bringing together data from business systems—such as Business Intelligence (BI), Customer Relationship Management (CRM), market research, web analytics, and even external sources—and analyzing them in an integrated manner. For example, while social media might be a leading indicator of product popularity, it is difficult if not impossible to connect that insight with actual transactions, and harder still to use that data to forecast impact to the supply chain, especially for companies that sell through channels (e.g., not directly to customers). AI can also be used to make predictions about products based on sensor data. Enterprise intelligence looks at multiple datasets in context, using machine learning algorithms to extract not only insights but predictions, recommendations, and actions based on those findings. Most importantly, intelligence systems use the outcomes of these actions to (re)train their algorithms. Enterprise intelligence is used for a variety of functions, including marketing, security, enterprise performance management, analytics, and information management (see Figure 2).8 For example, Hewlett Packard uses vibration sensors that detect and evaluate printer sounds and alert users if the machine is likely to run out of ink or fail within the next two weeks.9 Customer Experience and Service Identify Customer Service Issues Predict Potential Churn Sales and Marketing / eCommerce Customer Acquisition Optimize Customer Onboarding Predict Campaign Performance Identify Upsell and Cross-Sell Opportunities Optimize Customer Lifetime Value Optimize CRM Strategy Optimize Shopping Cart Value Optimize Customer Retention Optimize Customer Loyalty Advertising Ad Targeting Revenue Optimization Figure 2: Use Cases for Enterprise Intelligence Employee Productivity Optimize Employee Onboarding Predict Potential Churn Optimize Resource Planning Recruitment Identify Qualified Candidates IT and Operations Workflow Management Enterprise Performance Management Security Information Management Identify Potential Fraud Optimize Data Management Processes Manufacturing Predict Maintenance Issues Supply Chain Optimize Demand Forecasting

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