Welcome. This is a course on AI for Business. In this course, we will talk about AI from a managerial standpoint. We'll look at several business use cases for artificial intelligence and we will look at a strategic framework for managers to use in order to get the return from AI investments. I'm Kartik Hosanagar, I'm a Professor of Technology and Digital Business at the Wharton School. My research focuses broadly on the digital economy. I look at Internet commerce, digital media, digital marketing, and data-driven decision-making. I was previously a co-founder of Yodel, which was a marketing platform for small businesses and I've worked with a number of startups and large companies over the years, looking specifically at the applications of AI and Data Science and Business. I'm also an author of the book Human's Guide to Machine Intelligence, which looks at the implications of using artificial intelligence to make business decisions, both within the enterprise and outside. Artificial intelligence or AI is all around us. AI is about getting computers to do the kinds of things that require human intelligence. For example, understanding language, reasoning, navigating the physical world, learning, and so on. Machine learning is a subfield of AI that is focused on getting computers to learn without explicitly programming them. AI is increasingly being seen as the next phase of digital transformation. Over the years, a number of different digital technologies have helped enable business transformation, which is the idea of organizing or transforming a company's activities and processes in order to make use of the new opportunities created by digital technologies. Now in the late '90s, the technology that helped usher in that transformation was the Internet. A number of companies opened online divisions to help make use of that opportunity. But unfortunately, a number of these firms also shut down these online divisions after the dot-com bust. The few companies that persisted actually benefited significantly in the long run and the companies that did not ended up paying a significant price. In the mid 2000s, Cloud computing helped usher in a similar change. Here again, a number of companies started investing in Cloud computing, but again, a few companies backed off when they realized that their early forays into Cloud computing faced a number of challenges related to security with moving their data to the Cloud or with regulatory compliance. But these companies that backed off, again, paid the price and the companies that persisted were the ones that were well-positioned in the long run and helped create a certain amount of business agility that has helped them in the long run. In the late 2000s, mobile computing helped create a similar change, and the companies that invested in mobile computing early helped really create mobile-first and mobile-only products and helped transform the businesses into a mobile world. Today, it's increasingly appearing like AI will be equally transformative. In fact, there is early evidence that AI can be seen as what is often referred to as a general purpose technology. Now a general purpose technology is a technology that has the potential for widespread use across a range of sectors, and these general purpose technologies can stimulate innovation and drive economic growth. At an organizational level, they can also inform product strategy and overall design of the organization itself. Now three factors are seen as being indicative of whether a technology is a general purpose technology. The first is that the technology has widespread use across multiple industries. The second is that there are large number of research jobs related to that technology and these research jobs themselves are spread across a number of other industries as well. In a recent study by Goldfarb et al, the researchers looked at whether artificial intelligence shows promise as a general purpose technology. They looked at a number of recent technologies that have got attention in the press. For example, machine learning, geographical information systems, CRISPR, quantum computing, fracking, robotics, nanotechnology, Internet of Things, and Cloud computing. They also looked at millions of job postings and classified these job postings based on which technology they were related to. They evaluated whether machine learning, which is a subfield of artificial intelligence, look different. As you can see on the slide, the researchers found that a number of job postings were related to machine learning, as also a few other technologies like robotics and Cloud computing. In fact, as many as 14.6 percent or almost 15 percent of the jobs for machine learning, were research-related jobs. Now research-related jobs are particularly important indicator of a general purpose technology because they help demonstrate that the technology is capable of ongoing improvement. This ongoing improvement creates significant future potential, some of which is not currently recognized and certainly machine learning, which is the most important sub-field of AI, seems to demonstrate that capability. The researchers also looked at whether machine learning jobs in particular were spread across a number of different industries. They did find that machine learning jobs can be seen in a number of different industries, primarily in education services, in professional services, and manufacturing, and financial services, and so on. In contrast, some of the other technologies like quantum computing were seen primarily in one or two industries like professional services. In short, it does seem like machine learning jobs are available in multiple industries and multiple industries see value from those skills today. Next, the researchers looked at whether research jobs, in particular, were also widespread across industries. Here again, the researchers found that a number of industries, including manufacturing, professional services, information technology-related jobs, finance, education, all of these had need for research jobs related to machine learning. Not every other technology showed this widespread. In short, a number of these statistics do suggest that machine learning in particular and AI in general, is likely to be a general purpose technology. There are many implications of this. The first is that companies and managers need to realize that machine learning and AI will have significant impact on a wide variety of industries. Just because you're not a technology industry, does not mean that you're shielded from the transformative impacts of machine learning. Secondly, the fact that a lot of these jobs are research jobs, which implies the technology is evolving, also implies that managers need to be patient with the technology. The transformative impact of the technology might come with a luck. Therefore, to effectively make use of these opportunities, managers will need to understand the technology and its applications, they will need to make many changes to their business models, to their tech infrastructure, to their organizational processes, and to their culture as well. All of that requires significant changes. The purpose of this course is to help you get there.
