So, hands up who was woken up by Alexa this morning? Or now has Google Home finding their favourite radio station for them? Or had fun over the holidays trying to get Siri to tell them a joke? Artificial intelligence is now more accessible and becoming mainstream. The rapid development and evolution of AI technologies, while unleashing opportunities for business and communities across the world, have prompted a number of important overarching questions that go beyond the walls of academia and hi-tech research centres in Silicon Valley.
Implementing artificial intelligence into an existing business is about more than algorithms. In fact, many AI researchers believe that algorithms are the easiest part of an artificial intelligence implementation. Algorithms need data, and for a business to assess, organize, clean, and use it's data requires ways of thinking that are entirely foreign to most existing enterprises. Partnering with Corinium Global Intelligence, we asked six experienced AI and analytics professionals (all speakers at Corinium's Chief Analytics Officer Spring event in on May 14th-16th in San Francisco) the following three important questions: In the sub-sections of the article that follows, we'll explore each of these questions in depth, highlighting the best insights from the professionals we corresponded with. IT procurement, software development, and software aren't new concepts to many experienced executives.
For some organizations, harnessing artificial intelligence's full potential begins tentatively with explorations of select enterprise opportunities and a few potential use cases. While testing the waters this way may deliver valuable insights, it likely won't be enough to make your company a market maker (rather than a fast follower). To become a true AI-fueled organization, a company may need to fundamentally rethink the way humans and machines interact within working environments. Executives should also consider deploying machine learning and other cognitive tools systematically across every core business process and enterprise operation to support data-driven decision-making. Likewise, AI could drive new offerings and business models. These are not minor steps, but as AI technologies standardize rapidly across industries, becoming an AI-fueled organization will likely be more than a strategy for success--it could be table stakes for survival. In his new book The AI Advantage, Deloitte Analytics senior adviser Thomas H. Davenport describes three stages in the journey that companies can take toward achieving full utilization of artificial intelligence.1 In the first stage, which Davenport calls assisted intelligence, companies harness large-scale data programs, the power of the cloud, and science-based approaches to make data-driven business decisions. Today, companies at the vanguard of the AI revolution are already working toward the next stage--augmented intelligence--in which machine learning (ML) capabilities layered on top of existing information management systems work to augment human analytical competencies. According to Davenport, in the coming years, more companies will progress toward autonomous intelligence, the third AI utilization stage, in which processes are digitized and automated to a degree whereby machines, bots, and systems can directly act upon intelligence derived from them. The journey from the assisted to augmented intelligence stages, and then on to fully autonomous intelligence, is part of a growing trend in which companies transform themselves into "AI-fueled organizations."
New technical artifacts connected to the Internet constantly share, process, and storage a huge amount of data. This practice is what unifies the concept of Internet of Things ("IoT") to the concept of Big Data. With the growing dissemination of Big Data and computing techniques, technological evolution and economic pressure spread rapidly, and algorithms have become a great resource for innovation and business models. This rapid diffusion of algorithms and their increasing influence, however, have consequences for the market and for society, consequences which include questions of ethics and governance. Automated systems that turn on the lights and warm the dinner by realizing that you're returning home from work, smart bracelets and insoles that share with your friends how much you've walked or cycled during the day in the city or sensors that automatically warn farmers when an animal is sick or pregnant.
We know artificial intelligence will remake -- is already in the process of remaking -- both business and the broader world beyond. What we don't know yet is what unintended consequences AI will wreak as it becomes more advanced and commonplace. One hindrance to envisioning that future is that AI is not "a technology," in the same sense that ERP, for example, is a technology. While there are different flavors of ERP, with differing sets of capabilities, we generally understand that it's software designed to integrate an organization's operational and financial processes into a unified system. Artificial intelligence, though, is "a diverse set of methods and tools continuously evolving in tandem with advances in data science, chip design, cloud services, and end-user adoption," as Ernst & Young (EY) put it in a recent paper.