In an economy where data is changing how companies create value -- and compete -- experts predict that using artificial intelligence (AI) at a larger scale will add as much as $15.7 trillion to the global economy by 2030. As AI is changing how companies work, many believe that who does this work will change, too -- and that organizations will begin to replace human employees with intelligent machines. This is already happening: intelligent systems are displacing humans in manufacturing, service delivery, recruitment, and the financial industry, consequently moving human workers towards lower-paid jobs or making them unemployed. This trend has led some to conclude that in 2040 our workforce may be totally unrecognizable. Are humans and machine really in competition with each other though?

The increasing attention being paid to artificial intelligence raises important questions about its integration with social sciences and humanity, according to David De Cremer, founder and director of the Centre on AI Technology for Humankind at the National University of Singapore Business School. He is the author of the recent book, Leadership by Algorithm: Who Leads and Who Follows in the AI Era? While AI today is good at repetitive tasks and can replace many managerial functions, it could over time acquire the "general intelligence" that humans have, he said in a recent interview with AI for Business (AIB), a new initiative at Analytics at Wharton. Headed by Wharton operations, information and decisions professor Kartik Hosanagar, AIB is a research initiative that focuses on helping students expand their knowledge and application of machine learning and understand the business and societal implications of AI. According to De Cremer, AI will never have "a soul" and it cannot replace human leadership qualities that let people be creative and have different perspectives. Leadership is required to guide the development and applications of AI in ways that best serve the needs of humans. "The job of the future may well be [that of] a philosopher who understands technology, what it means to our human identity, and what it means for the kind of society we would like to see," he noted. An edited transcript of the interview appears below. AI for Business: A lot is being written about artificial intelligence. What inspired you to write Leadership by Algorithm?

During the past two years, Sales Mastery, Inc. (SMI) has been studying and cataloging the various artificial intelligence (AI) applications aimed at increasing sales rep productivity. With categories like: Appointment Setting; Automatic CRM Record Creation/Updating; Buyer Intent Analysis; Forecast Management; Guided Selling; and Prospect Engagement, to name just a few. It seems that AI is everywhere. A year ago, McKinsey published a report offering 3 other flavors of AI, in increasing levels of capability/complexity: Assisted Intelligence; Augmented Intelligence; and Autonomous Intelligence. These ranged from basically serving up facts/insights to the technology being able to generate its own outputs with minimal/no human intervention once started.

During the past two years, Sales Mastery, Inc. (SMI) has been studying and cataloging the various artificial intelligence (AI) applications aimed at …

Finding success is all about embracing the change to come -- and that's never been more relevant in real estate. Our industry is evolving in so many exciting ways with greater access to data and technologies that allow us to do our jobs better by cultivating leads, pinpointing pricing and, ultimately, helping clients achieve their real estate goals. It's a necessary, symbiotic relationship, one that can change quickly with each new innovation. Real estate agents are certainly professionals who love shiny new things, but we also understand the importance of utility and use that as a means of vetting the many tools and technologies that could be integrated into our day-to-day operations. The majority don't make the cut.

Roger Schank, an experienced computer and cognitive scientist with long experience in artificial intelligence research, is continually offended when media present simple tools like chatbots as examples of AI. "Key word analysis that enables responses previously written by people to be found and printed out, is not AI," as Schank sees it. And he complains, "We are in a situation where machine learning is not about learning at all, but about massive matching capabilities to produce canned responses." Schank worries that a bubble of hype about AI will lead, as it has in the past, to an "AI winter" -- when disillusionment from unfulfilled expectations causes interest and research funding in AI to dry up. Given the breadth of investment now in business, military, and consumer AI applications, perhaps this time may be different. Which is not a minor problem.