CURT NICKISCH: Welcome to the HBR IdeaCast from Harvard Business Review. A shiny new piece of technology is not good enough on its own. It needs to be implemented at the right time, used in the right context, and accepted in the right culture, applied in the right way. In short, it needs to be part of the right system. AI can help individuals and teams make better predictions, combine that with judgment and you get better decisions. But those decisions have ripple effects on other parts of the system, ripple effects that can undermine the very prediction that was made. Our guest today says, "If organizations want to take artificial intelligence to the next level, they need to get better at coordinating optimal decisions over a wider network."

CURT NICKISCH: Welcome to the HBR IdeaCast from Harvard Business Review. A growing number of companies are turning to artificial intelligence to solve some of their most vexing problems. The promise of AI is that it can go through vast amounts of data and help people make better decisions. And one area where companies often search for profitable use cases for the technology is in marketing. It's harder than it looks. Data scientists at one consumer goods company recently used AI to improve the accuracy of the sales forecasting system. While they did get the system working better overall, it actually got worse at forecasting high margin products. And so the new, improved system actually lost money. Today's guest says that many leaders lean too heavily on AI and marketing without first thinking through how to interact with it.

Matt Beane, assistant professor at the University of California, Santa Barbara, finds that robots, machine learning, and AI are changing how we train for our jobs -- not just how we do them. His study shows that robot-assisted surgery is disrupting the traditional learning pathway of younger physicians. He says this trend is emerging in many industries, from finance to law enforcement to education. And he shares lessons from trainees who are successfully working around these new barriers. Beane is the author of the HBR article "Learning to Work with Intelligent Machines." CURT NICKISCH: Welcome to the HBR IdeaCast from Harvard Business Review. Like it or not, people increasingly do their jobs with robots, machine learning, or artificial intelligence. These developing technologies are already destroying some jobs and changing how many others are performed. But it's not just work that's changing.

Avi Goldfarb, a professor at the University of Toronto's Rotman School of Management, explains the economics of machine learning, a branch of artificial intelligence that makes predictions. He says as prediction gets cheaper and better, machines are going to be doing more of it. That means businesses -- and individual workers -- need to figure out how to take advantage of the technology to stay competitive. Goldfarb is the coauthor of the book Prediction Machines: The Simple Economics of Artificial Intelligence. CURT NICKISCH: Welcome to the HBR IdeaCast, from Harvard Business Review. YOUTUBE: [Two women speaking] We've got this all tabbed up? In it, three young English-speaking women use Google Translate to order food in Hindi from an Indian restaurant. They copy and paste their order in English into the computer, and it translates items like "samosas" and reads them aloud in the foreign language.