The increasing frequency of interactions we have with AI and automated technologies means it is vital to understand how those things make people feel about themselves. Why? Because how people feel about themselves affects a wide range of success factors, including sales, customer loyalty, word-of-mouth referrals, employee satisfaction, and employee performance. The authors have been studying people's reactions to automated technology for more than seven years. In this article they focus on psychological responses to AI and automated technologies that they've observed in service and business-process design, product design, and communication, and offer practical guidance to help leaders and managers figure out how best to use these new technologies to serve customers, support employees, and advance the interests of their organization.

The human brain likes simple straight lines. As a result, people tend to expect that relationships between variables and outcomes will be linear. Often this is the case: The amount of data an iPad will hold increases at the same rate as its storage capacity. But frequently relationships are not linear: The time savings from upgrading a broadband connection get smaller and smaller as download speed increases. Would it surprise you to know that upgrading a car from 10 MPG to 20 MPG saves more gas than upgrading from 20 MPG to 50 MPG? Because it does. As fuel efficiency increases, gas consumption falls sharply at first and then more gradually. This is just one of four nonlinear patterns the authors identify in their article. Nonlinear phenomena are all around in business: in the relationship between price, volume, and profits; between retention rate and customer lifetime value; between search rankings and sales. If you don't recognize when they're in play, you're likely to make poor decisions. But if you map out relationships in data visualizations, you can actually see whether they are nonlinear and how--and then make choices that maximize your desired outcome.

Professor Stefano Puntoni set out to prove that breast cancer awareness campaigns were more effective when they incorporated the color pink. Instead, his studies repeatedly showed that pink made women less likely to recognize their risk of cancer and less likely to donate to research. And that led him to explore the surprising effects of gender cues in marketing messages.