HBR Articles

New digital technologies present threats and opportunities for companies. Some companies fail to recognize what their customers are drawn to because they’re too narrowly focused on products and services. Five traps contribute to this lack of foresight: the product trap, the value-chain trap, the operational-efficiency trap, the customer trap, and the competitor trap. Focus on the bigger picture: What do consumers want? Read this article from digital transformation professor Mohan Subramaniam to recognize these traps and learn ways to overcome them. 

Most leaders agree that creating a psychologically safe culture is important. However, subtle leadership behaviors can unintentionally discourage people from speaking up. In this article, executive coach Dina Smith shares 6 tips for leaders to guard against the strategic disadvantages of being stuck in an echo chamber. For example, Practice “yes and,” Demonstrate curiosity and listen, and seed different perspectives. Download this report to learn how to avoid behavior that shuts down dissenting perspectives and what to do instead.

Digital transformation looks different for each company. Successful businesses understand the nuances of transformations and know how to manage each. Can your company navigate a complex landscape of interconnected and interdependent issues, each with multiple stakeholders and agendas? To determine the right approach, ask yourself two questions: 1) Is your transformation driven by internal needs or external forces? 2) Does it need to happen quickly, or do you have more time to transform?

Uncertainty is nerve-wracking for most of us. But uncertainty leaves the door open to possibility. Your biggest personal transformations or achievements likely evolved during a period of uncertainty. Pushing through that stress to improve on the other side is a huge accomplishment. Don’t let uncertainty paralyze you. 

The rise in artificial intelligence brings with it ethical risks: From issues with bias, privacy concerns, and more, the impact when a problem occurs can be monumental. Consider the data used to train AI – it may reflect historical bias. In some cases, it undersamples specific subpopulations. What if you’ve set the wrong goal?