As artificial intelligence algorithms infiltrate the enterprise, organizational learning matters as much as machine learning. How should smart management teams maximize the economic value of smarter systems? Business process redesign and better training are important, but better use cases – those real-world tasks and interactions that determine everyday business outcomes – offer the biggest payoffs. Privileging smarter algorithms over thoughtful use cases is the most pernicious mistake I see in current enterprise AI initiatives. Something's wrong when optimizing process technologies take precedence over how work actually gets done.

Most of us have developed our ideas of what artificial intelligence should look like from movies, television shows, and popular novels. When thinking about AI, our minds flash to images of robots masquerading as humans, as we saw in Spielberg's film of the same name, or countless other films such as Blade Runner and the Alien movies. Or, we conjure up ideas of computers that think and speak like humans, such as the one who so famously told Dave "I'm sorry Dave, I'm afraid I can't do that" in the film 2001. Over the years, in our real life experiences, we've become accustomed to computers and other devices asking us questions, qualifying us via complicated phone menus, or helping us to find quick facts with the aid of assistants like Siri. We realize that the AI found in Hollywood is years away, and are realistic about the technology we have today and how it's progressing. After all, making a piece of software that can think and act exactly like a human is a pretty tall order.