We know more about A.I. namely Mika, Ash, and Roz because they tell us what they like. Ash likes band shirts and records; Mika likes historical replicas and small precious things; Roz enjoys bright colors. Well, Roz is an A.I., and also simultaneously a corporate email help-desk, Mika's friend, and a forklift, because like many of our real-world A.I.s, she's a network made up of a few physical devices (phones and robots) and an unknown number of distributed digital programs. These systems are intended to be low-bias statistical processors, like "There is a 70 percent chance that is a green polo shirt." At the same time, they don't know what "green" or "polo" is, or even what a "shirt" is for.

To evaluate whether the strategy or approach you're evaluating requires artificial intelligence, let's turn back to our definition of AI as any computer-based system that observes, analyzes, and learns. Thus, a true AI system is able to sense its own environment and augment its base of knowledge in close to real time. A Tesla's onboard computers analyze the images, blips, and other data it collects to make sense of its surroundings, allowing for the automation of several driving decisions. Using this data, companies and sales professionals are able to arrive at many counterintuitive insights -- for instance, calls with more positive sentiment are actually associated with lower closing rates than calls with less positive sentiment. The ability to test, learn, and improve is only available to the most advanced machine learning systems today.