"The attention around AI tends to focus on the latest technologies," Iansiti argues, "but the firms that are thriving have harnessed the subtle, inherent power of AI to break down traditional operational constraints, capture new value, and accelerate growth and innovation." What sets AI-driven firms apart is their ability to avoid the inefficiencies and bottlenecks that plague growth when complexity -- primarily caused by humans -- outstrips organizational capacity. These firms strive to construct a model for operational execution that does not require human intervention (ideally, no real-time "human bottlenecks"). In the new digital operating model, most operational tasks circumvent humans entirely. The ultimate aim is to automate and digitize as many operational processes as possible to take advantage of digital reliability and scalability.

But if the narrative of the present is one of "prediction machines," referencing the book of the same title by Ajay Agrawal, Joshua Gans, and Avi Goldfarb, the narrative of the future will belong to "decision machines." If the narrative of the present is one of managers who are valued for showing judgment in decision making -- don't tell me whether someone will do well on the job, or whether a new product will win in the marketplace, but tell me instead who I should hire, which products I should bet on -- then the narrative of the future will be one in which we are valued for our ability to judge and shape the decision-making capabilities of machines. Artificial intelligence (AI) is the pursuit of machines that are able to act purposefully to make decisions towards the pursuit of goals. Machines need to be able to predict to decide, but decision making requires much more. Decision making requires bringing together and reconciling multiple points of view.

As Grushka-Cockayne explains, there was enthusiasm among key stakeholders at Heathrow to upgrade existing data systems at the airport, and a consensus about the opportunity to better leverage data to improve the experience of connecting passengers--who account for roughly one-third of all travelers who pass through Heathrow annually. The question was how to do this. "People want to use machine learning and big data--all of these buzz words," says Grushka-Cockayne, "but if they don't know how to focus in on a very specific task that can generate predictions, it is difficult to use the technology to actually improve decision-making." Grushka-Cockayne and her team spent several months working with partners at Heathrow to define the scope of their research--the development of a machine learning model that could predict a passenger's journey through Heathrow in route to his or her connecting flight. The goal was to be able to anticipate the number of people passing through immigration in real time (enabling more efficient staff allocation at immigration lines), and also to predict whether a passenger would be late for his or her flight (allowing the airport to proactively offer supporting services). But it wasn't easy to capture the complexity of a passenger's journey through an airport in a statistical model.