#1: Avoid Organizational Telephone
Too often, an AI project idea makes its way from conception to implementation through a game of organizational telephone. The path from the original business stakeholder who dreams it up to the machine learning engineer or data scientist who will actually build it inevitably passes through five steps and three management changes. Just as in childhood games of telephone, a lot is lost in the process. Brooke Wenig, Machine Learning Practice Lead at Databricks, had an experience that exemplifies organizational telephone games and the kind of damage they can cause. While serving an internship at MyFitnessPal, an app that allows users to track their diet and exercise, Brooke was given the task of building a model to classify the foods users entered into the app into groups, such as fruits, vegetables, etc. Brooke spent more than six weeks cleaning the data and creating mappings into a clear hierarchy, only to find out the groupings she had created were not at all what the product team needed or wanted. So, she had to start over from square one. Time and resources had been lost because no one had told her which groups the app could actually monetize, and the groups she had created were worthless from a business standpoint. The reason Brooke didn’t have the information she needed was the business folks—the product team—had not been in close contact with the person executing the AI model, and because of this, the project completely stalled for weeks on end. A lot more than just data science goes into an AI model’s creation—business decisions make up the most significant portions of the work involved. So as a business decision-maker, you must get involved granularly, to ensure that the people building the AI model are doing so in a way that is additive to the business.#2: Pick the Right Objectives, Success Measurements, and Milestones
