4 Linking business and technology
This chapter covers
- Linking business and technology metrics
- Measuring technical progress in business terms
- Applying the L and U of the CLUE process
- Overcoming organizational obstacles to measuring technical progress
In chapter 3, you selected your first AI project to run. Now you have the research questions that project needs to answer. This chapter shows you how to properly organize that project by using metrics to link a business problem with the technical solution you’re building. It also shows you how to understand what your technical progress means when translated into a business result. Finally, it shows you how to avoid typical organizational obstacles you might meet in your quest to link business and technology.
But before we do all of that, let’s first explain what causes the average organization to fall into the trap of making AI project decisions based on intuition as opposed to data.
4.1 A project can’t be stopped midair
Running any project is more like piloting a plane than driving a car. When you’re piloting a plane, if something happens, you don’t have the option of pulling onto the side of the road and sorting out the problems. And if something happens, the flight has to continue, and you’d have to sort things out while you’re still flying. This section shows why such a situation could incentivize an organization into making many decisions based on instinct rather than data.