Defining a good AI project is a fascinating challenge. Unless you’re doing pure research, it requires you to find a strong intersection between business and technology. Finding that intersection is hard for many reasons. Today I want to focus on a specific one: the two worlds of business and technology are often in the hands of different people within an organization, and making them work together effectively isn’t trivial.
Let’s put ourselves in the shoes of a hypothetical pharmaceutical company, Acme Bio (what we’ll say applies to every industry).
Technology is built by humans, for humans.
This is a probably obvious, yet often forgotten truth. If you want to be successful at pioneering AI into an organization, you don’t have the luxury of forgetting it.
During my work, I often interact with managers running companies that are dealing with their first AI projects. What I realised is that understanding the emotional implications of their new journey is sometimes even more important than the technology itself.
My Instagram and LinkedIn DMs are often filled up with people asking me a variation of the same question: > How do I become a Data Scientist?
I always take some time to try to understand the motivations of each person who turns to me for advice, but since I often notice the same patterns I decided to write this post as a general guidance for everybody.
I noticed that there are mostly three kinds of people asking me this question: 1.