The necessary equipment for your Artificial Intelligence project

There are 3 types of roles in an Artificial Intelligence (AI) project. The 3 are fundamental to carry out an AI project, and are characterized by having a clearly differentiated domain of knowledge: the data scientist, the IT technician or architect, and the domain expert.

Next, we are going to try to explain them using the analogy of a movie that you will surely know:

Data Scientist: The Explorer

Without a doubt, the protagonist in an AI project. The data archaeologist.

It is responsible for handling the bulk of data and making sense of it. They are able to create Machine Learning (ML) algorithms, truly self-taught, that learn more and more from the data.

IT Technician or Architect: The Facilitator

We need IT specialists who create systems and make them reliable and available. In this case, we represent it as the father of the data scientist. Henry Jones created the Grail Diary: a true data warehouse that the son was in charge of interpreting!

IT specialists will create the datalakes and break down the silos, ensuring that the rest of the team can access the data. Finally, they will define and implement the data ingestion and migration processes.

In this category we find Data Architect, Data Engineer, Data Analyst and Data Custodian, among others.

Domain Expert: The "Decision Maker"

Domain experts are responsible for ensuring that the AI model truly adds value. They are the ones who know the business to which AI will bring value and who promote the project. Here we find business leaders in any sector and we can also find them in any area of the company: marketing, human resources, operations, logistics... In this case, we represent them in the figure of Marcus Brody: the one who planned and led expeditions from the university.

Domain experts perform three functions of vital importance:

1. Imagine the use case and which company data may be useful.
2. Understand what changes in business processes will be necessary. It is important to understand that the application of AI often involves changes in the way a company operates.
3. Continue training the algorithm. In order for the AI to be effective, we will need to create the technical and operational conditions to continue training the model, for example, by labeling the data more accurately (for example, using supervised learning algorithms).

In this category we find the Data Owner or the Data Steward.

Finally, it is important to note that addressing an AI project involves the people in charge of the three domains working together as a team. This requires a common language and a basic understanding of Artificial Intelligence by the three roles.

If you want to keep moving forward, we suggest you read "Los retos de aplicar Inteligencia Artificial" and if you need help in adopting Artificial Intelligence and Machine Learning, contact us.