El equipo necesario para tu proyecto de Inteligencia Artificial

The necessary team for your Artificial Intelligence project

Inteligencia Artificial

There are 3 types of roles in an Artificial Intelligence (AI) project. All of them are essential to carry out an AI project, and are characterized by having a clearly defined domain of knowledge. These roles are the data scientist, the IT technician or architect, and the domain expert.

We will try to explain them using the analogy of a movie that you will surely know:

1. The data scientist: the explorer

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

They are in charge of handling the bulk of the data and making sense of them. They are capable of creating truly self-taught Machine Learning (ML) algorithms that learn more and more from the data.

2. The IT specialist or architect: the facilitator

We need IT specialists who create the systems and make them reliable and available. In this case, this role is represented by the father of the data scientist: Henry Jones created the Grail Diary, a real data warehouse that his son took upon himself to interpret!

IT specialists will create datalakes and break silos, making sure the rest of the team can access the data.

Finally, they will define and implement data ingestion and migration processes. In this category, we find data architects, data engineers, data analysts and data custodians, among others.

el facilitador - inteligencia artificial

 

3. The domain expert: the decision maker

Domain experts are responsible to make sure that the AI ​​model really adds value. They are the ones who know the business to which AI will add this value and the ones who promote the project. For this, we can find business leaders in any sector and also in any area of ​​the company: marketing, HR, operations, logistics, etc. In this case, the role is represented by the figure of Marcus Brody, the person who planned and run the expeditions from the university.

Domain experts perform three critical functions:

  1. Imagining the use case and what business data might be useful.
  2. Understanding what changes in business processes will be necessary. It is important to assimilate that the application of AI will often lead to changes in the way a company works.
  3. Keep training the algorithm. For the AI ​​to be effective, it will be necessary to create the technical and operational conditions to continue training the model by, for example, better and better labeling the data using supervised learning algorithms.

In this category, we find data owners or data stewards.

el decision maker - inteligencia artificial

Finally, it is important to note that tackling an AI project implies that the people in charge of the three domains work together as a team. This requires a common language and a basic understanding of AI by all three roles.

If you want to move forward, we suggest you to read "The challenges of applying Artificial Intelligence" and, if you need help in adopting AI and Machine Learning, contact us!.

Martí Fàbrega

Martí is a Digital Transformation Consultant and Senior Business Development Manager at Opentrends. His aim is to transform technology into business value for his clients, putting the greatest possible focus on innovation.