Introduction to AI teams and how they are organised. AI teams are specialised groups within organisations focused on utilising data and artificial intelligence techniques to drive business value. Their roles and structures depend on the sector they operate in, and an organisations' maturity level regarding AI adoption.

For example, tech firms that specialise in developing AI technologies often have large teams of highly specialised AI scientists, who create products like Chatbots, generative AI tools, or recommendation systems.

In contrast, other companies that operate in other industries such as retail or hospitality may have smaller AI teams that are focussed on improving operational efficiency, such as enhancing marketing strategies, and optimising supply chain management.

The structure of AI teams can vary widely between companies, but a few essential roles are commonly found across all industries.

Data scientists manipulate data and use AI techniques to extract valuable insights. Their role involves a variety of tasks, from building dashboards that communicate essential information to employing advanced AI techniques such as machine learning models to predict trends, classify data, and solve complex problems.

Data scientists work with large data sets, apply statistical analysis, and develop algorithms to discover patterns and correlations that inform decision making. AI teams may also have AI scientists who are highly specialised experts who focus on the development and refinement of cutting-edge artificial intelligence algorithms and techniques.

These algorithms can be adopted and implemented by data scientists. Software developers specialise in creating software like mobile or web applications. They also create the underlying systems that power those applications, like operating systems, and databases. Their responsibilities include software implementation, testing and maintenance.

Data engineers are the architects behind the scenes. They design, build and maintain the infrastructure that allows data to be collected, processed, stored, and accessed efficiently.

Finally, product managers oversee the development and lifecycle of AI products. They work closely with all roles, and often serve as intermediaries with business stakeholders to ensure the product meets the company's needs and objectives.

The way AI teams are embedded in organisations depends on the company and its operational model, but there are two widespread structures, the centralised and decentralised models.

In the centralised model, one centralised AI team acts as the reference point for any project requiring AI expertise. They serve as AI providers to the rest of the company.

In the decentralised model, several small AI teams are distributed across different departments, providing AI services at the department level. Each team focuses on the specific needs of its department.

Centralised teams ensure consistent ways of working and ensure resource efficiency, while decentralised teams allow for more tailored and immediate support, although this can potentially lead to duplication of efforts and inconsistent standards.

There's no perfect format for structuring AI teams, and the choice depends on the organisation's needs and goals. Understanding how these teams are structured within your company can help you understand how you can benefit from their services.

That's a wrap for this video. Thanks for watching.

Structure & Roles of an AI Team

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