What are important elements to consider when putting together a team to execute your operating model for AI?
When building the right team to execute on your operating model for AI it is key to take into account the following elements:

1) How mature is my data foundation: Whether your data is still in silos, stuck in proprietary formats or difficult to access in a unified way will have big implications on the amount of data engineering work and data platform expertise that is required.

2) Infrastructure and platform administration: Whether you need to maintain or leverage as a service offerings can have a huge impact on your overall team composition. Moreover, if your Data and AI platform is made up of multiple services and components the administrative burden of governing and securing data and users and keeping all parts working together can be overwhelming especially at enterprise scale.

3) MLOps: To make the most of AI you need to be able to use it to impact your business. Hiring a full data science team without having the right ML engineering expertise or the right tools to package, test, deploy and monitor is extremely wasteful. There are several steps that go into running effective end-to-end AI applications and your operating model should reflect that in the roles that are involved and in the way model lifecycle management is executed from use case identification, to development to deployment, and perhaps most importantly utilisation.  

These three dimensions should inform your focus and the roles that should be part of your development team. Over time, the prevalence of certain roles might shift as your organisation matures along these dimensions and on the platform decisions that you make.
Instruction: What are important elements to consider when putting together a team to execute your operating model for AI?
Context: 
Response: When building the right team to execute on your operating model for AI it is key to take into account the following elements:

1) How mature is my data foundation: Whether your data is still in silos, stuck in proprietary formats or difficult to access in a unified way will have big implications on the amount of data engineering work and data platform expertise that is required.

2) Infrastructure and platform administration: Whether you need to maintain or leverage as a service offerings can have a huge impact on your overall team composition. Moreover, if your Data and AI platform is made up of multiple services and components the administrative burden of governing and securing data and users and keeping all parts working together can be overwhelming especially at enterprise scale.

3) MLOps: To make the most of AI you need to be able to use it to impact your business. Hiring a full data science team without having the right ML engineering expertise or the right tools to package, test, deploy and monitor is extremely wasteful. There are several steps that go into running effective end-to-end AI applications and your operating model should reflect that in the roles that are involved and in the way model lifecycle management is executed from use case identification, to development to deployment, and perhaps most importantly utilisation.  

These three dimensions should inform your focus and the roles that should be part of your development team. Over time, the prevalence of certain roles might shift as your organisation matures along these dimensions and on the platform decisions that you make.