More companies in every industry are adopting artificial intelligence to transform business processes. But the success of their AI initiatives depends on more than just data and technology — it’s also about having the right people on board.
An effective enterprise AI team is a diverse group that encompasses far more than a handful of data scientists and engineers. Successful AI teams also include a range of people who understand the business and the problems it’s trying to solve, says Bradley Shimmin, chief analyst for AI platforms, analytics, and data management at consulting firm Omdia.
“The technologies and the tooling that we have available is skewing more and more toward enabling and empowering domain professionals, the business users, or the analytics professionals to take direct ownership of AI within companies,” he says.
Carlos Anchia, co-founder and CEO of AI startup Plainsight, agrees that AI success rests largely on establishing a well-rounded team with a diverse range of advanced skills, but doing so is challenging.
“Identifying what makes a highly efficient AI team may seem like an easy thing to do, but when you examine the detailed responsibilities of individuals on successful AI teams, you quickly come to the conclusion that building these groups is extremely hard,” he says.
To help you assemble your ideal AI team, here is a look at 10 key roles found in well-run enterprise AI teams today.
Data scientists are the core of any AI team. They process and analyze data, build machine learning (ML) models, and draw conclusions to improve ML models already in production.
A data scientist is a mix of a product analyst and a business analyst with a pinch of machine learning knowledge, says Mark Eltsefon, data scientist at TikTok.
“The main objective is to understand key metrics that have a major impact on business, gather data to analyze the possible bottlenecks, visualize different cohorts of users and metrics, and propose various solutions on how to increase these metrics, including making a prototype of the solution,” says Eltsefon, who adds that, when working on a new feature for TikTok users, it’s impossible to understand whether the feature benefits or alienates users without data science.
“You don’t understand how long you should test your feature and what exactly you should measure,” he says. “For all of this, you have to apply AI methods.”
Data scientists may build the ML models, but its ML engineers who implement them.
“This person is tasked with packing the ML model into a container and deploying to production — usually as a microservice,” says Dattaraj Rao, innovation and R&D architect at technology services company Persistent Systems.
The role requires expert back-end programming and server configuration skills, as well as knowledge of containers and continuous integration and delivery deployment, Rao says. “An ML engineer is also involved with validation of models, A/B testing, and monitoring in production.”
And in a mature ML environment, ML engineers also need to experiment with serving tools that can help find the best performing model in production with minimal trials, he says.
Data engineers build and maintain the systems that make up an organization’s data infrastructure. They are crucial to AI initiatives because data needs to be both collected and made suitable for consumption before anything trustworthy can be done with it, says Erik Gfesser, director and chief architect at Deloitte.
“Data engineers build data pipelines to collect and assemble data for downstream usage, and in a DevOps setting, they build pipelines to implement the infrastructure on which these data pipelines run,” he says.
The data engineer is foundational for both ML and non-ML initiatives, he says.
