How to Staff Your AI Team

The sheer diversity and complexity of AI projects combined with a requirement for rapid time to production create the need to find key AI roles to achieve successful AI projects.

Organizations face challenges in scaling artificial intelligence (AI) projects because they lack the requisite skills, collaboration, tooling and know-how to create and manage a robust, production-grade AI pipeline.

Through 2023, Gartner estimates that 50% of IT leaders will struggle to move their AI projects past proof of concept (POC) to a production level of maturity. To reduce this high failure rate, organizations need to build the right roles for AI success.

“In many organizations, data scientists are still wearing too many hats due to a dearth of talent across other roles,” said Arun Chandrasekaran, Distinguished VP Analyst, Gartner, during his session at virtual Gartner IT Symposium/Xpo® 2020.

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AI is a team sport

To successfully operationalize and scale AI initiatives, organizations need to build diverse AI roles and skills. 

“AI is a team sport,” said Chandrasekaran. “On their AI team, CIOs and technology innovation leaders need to have data scientists, data engineers and complement the team with AI architects and machine learning (ML) engineers. Together they can envision, build, deploy and operationalize an end-to-end ML/AI pipeline.”

That team should never operate in isolation and should collaborate with business domain experts, IT experts, and other relevant staff and stakeholders to deliver successful AI initiatives.

Read more: 2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020

Roles of AI architects and ML engineers

Finding the right resources and how they work together as part of the AI team becomes pivotal for the success of AI projects. Two newer roles are complementing the AI team: AI architects and ML engineers.

The AI architect is laser-focused on the transformational architecture efforts that AI introduces. Their primary responsibility is to orchestrate the deployment and management of models in production and provide inputs on the applicability of ML and deep learning models within AI’s various disciplines, such as natural language processing or image recognition.

Gartner outlines four core AI roles and their respective responsibilities.

Because ML is the most utilized AI initiative, organizations are also increasingly hiring ML engineers as part of the AI team. They can move ML solutions in production and optimize the environment for performance and scalability. 

Through 2023, the ML engineer role will be the fastest-growing role in the AI/ML space. Gartner estimates that today there is one ML engineer for every 10 data scientists, and it will likely change to between 5 and 10 by 2023. 

“ML engineers need to ensure that AI platforms deliver against technical and business SLA requirements,” said Chandrasekaran. “ML engineers are expected to be the connecting fabric with data scientists from an IT perspective and ensure their ML models run well in production.”

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Recommended reading for Gartner clients*: “Artificial Intelligence Architect: A Key Role to Operationalize and Scale Your AI Initiatives” and Machine Learning Engineer — A Role That Bridges the Gap Between Data Science and IT” by Arun Chandrasekaran, et al.

 

*Note: Some documents may not be available to all Gartner clients.

 

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