What’s The Ideal AI Team Composition?
Everyone wants a simple answer that defines roles, ratios, and per-project team numbers. But no one gets paid for oversimplifications and reductive answers. Short-term satisfaction quickly turns into consequences. Focus on outcomes and use frameworks to streamline the complexity away. That’s how some people get ahead while others are laid off.
In this article, I will define the ideal AI team for 3 stages of data maturity and 3 stages of AI maturity. It’s a detailed answer. I’ll use frameworks to make it easier to explain and implement yourself.
Problem Statement…Why So Much Confusion?
Social media is filled with 10X Software Engineer and Full Stack Data Science experts who lecture because they can’t do. They tell anyone who will listen that their way is the highest form of their craft. Most are looking for external validation since they aren’t getting feedback at the office.
Technical teams rarely quantify their value internally, and the business only provides feedback when things go wrong. Most business units don’t understand how they create value, either. Product teams can’t quantify why customers pay for products. No part of the business can explain how data and AI initiatives create value.
No one can articulate their needs if no one knows what’s valuable. AI teams inherit an early maturity business. It uses data but isn’t learning anything new from it. This is where the ideal AI team starts. How do we hire the right capabilities if we don’t know what the AI team will build?
Kitchen sink job descriptions and unicorn chases are necessary if the AI team could be tasked with building almost anything. That’s where all the job description vs. job memes come from. Monolithic definitions of the 10X Data Engineer or Full Stack Data Scientist fall into two traps.
Few people have the capabilities breadth and depth to fit the Full Stack definition. They are the toughest to source and most expensive.
The business’s talent needs change as it matures, so the definition of Full Stack changes, too. That leads to expensive layoff and rehiring cycles.
Talent needs are defined by what the business decides to build, but leaders need data to make those decisions well. New frameworks are needed to carry those decisions forward.
Initiatives deliver on opportunities and implement support for use cases. The ideal AI team follows the business’s data and AI maturity progression. The product roadmap defines the maturity journey with enough granularity to align hiring.
Without alignment, AI teams are cost centers no matter how 10Xed and Full Stacked they are. Following career advice that’s disconnected from value-creation sets people up for layoffs. Here’s a better approach.