Insights
Building a Team for Your First AI Product
Technology
•
Jun 3, 2024
Every founder knows that a great product starts with a great team—but when it comes to AI, what “great” means is evolving fast. The old playbook of hiring only engineers or data scientists is out. Today, successful AI product teams blend technical skill, product sense, and domain expertise. If you’re building your first AI-driven product, how do you put the right team together?
Start with the basics: You need someone who deeply understands the user problem. According to Harvard Business Review and McKinsey’s 2023 global AI survey, companies that include product managers or domain experts in their AI teams see faster adoption and better results than those who rely only on technologists. These “translator” roles bridge the gap between user needs and technical implementation.
Next, consider your technical stack. You don’t need to start with PhD-level machine learning researchers. Most startups succeed with a few versatile software engineers who are eager to learn and adapt. If you’re using established APIs (like OpenAI or Anthropic) or open-source tools (from Hugging Face), look for engineers who can rapidly integrate and test different models, not just write ML code from scratch.
Data expertise is another pillar. AI products are only as good as the data they’re trained on. As Google Cloud’s AI adoption guide explains, having even a single person with strong data skills—be it a data engineer or analyst—can make a big difference in sourcing, cleaning, and structuring your data. This becomes essential as you move from prototype to production.
But there’s more to an AI team than technical chops. Startups like Zapier and Jasper highlight the value of “prompt engineers”—those who specialize in writing and refining prompts for large language models. It’s a new but growing role, helping bridge the gap between raw model output and user-friendly experience.
Designers round out the roster. AI-powered products often require rethinking UX—when does the AI step in? How transparent should it be? A designer with a knack for clarity and trust-building can turn a functional AI demo into a product people actually want to use.
Of course, budget and runway matter. Early on, many teams start with freelancers or part-time contributors, especially for design or data labeling. As you scale, consider hiring full-time for roles that touch user experience, data, or security.
In short, building your AI product team isn’t just about stacking technical talent. It’s about assembling a crew that can understand the user, wrangle data, experiment with models, and design delightful experiences. The winning mix will look different for every company—but as leading teams at Google, Jasper, and Zapier show, the key is to blend expertise and stay nimble. Build with the user in mind, stay curious, and you’ll set your AI product up for success.
References:
Related insights
Why Most AI Pilots Stall and How to Break the Cycle
Building a Team for Your First AI Product
Technology
•
Jun 3, 2024
Every founder knows that a great product starts with a great team—but when it comes to AI, what “great” means is evolving fast. The old playbook of hiring only engineers or data scientists is out. Today, successful AI product teams blend technical skill, product sense, and domain expertise. If you’re building your first AI-driven product, how do you put the right team together?
Start with the basics: You need someone who deeply understands the user problem. According to Harvard Business Review and McKinsey’s 2023 global AI survey, companies that include product managers or domain experts in their AI teams see faster adoption and better results than those who rely only on technologists. These “translator” roles bridge the gap between user needs and technical implementation.
Next, consider your technical stack. You don’t need to start with PhD-level machine learning researchers. Most startups succeed with a few versatile software engineers who are eager to learn and adapt. If you’re using established APIs (like OpenAI or Anthropic) or open-source tools (from Hugging Face), look for engineers who can rapidly integrate and test different models, not just write ML code from scratch.
Data expertise is another pillar. AI products are only as good as the data they’re trained on. As Google Cloud’s AI adoption guide explains, having even a single person with strong data skills—be it a data engineer or analyst—can make a big difference in sourcing, cleaning, and structuring your data. This becomes essential as you move from prototype to production.
But there’s more to an AI team than technical chops. Startups like Zapier and Jasper highlight the value of “prompt engineers”—those who specialize in writing and refining prompts for large language models. It’s a new but growing role, helping bridge the gap between raw model output and user-friendly experience.
Designers round out the roster. AI-powered products often require rethinking UX—when does the AI step in? How transparent should it be? A designer with a knack for clarity and trust-building can turn a functional AI demo into a product people actually want to use.
Of course, budget and runway matter. Early on, many teams start with freelancers or part-time contributors, especially for design or data labeling. As you scale, consider hiring full-time for roles that touch user experience, data, or security.
In short, building your AI product team isn’t just about stacking technical talent. It’s about assembling a crew that can understand the user, wrangle data, experiment with models, and design delightful experiences. The winning mix will look different for every company—but as leading teams at Google, Jasper, and Zapier show, the key is to blend expertise and stay nimble. Build with the user in mind, stay curious, and you’ll set your AI product up for success.
References: