AI Essentials for Tech Executives

Chapter 7. Hiring the Right Team for AI

Another costly mistake that executives make is incorrectly hiring an AI team. This mistake can lead to wasted resources, team churn, and delayed AI efforts.

The AI Specialist Trap

Many executives rush to hire data scientists or machine learning engineers (MLEs) right away. While these roles are crucial, bringing them on too early is a classic “foot gun” scenario. Here’s why:

Data scientists and MLEs need data to work with. If you’re still in product discovery, there’s often little for them to do.

The people who fulfill these roles likely aren’t equipped to build full-stack products from scratch. Lacking a clear product direction, they may inadvertently steer you off course.

A Smarter Hiring Progression

Instead of immediately seeking AI specialists, follow this general progression when building an AI product:

1. Focus on building the product

Start by hiring Application Developers who can help iterate on the product concept quickly. At this stage, you’re still figuring out what your product is, and need to build a thesis.

2. Build the data foundations

Instrument your system to collect the right data and set up proper observability. This may require Platform or Data Engineers, depending on the type and scale of data you’re working with.

3. Optimize your AI system

Once you have a working product and data, that’s when you should hire MLEs or data scientists to optimize the system. At this point, they should be able to design metrics, build evaluation ...