We're seeing a pattern with companies under 50 people: they default to hiring one ML engineer as their first AI hire, and it consistently underperforms. The engineer gets stuck in research mode without the operational context to ship, or builds models that don't connect to business needs. Our working hypothesis is that you need a triangle: an engineer who can build, an operations contact who understands the workflow, and a data owner who can ground the work in actual business problems. Without all three, the first AI hire often becomes an expensive consultant rather than a team member.
We're curious how others are solving this. Are you finding that the single hire model works in specific contexts? What's the smallest team that's actually delivering value with AI rather than just experimenting? Have you seen successful models where the AI hire reports to a non-technical function? We're particularly interested in counterexamples where the single hire has thrived without the triangle structure—what conditions made that possible?
This piece is from our notes on helping SMBs (10-100 people) build their first in-house AI teams. If your team is exploring this — quick feedback and questions welcome in the comments.
Top comments (0)