Most companies hire 1 ML engineer and wonder why nothing ships. They're missing the operational layer that turns models into value. I've seen this pattern play out across 30+ SMBs.
The 3-person minimum team to ship AI in a 50-person company:
ML Engineer: Owns model development. Week 1: Build a simple classifier on your existing data to categorize support tickets.
Product Manager: Owns problem framing. Week 1: Interview 5 customers to identify 1 high-frequency problem AI could address.
DevOps Engineer: Owns deployment. Week 1: Containerize a simple model and set up basic monitoring for accuracy drift.
The ML Engineer isn't just a coder. They need to translate business problems into technical specs. I've seen too many teams build models nobody uses because this connection was missing.
The Product Manager must understand both the domain and AI's limitations. Week 2: Define success metrics for your ticket classifier—not just accuracy, but how it reduces response time.
The DevOps Engineer ensures models actually run in production. Week 3: Set up alerts for when your model's performance drops below 90% on your ticket classification task.
Common mistake: Hiring an AI consultant without assigning an internal ops owner. The consultant leaves, and the model dies. I've seen this happen 17 times. Always build internal capability alongside external help.
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.
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