Advisory · 7 min read
Hiring vs Partnering: How to Staff Your First AI Project
When to build an internal AI team, when to bring in a specialist partner, and how to structure engagements that transfer capability.
ferrislabs Advisory · September 2024
Key takeaways
- Hire generalists early and specialists later.
- Partners accelerate delivery when internal expertise is thin.
- Insist on knowledge transfer so your team can own the system.
Most African enterprises do not have a bench of ML engineers, data scientists and MLOps specialists. That is fine. The question is how to get from zero to one without overcommitting to a large permanent team or becoming dependent on a vendor who never leaves.
Start with a delivery partner
For the first one or two AI products, a partner can provide the architecture, implementation and training while your team learns. Choose a partner who documents decisions, pairs with your engineers and hands over runbooks. Avoid black box deliverables.
- Look for partners with local deployment experience and relevant domain knowledge.
- Structure contracts around outcomes and knowledge transfer, not just hours.
- Plan for a transition phase where your team takes over support.
Build the internal team gradually
Your first hires should be versatile: a data engineer who can also model, a product manager who understands analytics, and a software engineer comfortable with APIs and cloud. Add deep specialists such as ML researchers or prompt engineers only after you have a pipeline of advanced work.
“The goal is not to outsource forever. It is to learn fast and own the capability.”