A lot of startups default to hiring full-time AI specialists, even when the timing isn’t quite right. Long hiring cycles, salaries that stretch budgets, and break-even points that often take years to justify.
But in practice, the right AI talent model often depends less on ambition and more on stage, focus, and available resources.
In earlier-stage ventures, I’ve found that fractional leadership and external partners can move the needle faster. You get to test assumptions, ship experiments, and stay flexible, without the overhead of building a full in-house team too soon.
As you grow, it becomes important to bring more of that capability inside. But it doesn’t have to happen all at once. Keep strategic work close to the core, lean on specialists where needed, and build hybrid teams where learning flows both ways.
For larger orgs, a more federated approach often works best, central teams to drive standards, with embedded specialists working across products. It’s less about owning all the AI, more about making sure it shows up where it counts.
A few thoughts I keep coming back to:
- Align capabilities with real business needs — not just what’s trendy.
- Plan for transitions — strategy should evolve before the org chart does.
- Prioritize knowledge transfer — otherwise we’re renting output, not building capability.
Hiring still matters. But it’s not always the first step.
