Startups with an AI mandate usually pick from three options — hire full-time, buy a tool, run a pilot. All three fail in predictable ways. The fourth option is the one nobody pitches.
The framing is usually binary: build or buy. The reality is that startups need a working AI system before the next board meeting, with neither the runway for a full-time hire nor the patience for a pilot that dies.
The Three Options That Fail
Issue-tree the obvious paths and they all leak:
- **Hire full-time too early.** Wrong person, wrong stage, runway gone before they ship.
- **Buy a tool.** A tool is not a system; you still own integration, governance, and adoption.
- **Run a pilot.** Pilots demo well and ship nothing — the idea-to-production gap, startup edition.
The Cashflow Lens
Each option has a hidden cost. The hire costs comp plus the time-to-productivity gap. The tool costs the license plus the integration labor you didn't budget. The pilot costs the runway burned while "exploring." The fourth option — fractional execution that ships a system — costs an engagement fee and produces a working system.
The Fourth Option
The fourth option is strategy plus a shipped system: not a deck, not a tool, not a pilot. A working AI system, owned and handed off, delivered on a startup timeline. The premise behind FACTA's startups solution: investors will ask about AI, and the startup needs a real answer plus a working system — not slides.
When Each Option Actually Wins
- Hire full-time when the AI work is the company's core and you're past product-market fit.
- Buy a tool when the workflow is generic and the tool covers it end-to-end.
- Run a pilot only when you genuinely don't know if the problem is solvable.
- Take the fourth option when you need a system, not a study, before a deadline.
Conclusion
Build-vs-buy is a false binary for startups that need a working system on a deadline. The fourth option — fractional execution that ships — exists because the first three keep failing at the same stage.
About FACTA
FACTA helps startups and growth-stage teams turn AI into production systems that keep running — not demos that impress once.
We design the architecture around the parts that actually break under real usage: tooling you own, credentials you control, failover, cost controls, observability. The boring infrastructure that keeps a system alive after launch.
Led by Matías Baglieri and Carolina Fogliato, we focus on one thing:
AI leadership that builds. Not just advises.
Tell us your next board-meeting deadline and the AI question you have to answer.
We'll tell you which option fits and what a shipped system would look like. See the fractional CAIO model for the leadership behind it.
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