An AI portfolio you can't kill is a portfolio you can't manage. The discipline isn't picking winners — it's killing the projects that won't ship before they eat the runway that funds the ones that will.
AI portfolios drift into petting zoos because killing a project is politically harder than funding it. The prioritization framework that works is the one that makes killing cheap and funding deliberate.
The Issue Tree: Fund, Watch, or Kill
Every AI initiative sits in one of three boxes, reviewed quarterly:
- **Fund.** Tied to an outcome, has an owner, on a date.
- **Watch.** Promising but unproven — small budget, short deadline to prove it.
- **Kill.** Not tied to an outcome, no owner, or past its deadline.
The discipline is moving things down the funnel: watch to fund, or watch to kill — on a date, not a feeling.
The Cashflow Lens
Treat the portfolio like an investment book. Each initiative is capital deployed against an expected return. No return, no date, no kill criterion? It's not an investment — it's spending. A portfolio full of spending is how runway disappears.
The Test That Forces Honesty
The test for each initiative: if we killed it tomorrow, what would break? If the answer is "nothing the board cares about," kill it now. The initiatives that survive this test are the ones the board would miss — and those are the only ones worth funding.
What Kills the Portfolio
- Every initiative is "important" — so none is.
- No quarterly kill review — so nothing dies.
- The portfolio is a list, not a funnel — so nothing moves.
Conclusion
AI portfolio prioritization is the discipline of killing the projects that won't ship, quarterly, on a test the board would recognize. The portfolio that can't kill is the portfolio that can't ship.
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.
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