The short version
Most AI use cases never reach production, and it is rarely the model's fault. The ones that ship share three traits: a clear owner, data that already exists, and a business metric to justify them. Pick use cases with those traits and you ship. Chase the flashy, data-hungry ones first and you stall.
The use cases that reliably ship
- Support automation. Deflect repetitive tickets or messages with an assistant grounded in your own knowledge, with a human in the loop. High impact, quick to build.
- Document and quote automation. Turn intake data into quotes, contracts and reports. Clear ROI, uses data you already have.
- Internal knowledge assistant. One place to ask "how do we do X" and get the right answer. Saves time across the whole team.
- One high-value in-product feature. For B2B SaaS, a single AI capability users actually feel, wired to a metric. Higher effort, high payoff.
Why the others stall
The use cases that die tend to be the ambitious, data-hungry ones picked first for how impressive they sound. The data was clean in the demo and messy in reality. Nobody owned the path to production. And there was no metric, so nobody could justify shipping.
How to choose
Score each candidate by impact and effort, then filter by the three traits: owner, data, metric. Ship one, measure it, and let the win fund the next two. That sequence is the difference between an AI initiative that compounds and one that fragments into dead POCs.