How to Evaluate AI Use Cases Before You Build

Most AI initiatives fail before they start

Many organizations approach AI backwards: they pick a flashy use case, build a prototype, and only then ask whether it solves a real problem. By the time that question gets asked, months of engineering time are already spent. A better approach starts with evaluation, not experimentation.

Three questions worth asking first

Before committing engineering time to an AI use case, we run it through three simple filters.

• Value: If this works perfectly, what does it actually change? Vague answers like “improve efficiency” are a warning sign — look for a specific metric that moves.
• Feasibility: Does the underlying task have a track record of AI models performing it well? Novelty is exciting, but proven patterns ship faster and more reliably.
• Data readiness: Is the data this use case depends on actually accessible, current, and trustworthy — or does it live in five different systems that don't talk to each other?

Score before you build

We ask teams to score every candidate use case against these three dimensions on a simple scale, then plot them. The use cases that score high on all three aren't just the ones worth building first — they're usually the ones that convince a skeptical board that AI investment is paying off, which makes the next round of investment easier to secure.

The cases that don't make the cut still matter

A use case that scores low on data readiness isn't necessarily a bad idea — it's a data problem to fix before it's an AI problem to solve. Flagging that distinction early saves teams from blaming a model for a data pipeline that was never built.

This is the first filter we run with every client before any code gets written — not because prototyping is risky, but because prototyping the wrong thing is expensive.