How to choose the right AI use case to build

The most common failure pattern in AI adoption isn't a technical one. It's a strategic one. Teams build something technically impressive — a chatbot, a recommendation engine, a predictive model — and then find that nobody uses it, or that it doesn't move any metric that matters.

The root cause is almost always the same: the use case was selected based on what was technically possible, not what was operationally valuable.

Before committing to a build, every AI use case should pass three tests: it needs to address a real problem that the business actually has, the problem needs to occur frequently enough to justify the investment, and there needs to be data available to train or prompt the model effectively.

If any of these three conditions aren't met, the project is likely to underdeliver. Not because the AI failed — but because the conditions for success weren't in place before the first line of code was written.

The most useful reframe is this: don't start by asking 'where can we use AI?' Start by asking 'what decisions do we make repeatedly that could be made better or faster?' That shift in framing tends to surface use cases that are both high-value and tractable.

Document the current process. Identify the decision point. Understand what inputs are available. Then assess whether AI can improve that decision — not just automate it.

In a recent engagement, a client came to us wanting to build a customer-facing AI assistant. After a short discovery process, we identified that the bigger opportunity was an internal tool that would help their support team resolve tickets 40% faster. Less visible externally, but far more impactful on the metrics that mattered.

The best use case isn't always the most exciting one. It's the one with the clearest path from implementation to business outcome.