How We Evaluate Whether an AI Feature Is Worth Building

The most common client request right now is some variation of "we want to add AI to our product." When we ask what specific problem AI would solve, the answer is often vague. They want AI because competitors have it, investors expect it, or the technology feels inevitable. These are reasons to explore AI, not to ship it.

We built a decision framework after watching three clients spend between $15,000 and $40,000 on AI features that delivered negative ROI. One added AI-powered search to a tool where keyword search worked fine for 200 users and 5,000 documents. The AI search was slower, more expensive, and confused users accustomed to exact-match behavior. Another added AI-generated summaries to a dashboard that were occasionally inaccurate, eroding trust in the entire interface. The third built a chatbot that handled 30% of queries correctly and added a step for the other 70%.

Our CAVE framework evaluates four dimensions before we recommend building an AI feature. Cost compares the per-unit cost of AI versus the current approach, including API fees, infrastructure, monitoring, and the engineering time to maintain the feature. Accuracy defines the minimum acceptable threshold and measures whether AI can meet it on representative data before we commit to building. Volume determines whether the economics make sense by calculating breakeven in months. If breakeven exceeds twelve months, we push back hard. Effort captures the total engineering complexity: data pipelines, validation, fallback logic, monitoring, user interface, and ongoing prompt maintenance.

We score each dimension 1-5 and require a minimum total of 14 out of 20 to recommend proceeding. Below 10, we actively discourage the AI approach. Between 10 and 14, we recommend a time-boxed two-week prototype to gather more data before committing.

The framework has killed about 40% of proposed AI features across our client portfolio. In every case, we identified a simpler alternative: rule-based logic, improved search indexing, better UX design, or manual processes that were fast enough given the actual volume. The features that score highest share common traits: they replace repetitive human judgment at scale, have clear accuracy benchmarks, sufficient volume to amortize costs within six months, and graceful degradation when the AI is wrong.

Our advice: start with the problem, not the technology. If you cannot articulate the specific workflow AI improves, the measurable metric it moves, and the fallback when it fails, you are ready to explore, not to build.