5 Questions to Ask Before Adding AI to Your Product

AI integration is exciting and easy to start. That is exactly why so many teams get it wrong. Adding an LLM call to your app takes an afternoon. Making it useful, reliable, and affordable at scale takes real thought — and most teams skip the thinking because the starting is so easy.

We have helped a dozen-plus teams ship AI features in 2026. The ones that went well asked these five questions first. The ones that struggled skipped two or three of them.

1. What Does the User Actually Want?

Not "wouldn't AI be cool here" — what is the user trying to accomplish, and is AI genuinely the best tool for it? Half the AI features we get asked to build would work better, cheaper, and more reliably as a regex, a SQL query, or a well-designed form.

AI earns its place when the task needs language understanding, reasoning, or pattern recognition over messy unstructured data. It is expensive theatre when a deterministic rule would do the job 100 times cheaper and never hallucinate. Start by being honest about which one you have.

2. What's the Cost Per Interaction?

Every AI call costs money, and the bill scales with your users — the opposite of most software, where the marginal cost of one more user is near zero. You need to know your cost per interaction before you ship, not after.

We have seen apps where the AI feature cost more per user than the subscription charged. Do the math: tokens per call, calls per user, users per month. Then project it at 10x and 100x. If the unit economics do not work at scale, fix the design now, not after launch.

3. What Happens When the LLM Is Wrong?

It will be wrong. The question is what happens when it is. If a wrong answer is a minor annoyance the user can correct, great. If a wrong answer charges the wrong card, sends the wrong email, or gives confidently false medical or legal information, you have a serious problem.

Every production AI feature needs guardrails sized to the stakes: validation layers, structured-output schemas that force the model into known shapes, confidence thresholds, and human-in-the-loop for anything high-stakes. Never ship a feature where a single hallucination is a critical failure.

4. How Will You Measure Quality?

"It seems to work" is not a quality metric. Before you ship, decide how you will know if the AI feature is actually good — and how you will know if it degrades when a provider changes a model under you.

That means a test set of real inputs with known good outputs, a way to track accuracy over time, and a feedback signal from real users. Without this, you are flying blind, and you will not notice when quality drops until customers complain.

5. What's the v0 to v1 to v2 Plan?

AI features are never done at launch. The model improves, your data grows, users find edge cases. A good AI integration is built to evolve: start narrow with a v0 that does one thing reliably, expand scope as you learn, and have a clear path to swap providers or upgrade models without rewriting everything.

Teams that ship a giant "AI does everything" v1 almost always end up with something that does many things badly. Teams that ship a sharp v0 and iterate end up with something users trust.

The Honest Takeaway

AI integration is easy to start and hard to do well. The gap between the two is these five questions. Answer them before you write code, and you will ship an AI feature users actually use — not an impressive demo that quietly racks up an API bill and erodes trust.

If you are weighing an AI feature and want a straight assessment of whether and how to build it, we offer a free AI integration roadmap — the right pattern, the right provider, a cost model, and a realistic timeline, with no sales call required.