Before You Add AI to Your Product, Ask These 7 Questions

The pressure to add AI to your product — from competitors, investors and a near-daily stream of industry announcements — is tangible.

But before committing to an AI feature or tool, you need to ask yourself these seven questions first.

1. Are you solving a real problem?

I think most bad AI features start with the question, “Where can we add AI?” rather than “What are our users struggling with?”

When you start from the technology side, you end up building for the demo, not for the user. A chatbot that answers questions in natural language sounds impressive in a presentation, but it doesn’t show whether users had that problem in the first place or whether they’d actually reach for this over whatever they already used.

For example, if an AI assistant gets added to an onboarding flow that users already complete without friction, it adds no value, may hurt the experience and still costs real money to maintain.

2. Is your UX actually the problem?

When users are dropping off or getting confused, the instinct might be to add an AI assistant. But AI layered on top of a broken flow just adds complexity and gives users a more sophisticated way to get lost. Which is why, before reaching for AI, ask whether a cleaner interface, a better tooltip or a shorter form would solve the problem. 

A product that adds a chatbot to help users find features may find the real fix was reorganizing the navigation menu, a change that takes less time to ship and costs nothing to maintain.

If a purpose-built tool exists for what you’re trying to achieve, use it.

Google Maps is better at navigation than an AI chatbot. A weather API is more reliable for forecasts. A spreadsheet formula will outperform a language model on compound interest by being right every time. That’s because these tools were built to do one thing perfectly — AI does many things adequately.

4. Is AI accurate enough for this task?

For tasks with a single correct answer, a deterministic tool will serve your users better than AI (think mortgage payments, tax estimates, unit conversions). AI models don’t compute answers; they predict how an answer should look based on patterns in their training data, and the results can be close or off by a wide margin.

In fact, in the third iteration of our ORCA Benchmark (Omni Research on Calculation in AI), which tests leading AI models on math and logic tasks, the best-performing free model (Grok 4.20) scored 70.4% accuracy, while Claude and ChatGPT came in at 53.2% and 48.4%, respectively.

What makes those numbers worse is what we call the Instability Metric: how often a model reverses a correct answer when you push back. For both Claude and ChatGPT, that rate sits between 60% and 65%.

5. Do the economics add up?

For low-volume or low-margin use cases, AI can cost more than it’s worth. Every API call costs money, and a lookup table or a simple formula can usually do the same job for a fraction of the price.

Furthermore, models change often, integrations can break, and each of those is engineering time you’ll need to budget for. A small ecommerce business that adds an AI product recommender on every page load may find that the API costs exceed any conversion lift it generates.

6. Will your users actually adopt it?

An AI feature that works can fail if users don’t adopt it.

Skepticism toward AI is high these days, and some users may avoid it, regardless of how well it performs. That’s why, before building, it’s worth asking: Have your users actually asked for this, or are you assuming they will?

Shipping something nobody uses is a cost, in engineering time and maintenance, among other things.

7. Will one mistake cost you more than this feature is worth?

If an AI mistake would cost you more than the feature is worth, don’t build it.

Take a hypothetical accounting tool that surfaces an AI-generated tax estimate. The upside of the feature is convenience: The user saves a step or two. But if you get it wrong, the client won’t just distrust that feature; they’ll question the whole product.

In services where people trust you with their health, money or legal exposure, that trade-off is hard to justify.

So where does this leave you?

At Omni Calculator, we use AI across the business, and we’re currently beta-testing a way for Omni users to create their own calculators with AI. The point is that AI works well in some contexts and poorly in others, and the cost of getting that wrong, in engineering time, user trust and opportunity cost, is higher than some teams might account for.

The pressure to move fast on AI isn’t going away. But the teams that slow down long enough to ask these questions first will ship features users reach for.