Vamsee Jasti

Every AI feature starts the same way. Someone builds a demo, it looks magical, and the room gets excited. Three months later, the team is drowning in edge cases and the feature still isn’t shipped.

I’ve seen this pattern enough times to know it’s not an execution failure — it’s an estimation failure. The gap between “this works in a demo” and “this works as a product” is wider for AI than for almost anything else in software. And PMs are usually the ones who underestimate it.

Here’s what I’ve learned about closing that gap.

The demo is the easy part

A demo needs to work on five examples. A product needs to work on five thousand. That’s not a difference of scale — it’s a difference of kind. In a demo, you’re showing the happy path. In a product, you’re absorbing every weird input, every ambiguous request, every combination of user behavior that nobody on the team anticipated.

The moment you move from demo to production, the distribution of inputs changes completely. Your carefully curated examples are replaced by real users who will do things you didn’t imagine.

Five things PMs consistently underestimate

Edge case volume. AI features don’t have clean failure boundaries. A traditional feature either works or throws an error. An AI feature can be subtly, confidently wrong — and that’s much harder to catch and much more damaging to trust. You need to plan for a long tail of bad outputs that won’t surface until real users interact with the feature.

Evaluation infrastructure. If you can’t measure quality, you can’t ship. And measuring quality for AI features is genuinely hard. You need eval sets — curated examples with expected outputs — before you write a single line of product code. Not after. This is the single biggest thing I see teams skip, and it always costs them later.

Latency budgets. The best model in the world is useless if users are staring at a spinner for eight seconds. Every AI feature has a latency tradeoff: smarter but slower, or faster but dumber. PMs need to own this tradeoff explicitly, not leave it as an engineering afterthought.

User trust design. Users don’t automatically trust AI outputs, and they shouldn’t. Your UX needs to account for this. Can users see why the AI made a decision? Can they correct it? Can they override it? Trust is designed, not assumed. If your feature just presents an AI output with no affordance for skepticism, you haven’t thought hard enough about the UX.

The cost of “almost right.” An AI feature that’s right 90% of the time sounds good in a planning meeting. But what does the other 10% feel like? If it’s autocompleting a search query, 90% is fine. If it’s summarizing a legal document, 90% is dangerous. The acceptable error rate depends entirely on the stakes of the task, and PMs are the ones who should be making that call.

A simple pre-ship checklist

Before greenlighting any AI feature for production, I now run through these questions:

Do we have an eval set with at least 100 representative examples? Do we know our accuracy on that set, and is it good enough given the stakes of the task? Have we designed the UX for the failure case, not just the success case? Have we tested with users who aren’t already bought into the concept? Is the latency acceptable on real hardware, not just our development machines?

If the answer to any of these is no, the feature isn’t ready — regardless of how good the demo looks.

The real lesson

The gap between demo and product isn’t a technical problem. It’s a product management problem. It’s about knowing which questions to ask early, setting quality bars before you’re emotionally invested in shipping, and designing for the 10% of cases where the AI gets it wrong.

The demo gets you buy-in. The product earns trust. They’re completely different things, and the PM is the one who needs to know the difference.