AI product design: why most AI features don't actually work

We've all seen it in recent years: you're using a SaaS product that works fine, then one day they add an AI button. It glows, saying "AI-powered", and does something vaguely useful that nobody asked for.

That's not AI product design, that's a checkbox.

The copilot problem

Somewhere along the way, the industry decided that AI product design means "add a chatbot to the sidebar." Every tool now has a copilot and most of them are mediocre. That's because they are bolted on, not built in.

Good AI product design starts from a different question. Not "where can we add AI?", but "what should this product do that was previously impossible?". That's a much harder question. It's also the only one worth asking.

Design for the workflow, not the model

Most AI products are designed around the model's capabilities. "GPT-4 can summarize text, so let's add a summarize button." Cool, but nobody's workflow changed.

Great AI products are designed around the user's workflow. What's the most painful, repetitive, high-stakes part of their job? Can AI remove that pain entirely? Not assist, not reduce, but completely removing the pain, that's the bar.

A document review tool that highlights risky clauses is helpful. A document review tool that drafts the response, flags the risk level, and routes it to the right person, that's a product.

The three laws of AI product design

We're not Alan Turing, but we've learned a few things building AI products.

1. Invisible beats impressive. The best AI features don't announce themselves. They just make the product feel faster, smarter, and more intuitive. If your user has to think about the AI, you've already lost.

2. Confidence needs calibration. AI that's wrong confidently is worse than no AI at all. Good product design includes uncertainty. Show the user when the model isn't sure, let them override and build trust through honesty and transparency.

3. Feedback is the product. Every AI product should get smarter over time. That means designing feedback loops from day one. Not as a roadmap item but as a core feature. If your users can't correct the AI, your product has a shelf life.

The design-engineering gap

Here's a dirty secret of the AI industry. Most AI products are designed by engineers, and most designers don't understand AI well enough to push back. This results in products that are technically impressive but also practically confusing.

At StackHavn, we don't separate product design from engineering. We believe they are the same discipline. Our product thinking shapes the architecture while thinking about the user experience.

The latter is not a nice-to-have. It's the whole point of being an AI product studio instead of a dev shop.

What good looks like

Good AI product design is boring from the outside. The user doesn't marvel at the technology, they just get their job done faster.

Here's what we look for in every product we build. Does it save the user time? Does it reduce errors? Does it work without a tutorial? Can it recover gracefully when the model is wrong? If the answer to all four is yes, you've got something. If you're relying on a purple glow and the word "AI" in your marketing, you don't.

Start with the problem

We have a lot of conversations that start with "we want to build an AI tool that...".

We always rethink them into "Our users struggle with...".

That slight change "from technology to problem" makes all the difference.

AI is a means, never the end.