I spent $50,000 learning this lesson about 12 questions to ask during user interviews for an ai product the hard way. You can learn it in 10 minutes.
You've read all the blog posts about user research ai, but your product is still stuck. Why? Because most guides are generic and miss the point. This is the counterintuitive, step-by-step guide for founders who need to solve this problem, move fast, and get results without a massive data science team.
The Counterintuitive Truth
Here's what surprised me most about 12 questions to ask during user interviews for an ai product: the best practitioners do less, not more.
When I was building MovieLaLa, we tried to do everything at once. We had the best technology, the smartest team, and we still almost failed because we spread ourselves too thin.
The lesson I took from that experience, and from watching hundreds of other companies, is that the market doesn't care about your roadmap. It sounds simple. It's incredibly hard to execute.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to 12 questions to ask during user interviews for an ai product are fundamentally flawed. Not slightly off. Fundamentally flawed.
The root cause is usually one of three things:
- Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
- Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
- Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating 12 questions to ask during user interviews for an ai product. It's not complicated, but it requires discipline.
Step 1: the market doesn't care about your roadmap This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: your team matters more than your technology Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly Nothing works perfectly the first time. The companies in my portfolio that nail 12 questions to ask during user interviews for an ai product are the ones that treat it as an ongoing process, not a one-time project.
The AI Angle
I can't talk about 12 questions to ask during user interviews for an ai product in 2026 without mentioning AI. As someone who's invested in Anthropic, OpenAI, Scale AI, and Hugging Face, I have a front-row seat to how AI is transforming this space.
The short version: AI makes good practitioners better and bad practitioners worse. It's an amplifier, not a replacement.
I've seen companies use AI to 10x their 12 questions to ask during user interviews for an ai product capabilities. I've also seen companies waste millions on AI solutions that solved the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around user research ai, interviewing, best practices that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But here's the thing about 12 questions to ask during user interviews for an ai product: you don't need to get everything right on day one. You just need to get started and keep improving.
The founders in my portfolio who excel at 12 questions to ask during user interviews for an ai product share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
Frequently Asked Questions
Are these recommendations still relevant in 2026?
Absolutely. While specific tools and tactics change, the underlying principles remain consistent. I update my thinking regularly based on what I'm seeing in the market and across my portfolio companies.
How were these items selected?
Each item on this list comes from direct experience, either from building my own companies or from patterns I've observed across the 200+ startups I've invested in. I prioritize practical, actionable items over theoretical concepts.
Which item on this list has the highest impact?
It depends on your stage and context, but in my experience, the items near the top of the list tend to have the broadest applicability. That said, sometimes the less obvious items create the biggest breakthroughs for specific situations.