Product building with AI - the exercise you need
Designing data products is kind of like hosting a dinner party for people with wildly different appetites. Some want dashboards. Some want raw data. Some want magic insight at the push of a button. You’re left trying to cook something everyone will eat—while hoping they come back hungry for more.
This is where AI can help. Not in a “chatbot for everything” kind of way—but as a thoughtful assistant who can lighten the cognitive load, nudge you in the right direction, and maybe even spot what your users aren’t saying out loud.
Here’s how AI is becoming a secret weapon for product builders designing data products:
1. AI Helps You Zoom In on User Behavior (Without a Team of Analysts)
One of the hardest parts of designing great data products is understanding what users are actually doing. Are they downloading a CSV every day at 9 AM? Are they getting stuck on a particular filter combo? Are they opening your dashboard and bouncing faster than a popup?
AI-powered product analytics tools can now surface these patterns faster than you can run a SQL query. However, you still need to decide what it all means—but AI gets you to the “aha” faster.
2. AI Speeds Up Prototyping and Reduces Design Guesswork
Building good UX for data is… not easy. It's too easy to throw twenty charts on a page and call it a day. But what if AI could help you prototype visualizations, suggest layouts based on user flows, or even recommend which metric to highlight based on past usage?
Even sketching out product logic in natural language is getting easier—so the distance between idea and interface is shrinking.
3. AI Helps Translate “Data Nerd” to “Human”
Let’s be real—data teams sometimes build for data teams. If you’re designing a product that needs to serve both power users and non-technical folks, AI can bridge the gap.
Natural language querying (e.g., “Show me our top 5 churn reasons last quarter”) is getting more reliable. With tools like ThoughtSpot, or embedded GPT agents, you can make a data product feel more like a conversation than a spreadsheet.
This doesn’t mean ditching SQL. But it does mean building interfaces that invite more people into the data—not just the ones who know how to write joins.
4. AI Can Test Ideas at Scale Before You Commit
Want to test a new recommendation algorithm? Personalize dashboards based on user roles? Build a custom cohort analyzer? AI can help you simulate outcomes before you ship the thing.
This is especially helpful when you're trying to prioritize features. You can model likely impact, spot bias, or test what a personalized experience would feel like—without writing thousands of lines of code.
AI Doesn’t Replace Judgment—It Enhances It
Think of AI as a co-pilot for product intuition. It won’t replace user interviews. It won’t stop you from needing to deeply understand the problem space. But it will help you move faster, make smarter bets, and spend more time designing with your users in mind—not just crunching numbers behind the scenes.