AI-enabled products are on the rise, promising intelligent solutions across industries—from predictive recommendations to automated workflows and smart assistants. But if you’re building with AI, one fundamental question should guide your development process:

Do you have AI–Problem Fit?

As highlighted by Productboard’s lead designer Michal Matyščák, achieving AI–Problem Fit is the make-or-break factor for any successful AI initiative—and it’s too often overlooked.

What Is AI–Problem Fit?

AI–Problem Fit means deeply understanding the user’s problem before applying machine learning as a solution. It’s not about chasing trends. It’s about asking:

“Is AI truly the best way to solve this specific problem?”

If yes, you're on the right track. If not, you should be solving it with simpler, more effective tools.

Why Research Comes First

Too many teams rush into building AI-powered features without understanding:

• Whether the problem actually needs AI
• Whether the data is even sufficient or high quality
• What outputs the model will provide—and how users will interact with them

This is where traditional user research takes on new urgency.

✅ Validate the Data Model First

Before even thinking about wireframes or interfaces, check:

• Is the data accurate and representative?
• Can the model be trained reliably?
• Are there known limitations or uncertainties?

This early validation shapes everything that comes after—from interface design to user messaging.

Designing Around the AI Model

Once you've validated your AI model, design teams can begin to craft interfaces that reflect the model’s capabilities and limitations. This includes:

• Communicating uncertainty clearly
• Setting realistic expectations
• Ensuring transparency in decision-making

For example, if the AI model can’t guarantee accuracy in a certain scenario, your UI needs to show that—possibly with confidence scores, fallback options, or explanatory tooltips.

Ethics, Bias & Transparency

Every AI system inherits bias from its training data. Early research allows teams to:

• Detect and surface these biases
• Design solutions that promote fairness
• Empower users with control and visibility into how decisions are made

Building trust with users starts with acknowledging limitations—and designing ethically from the ground up.

Iteration: AI + Prototypes + Feedback Loops

The best AI products don’t launch fully formed. They’re co-developed with users through rapid prototyping and real-world feedback.

• Build lightweight prototypes based on model outputs
• Test these with real users
• Feed their reactions back into both the design and the model

This tight feedback loop ensures both your AI and UX are constantly improving—together.

Scaling Research with Repositories

To build truly user-centric AI products, feedback can’t live in silos.

Imagine centralizing research across:

• User interviews
• Support tickets
• Sales calls
• App usage logs

A research repository, like the one proposed in this thesis, transforms scattered feedback into a searchable, shareable system of record. Think Zapier for research—connecting tools like Typeform, Zoom, Intercom, and Github into one cohesive insight hub.

This enables:

• Cross-functional collaboration between PMs, designers, engineers, and data scientists
• Real-time visibility into customer needs
• Faster, more informed decision-making at every stage

Smarter Tools, Smarter Teams

Future-ready research repositories aren’t just databases. They’re dynamic collaboration tools that include:

• AI-driven sentiment analysis to detect emotional tone
• Thematic clustering for surfacing core user needs
• Predictive analytics to spot emerging trends

These tools empower teams to move faster, build smarter, and stay ahead of user expectations.

Building the Future: AI x UX

I-enabled products don’t just require better models—they demand better product thinking. That means:

• Grounding every decision in user research
• Validating your model before you design
• Collaborating across teams with shared, searchable feedback
• Designing interfaces that reflect the reality—not just the promise—of AI

We're just at the beginning of this transformation. As product-led growth continues to rise, the teams that master user research for AI will have the ultimate competitive edge.

Conclusion

TL;DR
To build great AI products, you need more than smart algorithms. You need:

• AI–Problem Fit
• Data-first design
• Transparent UX
• Iterative prototypes
• A collaborative, searchable research system

Insight is the new infrastructure. And it’s time we built the tools to harness it.