User interview questions ux: how to use JTBD interview questions to uncover user experience insights

If you’re exploring user interview questions UX teams should ask, you’re in the right place. This guide offers practical strategies for analyzing the rich responses generated by Jobs-to-Be-Done interviews.

JTBD interviews go deeper than surface-level preferences, uncovering what users are truly trying to accomplish with your product. If you want to create insightful interviews without fuss, try the AI survey generator to get started quickly.

Understanding Jobs-to-Be-Done interviews for UX research

The Jobs-to-Be-Done (JTBD) framework is all about figuring out what “job” a user is trying to get done when they use your product. Instead of just asking people what they like, JTBD interviews try to unearth why users “hire” your service—what they want to achieve and why.

Traditional UX interview questions typically focus on likes, dislikes, or feature requests. In contrast, JTBD interview questions dig for motivations and unmet needs. When I use these interviews, I’m aiming to understand job stories—user situations, deeper motivations, and desired outcomes rather than just opinions.

Here’s a quick look at how the approaches differ:

One thing is clear: these questions reveal the “why” behind every “what.” And with 73% of UX professionals believing AI has a positive impact on design, blending AI with JTBD questioning takes insights to a deeper, more actionable level. [1]

Example JTBD interview questions and job stories

You’ve probably seen the job story framework before: “When [situation], I want to [motivation], so I can [expected outcome].” It’s a fantastic way to pinpoint what matters in the user’s world.

Let me share a few job stories and example question sets you could use in UX research:

Job story 1:
When I’m starting a new project at work, I want to quickly organize my tasks, so I can focus without feeling overwhelmed.

• Context question: Can you walk me through the last time you started a new project?

• Motivation question: What made organizing your tasks important in that moment?

• Desired outcome question: How did you know you were organized enough to get started?

What was happening that made you decide it was time to organize your tasks?

Job story 2:
When I’m stuck on a technical problem, I want an easy way to find trustworthy answers, so I can get moving again.

• Context question: Tell me about a recent time when you were stuck on something technical—what did you try first?

• Motivation question: Why was finding a trustworthy answer so important right then?

• Desired outcome question: What makes an answer “good enough” for you to proceed?

Were there any points where you doubted the solutions you found? What did you do next?

Job story 3:
When I share a file with my team, I want to know everyone got the right version, so I can avoid confusion down the line.

• Context question: Share a story about a time you needed to send out an important file to your team.

• Motivation question: What worried you most about sharing files?

• Desired outcome question: How do you feel when you know everyone has the right version?

Did you do anything to double-check that everyone received what they needed?

Job story 4:
When I’m researching a big purchase, I want to see real user feedback, so I can make a confident decision.

• Context question: The last time you made an important purchase, how did you look for information?

• Motivation question: Why does seeing real user feedback matter to you?

• Desired outcome question: What convinced you that you had enough information to buy?

If you had doubts, what finally tipped you toward making a decision?

All these examples ground the conversation in user reality, letting you build UX improvements on solid ground. If you’d like to see more prompt examples or automate these interview steps, try the intuitive AI survey builder from Specific.

Using AI follow-up questions to uncover hidden jobs

One of the incredible powers of conversational surveys is the ability to automatically probe—using smart, dynamic follow-up questions instead of rigid scripts. With AI, the survey keeps digging until the heart of the user’s job emerges. These “why” layers reveal motivations we’d never get from a standard form.

With platforms like Specific, you can add AI-powered automatic follow-ups that ask questions on the fly, adapting to each respondent:

Why was that approach important to you at the time?

What got in your way while trying to achieve that?

If you hadn’t used our product, what would you have tried instead?

What made you stick with your solution—or decide to switch?

By automatically probing for push and pull forces—what drives a user toward (or away from) your solution—these followups make surveys feel like genuine conversations, not interrogations. This conversational survey style isn’t just more natural; it dramatically increases respondent engagement, as users are treated like partners in innovation. And with 75% of online shoppers favoring self-service options like AI-powered chatbots, it’s clear the preference isn’t just on the researcher’s side. [2]

Analyzing JTBD interview data with AI

Once your data is in, there’s real magic in segmenting responses—by user type, behavior, or context—to see how different groups experience “the job.” I use Specific’s AI survey response analysis chat to make sense of these open-ended interviews, surfacing patterns that would take hours to spot by hand.

You can ask AI to spot themes, extract job stories, or even flag unique push/pull factors:

Summarize the most common situations people describe before hiring our product.

Which desired outcomes show up most frequently among power users?

Identify the main push and pull factors in these conversations.

What are the top three competing solutions our users mention before they choose us?

Extracting clear job statements and mapping how users talk about frustration, motivation, or satisfaction turns raw interviews into a product roadmap. AI chats help by clustering responses, finding “hidden jobs,” and distilling nuanced insights instantly.

One powerful advantage: AI quickly identifies competing solutions users consider—letting you benchmark against real-world alternatives, not just hypothetical ones.

With 70% of UX research teams now using AI for analyzing large user data sets, these tools aren’t futuristic—they’re how the best teams operate today. [3]

Turning JTBD insights into UX improvements

It’s not enough to collect jobs—you have to act on them. My process starts with mapping out all identified jobs from interviews, then connecting those jobs to product solutions or UX changes. I always prioritize by both frequency (how common is the job?) and importance (how painful is it if it’s unmet?).

If you skip JTBD interviews, you’re leaving critical insights on the table—missing why users get frustrated, what jobs are left unsolved, and where your next UX breakthrough could be.

With 62% of organizations now using AI-based customer journey mapping tools, there’s no reason not to put these insights to work. [1]

Start uncovering your users' jobs

I’ve seen firsthand that understanding what users actually want to accomplish—beyond features—unlocks a level of product clarity that makes all the difference. If you want to run conversational JTBD interviews that adapt intelligently and analyze results on demand, Specific offers a user experience that makes these deep insights actionable. Create your own survey now and see how much more you can learn about your users with conversational Job-to-Be-Done research.