Teresa Torres’ Continuous Discovery is on everyone’s lips – and for good reason. The problem? Suddenly Product Owners, Requirements Engineers, and Developers are expected to run user interviews. Without a plan. Without a method. Without really knowing what they’re doing.

The result: superficial conversations that merely confirm what you already thought. Nice validation, zero new insights.

We do it differently. In this seminar, you’ll learn how to run proper user interviews – methodical, efficient, and packed with real insights. No pseudo-research, but solid craftsmanship that truly moves your product forward.

Why you should joinMethod over gut feeling

Run your first interviews with confidence – based on solid methodology, not on hope.

Real insights, not empty validation

Make sure your discovery activities deliver new learnings instead of just confirming assumptions.

An efficient end-to-end process

Learn how to design the entire interview process in a time- and resource-efficient way – without compromising quality.

AI as a smart assistant

Understand when and how AI tools can support you without watering down methodological quality.

Tools you can use right away

Leave with ready-to-go templates, checklists, and exercises you can put into practice immediately.

Practice meets theory

Benefit from the perfect balance between methodological foundations and pragmatic solutions for your day-to-day work.

What you’ll get

Continuous Discovery in context – opportunities and challenges

Straight talk on the basics:

• Why Continuous Discovery is crucial, but comes with methodological pitfalls

• The role of user interviews in Teresa Torres’ Opportunity Solution Tree

• How it differs from classic UX research methods

• Common pitfalls when product teams without research background get started

• What really matters for generating valid insights

The methodological foundations of qualitative interviews

Your toolkit for successful user research:

• Different types of interviews and when to use them

• Objectivity and neutrality in qualitative research – and why they matter

• Mental models and what they reveal about user needs

• Ethics and data protection – doing it the right way

• Spotting and avoiding biases – your personal bullshit detector

Planning and preparing user interviews

Laying the groundwork for success:

• Defining clear research goals and questions

• Finding and selecting the right participants

• Designing the perfect setup (duration, format, environment)

• Organizing logistics so nothing goes wrong

• Handling the tech (recording, remote vs. in-person)

Creating effective interview guides

From research questions to structured conversations:

• How to build and structure a great guide

• Formulating open, non-directive questions – the art of asking right

• Avoiding leading questions and priming traps

• Balancing problem space vs. solution space

• Hands-on exercise: create your own guide

AI support for guide creation

Smart use of modern tools:

• How AI can help develop questioning techniques

• Prompting strategies for methodologically sound AI-generated questions

• Reviewing and improving AI-generated guides

• Hands-on exercise: collaborative guide design with AI

• Knowing the limits – when human expertise is irreplaceable

Running professional user interviews

The art of conversation:

• Building rapport and trust – getting people to open up

• Active listening and verbal/non-verbal communication

• Handling tricky situations and making use of unexpected answers

• Asking follow-up questions and digging deeper

• Practice sessions in a safe environment

Common mistakes and how to avoid them

Learn from others’ experience:

• Typical beginner mistakes in user interviews

• Confirmation bias – hearing only what you want to hear

• Spotting social desirability – when users just want to be nice

• Walking the fine line between probing and leading

• Practical tips for self-reflection and quality control

Efficient analysis of qualitative data

From interview to insight:

• Core principles of qualitative data analysis

• Structured documentation and transcription

• Coding and thematic analysis

• Identifying patterns and insights – spotting the real gold

• From individual findings to generalizable insights

AI-powered interview analysis

Leveraging modern tools:

• Where AI makes sense for transcription and first-pass analysis

• Prompting strategies for AI-assisted theme and pattern detection

• Critically evaluating AI-generated results – don’t believe everything the machine says

• Integrating AI insights into the Opportunity Solution Tree

• Practical case: analyzing a transcript with AI support

Integrating into the Continuous Discovery process

Closing the loop:

• Feeding interview results into the Opportunity Solution Tree

• Sharing insights with your product team in a way everyone understands

• Moving from interview to validated opportunity

• Planning and establishing regular interview cycles

• Building a sustainable interview practice in your team