Customer Interviews With AI: Scripts to Reduce Bias
AI can now transcribe, analyze, and even suggest follow-up questions during your customer interviews, but it can also amplify your bias if you’re not careful.
In this post, you'll learn how to use AI tools to reduce bias, structure interviews better, and turn conversations into clear insights faster. We'll go through practical scripts, metrics to track, and the biggest pitfalls to avoid.
Why it matters
Customer interviews are still one of the best ways to learn what people actually need. This is why customer interviews become critical for product management. But in 2025, doing them the old way, manually transcribing, looking for patterns yourself, and hoping you asked the right questions, just doesn't scale. This is where user research becomes critical.
With the help of AI, you can:
- Create bias-resistant scripts
- Capture and transcribe conversations in real time
- Track sentiment and pain signals
- Analyze patterns across interviews faster using data analysis
- Get to insights without losing context
But you still need to know what to ask and how to ask it.
Step-by-step playbook
1. Design neutral interview scripts with AI help
Use AI to review your questions for bias. Rephrase leading prompts like “Wouldn’t it be easier if…” into neutral ones like “Walk me through the last time you did...”.
Add branching logic by customer segment, and test everything with 2–3 people on your team before going live.
2. Set up real-time transcription + live analysis
Use tools like Otter or Claude to transcribe live, detect sentiment, and even suggest follow-ups. Set privacy protocols before recording anything.
Be careful: don’t let AI prompts distract you mid-call. Your job is to listen.
3. Add bias detection guardrails
AI can now flag confirmation-seeking phrases like “don’t you think…” and help randomize question order to avoid priming. After the interview, you can get a bias score on your questions.
4. Analyze patterns across interviews
Aggregate 10–20 interviews, then use AI to detect themes, rank pain intensity, and surface quotes that support key problems.
Don’t just trust frequency, look for depth and user emotion too.
5. Turn interviews into actions
Once you have themes, translate them into problem statements, hypotheses, and next research areas. This is where problem validation becomes critical. Update your personas and product roadmap with quotes and patterns that surfaced clearly.
Example interview script
Pre-interview:
- Name, Role, Company
- Goal: Understand how they solve [problem]
- Tools: AI transcription + bias detection
Opening:
“Thanks for joining. I'm exploring how people handle [problem]. There are no right answers, I’m just interested in your experience.”
Problem discovery:
- “Walk me through the last time you dealt with [problem]”
- “What made that experience difficult or memorable?”
- “How do you usually approach it today?”
Current solutions:
- “Can you show me how you solve it now?”
- “What happens when that doesn’t work?”
- “Who else is involved?”
Outcomes:
- “What would an ideal solution look like?”
- “What would need to change for you to try something new?”
Closing:
- “What didn’t I ask that I should’ve?”
- “Is there someone else I should talk to?”
What metrics to track
- Response Quality Score: Measures how clear and unbiased your questions were, and how thoughtful the answers were.
- Bias Detection Rate: How often AI flagged your phrasing as biased.
- Insight Confidence: How many interviews support each insight, and how consistent the patterns are.
- Time to Insight: How long it took to go from interview to usable insights.
- Engagement Score: Were the interviews long enough? Did people answer follow-ups?
- Pattern validation Rate: How many of the insights were confirmed in future tests or data.
Mistakes to avoid
- Letting AI interrupt flow, use it after the call, not during.
- Asking compound or confusing questions because AI suggests too many ideas.
- Taking AI-detected patterns as truth, validate them.
- Ignoring outliers that challenge your assumptions.
- Using transcription errors as evidence without checking the recording.
- Applying generic scripts to all users, always adapt to your domain.
FAQ
Can AI handle diverse accents?
Mostly, it works well on clear audio but drops in accuracy with heavy accents or jargon. Always review key quotes manually.
How many interviews do I need?
You can start seeing patterns at 10, but aim for 15–20 for B2C, 25+ for B2B.
What about privacy?
Get consent, use encrypted tools, and delete recordings when done. Don’t store identifiable data in AI models.
Does this replace other research?
No. AI interviews are great for speed, but they should complement behavioral data and usability testing.
Why CraftUp helps
We teach customer interviews the way modern builders actually run them, fast, AI-enabled, and focused on real insight.
- 5-min daily lessons on interview tactics, AI tools, and bias reduction
- Practical scripts and examples you can copy
- Updated frameworks for 2025, no fluff, no filler
Try it free at craftuplearn.com
Ready to master product management fundamentals and conduct better customer interviews?
