AI transcription tools

Definition

Use cases

• Without AI: You spend hours re-listening to a 60-minute interview trying to find one quote about the checkout flow. Most teams end up relying on memory, and memory is biased.

• With AI: You search “expensive” and instantly find every moment users talked about pricing. You pull those clips into a short reel and show stakeholders exactly where users struggled.

How it's used in practice

• Live transcription: Generate transcripts during interviews so researchers can flag important moments without stopping the conversation.

• Speaker separation: Automatically split moderator questions from participant responses so synthesis is faster later.

• Interview summaries: Generate quick recaps after a session finishes so teams can review key takeaways before research synthesis starts.

Challenges & limitations

• Privacy & consent: Sensitive user data may pass through third-party servers. Teams still need clear recording consent and security reviews.

• Transcript accuracy: Poor audio, accents, overlapping voices, or technical jargon can create transcripts that look polished but contain factual mistakes.

Commonly used tools

• Sonix — Fast multilingual transcription with support for 50+ languages.

• Otter.ai — Real-time captions and "AI Chat" to query meeting history. Best for internal team meetings.

• Dovetail — Research repository with transcript search, tagging, and clips for UX research teams.

• Descript — Edit audio and video directly through transcript editing.

• Fireflies.ai — Records meetings automatically and generates searchable summaries for internal teams.