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User interviews provide some of the richest insights a product team can uncover. But turning hours of recordings and transcripts into clear insights can often be slow and manual without the right tools.
With automated insights and AI Chat in Optimal Interviews, you can accelerate that entire workflow, from extracting insights from interview recordings to transforming them into outputs that fit directly into the tools your team already works in.
Instead of spending hours summarizing transcripts and translating research into stakeholder updates, AI Chat helps you quickly generate structured outputs for documentation, tickets, and decision-making.
Deliver Interview Insights Directly into the Tools Your Team Uses
AI Chat can surface key themes, quotes, and patterns across participant recordings. Once insights are identified, it can quickly transform them into formats your team already uses.
You can control the output by specifying tone, length, structure, and level of detail directly in your prompt. The more explicit you are about the format you want, the better the output.
Simply specify the details of the deliverable you want, and AI Chat can structure the output for documentation, planning, and product tools.
Here’s how teams can use AI Chat with some of the most common product, design, and research tools.
Notion
Notion is used by many teams for documentation, knowledge bases, product planning, and research repositories.
Example AI Chat prompts
- Turn these interview insights into a structured Notion research summary with sections for Key Findings, Supporting Quotes, and Recommendations.
- Create a Notion page outline summarizing onboarding interview insights with headings and bullet points.
Jira
Jira is a widely used issue tracking and project management platform that product and engineering teams rely on to manage work, track bugs, and plan development tasks.
Research insights often lead directly to product improvements, and AI Chat can translate insights into actionable tickets.
Example AI Chat prompts
- Convert these interview insights into three Jira tickets including title, description, and acceptance criteria.
- Turn this usability issue into a Jira bug ticket.
- Create a Jira epic summarizing onboarding improvements suggested by interview feedback.
Linear
Linear is a modern planning and issue tracking tool designed for fast-moving product teams. It’s often used for planning product work, managing projects and engineering tasks, and tracking product improvements.
AI Chat can quickly convert insights into structured Linear issues.
Example AI Chat prompts
- Convert these insights into Linear.app issues with clear titles, descriptions, and priority levels.
- Create a Linear.app issue summarizing the navigation problem identified in these interviews.
- Generate a set of tasks for the Linear.app addressing usability problems mentioned by participants.
Confluence
Confluence is a team collaboration and documentation platform used to share knowledge, publish research reports, and maintain internal documentation.
AI Chat can help transform research findings into polished documentation ready for stakeholders.
Example AI Chat prompts
- Turn these interview insights into a Confluence page with sections for Background, Findings, and Recommendations.
- Create a Confluence page explaining the usability issues uncovered in onboarding research.
- Turn opportunities to improve into concise post-it notes, with one key point per note, written in simple, scannable language to use in a Confluence whiteboard.
Best practice tip: For cleaner, copy-and-paste-ready outputs, consider adding “Do not include citations.” to any of these suggested prompts.
Accelerate the Impact of User Research
By combining automated interview insights with AI Chat, teams can quickly move from recordings to structured insights, and share them in formats that resonate with internal teams and stakeholders.
This makes it easier to clearly communicate what users are saying, build alignment across product, design, and engineering, get buy-in, and turn research insights into decisions that teams are ready to support and action.
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