AI-powered research analysis from interview to Jira backlog
A travel technology provider uses Airtable to ingest multilingual research interviews, generate AI summaries and sentiment scores, and link every insight directly to Jira product backlogs. UX Research, Product Management, and Digital Marketing teams now find and act on feedback from a single searchable repository instead of scattered files.
The problem
Research findings lived in disconnected files on a company intranet site and in personal folders, with no consistent taxonomy and no way to trace a piece of feedback to the product decision it should have informed.
Multilingual interviews added another layer of friction: analysts spent hours manually translating and tagging before any synthesis could begin, and insights accumulated in silos that Product teams rarely reached.
What they built
A unified Airtable base ingests interview transcripts from document import and OCR, feeds them through an AI layer that generates summaries and sentiment scores, and tags each insight with a standardized research taxonomy.
Linked records connect every insight to the relevant Jira backlog item, so Product teams can trace any feature risk directly to the customer feedback that surfaced it without leaving their existing tools.
The outcome
Analysts spend time interpreting findings rather than formatting them. Multilingual content is translated, summarized, and tagged in the same automated flow, closing the gap between field observation data captured at customer sites and product decisions.
Product and UX teams share one repository with conversational search, so insight discovery that once required emailing across three functions now takes seconds.
Inside the solution
AI-Accelerated Research Analysis & Translation
Research & Knowledge Management
- Atomic research data capture and structured tagging
- AI-driven transcript summarization and sentiment analysis
- Linking insights to Jira product backlogs
- Cross-functional feedback ingestion
- Translation of multilingual interview content
- Conversational search and document import
- Research findings fragmented across a company intranet site and personal file stores
- Manual tagging of research evidence with no consistent taxonomy
- Difficulty correlating customer insights to product risks
- Loss of knowledge due to siloed storage across teams
- Language barriers slowing synthesis of multilingual research
- Faster insight discovery through a single searchable repository
- Full traceability from customer feedback to product feature
- Standardized research taxonomy applied automatically
- Reduced manual synthesis time via AI summarization
- Improved cross-functional access to research findings
- UX Research
- Product Management
- Digital Marketing
- Jira
- Outlook/Teams
- Document import
- AI summarization
- Automations
AI-Accelerated Research Analysis & Translation
Ingest multilingual research transcripts from document import, OCR or session intake, then let Airtable AI summarize each one, score sentiment, translate it, and tag it against a shared research taxonomy of insight type, method, product area and severity. Every insight links to the product backlog item it should inform, so UX Research, Product Management and Digital Marketing share one searchable repository where automations queue translation passes, raise feature risk on linked backlog items, and email a weekly digest.
From scattered interview files to Jira-linked insight records
Ingest and tag
Summarize with AI
Link to Jira
FAQ
Frequently asked questions
Each transcript record triggers an Airtable AI field that writes a structured summary and sentiment score directly into the record. Analysts review and adjust rather than starting from a blank page, cutting synthesis time substantially for both short usability sessions and long-format interviews.
A translation step runs inside the same automation as summarization, so a French or Spanish interview produces an English-language summary and tagged insight record in the same pass. Analysts do not need to translate before they can tag or share findings.
Linked records in Airtable connect each insight to a Jira backlog item via the native integration. Product managers can filter insights by feature area and see the supporting customer feedback without leaving their existing planning tools.
The taxonomy standardizes insight type, research method, product area, and severity so that any team member can search or filter the repository by the dimensions that matter to them. Tags are applied by the AI summarization step and confirmed by the analyst, keeping categorization consistent across researchers.
Yes. Product Management and Digital Marketing access the repository through a shared interface with role-appropriate views. Conversational search lets anyone surface relevant findings by keyword or tag without needing to know how the base is structured.