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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Use AI to help find patterns in customer interviews, not to decide what customers need on its own. Start with a focused research question, read each transcript in context, and use AI for bounded tasks such as suggesting codes or retrieving relevant passages. Then verify every candidate finding against the original interview, compare cases—including evidence that challenges the pattern—and document how AI influenced the analysis.
What AI can—and cannot—do in interview analysis
AI can assist with transcription, locating passages, proposing candidate codes, and comparing mentions across interviews. These are ways to make qualitative analysis more manageable, not proof that a generated theme is accurate or representative. The researcher remains responsible for interpreting what participants meant and deciding whether evidence supports a customer need.
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The central safeguard is traceability: every proposed finding should link to the source interview, a verbatim passage, and enough surrounding context to check its meaning. The CDC Field Epidemiology Manual describes the foundation of qualitative analysis as “careful, systematic, and repeated reading of text to identify consistent themes and interconnections emerging from the data” (CDC, “Collecting and Analyzing Qualitative Data”).
Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches1. Define the decision and research question
Write down which product or service decision the study should inform, then phrase one focused question. For example, “What prevents first-time users from completing setup?” gives the analysis a clearer purpose than “What did people say about the product?” A focused question helps you decide which evidence is relevant without turning the analysis into a collection of disconnected observations.
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Keep a brief analysis memo as patterns emerge. Note what you are investigating, what you currently think the evidence suggests, and what remains uncertain. The CDC recommends connecting qualitative analysis to research questions and using memos to track developing ideas.
2. Confirm consent and prepare the material
Before recording interviews or uploading transcripts to an AI service, use an appropriate consent process and check the requirements that apply to your organization, study, and jurisdiction. Digital.gov advises giving participants an opportunity to provide informed consent and to withdraw from a study (Digital.gov, “Step 3: Plan your research”). The specific legal and institutional duties vary; the guidance cited here does not determine which rules apply to a particular study.
Keep recordings and transcript versions organized, and review transcripts against the source when accuracy matters. The UK Government’s guidance recommends transcribing recordings and reviewing transcripts soon after interviews (UK Government, “Interview study: qualitative studies”). Remove names and identifying details where required by your study commitments, and check an AI provider’s actual data-processing, retention, access, residency, and plan-specific controls. A vendor’s security or anonymization feature claim alone does not establish that the service is appropriate for your data.
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3. Read the full interviews before coding
Read each complete transcript—or a faithful transcript alongside the recording—before asking AI to code isolated excerpts. Context can change the meaning of a sentence: a participant may describe a workaround as temporary, or mention a problem that applies only to a particular task. Record a short case memo covering the participant’s goal, situation, friction, workaround, and any uncertainty.
Do not treat a set of extracted quotations as a substitute for understanding the interview’s flow. Repeated reading can reveal connections that are hard to see when analysis begins with snippets alone.
4. Ask AI to do bounded, checkable tasks
Give the tool the research question and ask for a limited output you can verify—for example, candidate codes, passages describing a workaround, or mentions of onboarding friction across interviews. Request the interview identifier, verbatim passage, and enough surrounding context for each result. Treat generated labels and summaries as provisional leads.
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Check each result against the transcript or recording. Confirm who said it, whether the quote is exact, whether relevant context was omitted, and whether the proposed label fits the research question. If the tool cannot provide a reliable path back to the source, do not use its summary as evidence for a need.
5. Refine codes and develop themes
Maintain a codebook so that labels remain consistent across interviews and over time. For each code, record its name, definition, inclusion and exclusion rules, and an example excerpt. The CDC notes that codebooks help prevent coding shift and drift (CDC, “Collecting and Analyzing Qualitative Data”).
AI can suggest labels or retrieve passages, but a researcher should check whether each code’s boundaries are clear and whether it fits the study. Group related codes into a theme only when the evidence forms a coherent pattern that answers the research question. The UK Government describes thematic analysis as identifying and developing common themes in collected data (UK Government, “Analyse your data: evaluating digital health products”).
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Do not equate frequency with importance or representativeness. A recurring issue may matter, but an infrequent, severe barrier can matter too. The study design and decision context determine how to interpret counts; there is no universal count that validates a customer need.
6. Compare participants and look for contrary evidence
Compare case summaries and supporting passages across respondents. Where the research question and sample justify it, compare meaningful groups as well. Ask who experienced the issue, in what circumstances, what differed, and whether anyone’s account contradicts the tentative theme. CDC’s cognitive-interviewing framework describes moving from interview-level summaries to comparisons across respondents and groups before drawing conclusions (CDC/NCHS, “Cognitive Interviewing”).
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Keep disconfirming cases visible rather than smoothing them into a single generated summary. A need may apply to a particular context or group rather than to every customer. State that scope when presenting the finding.
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7. Write customer needs as evidence-backed claims
A useful drafting structure is: “When [context], [type of customer] needs a way to [goal], because [observed barrier or motivation].” This is a writing aid, not a validated formula. Support the statement with representative quotations and enough context to show how they substantiate it.
- Separate what participants explicitly said from your interpretation of what they need.
- Identify the context or subgroup the finding applies to when it is not universal.
- Include contrary evidence or uncertainty when it changes how the claim should be understood.
- Retain links to the source interview and passage so another reviewer can inspect the reasoning.
8. Review and document AI’s contribution
Before sharing findings, have a human reviewer check source passages, speaker attribution, omissions, contradictory evidence, and whether AI-generated wording introduces assumptions. Keep a record of which analysis steps used AI, what data were provided, and what a person reviewed. The University of Göttingen’s AI Usage Cards, updated March 20, 2026, recommend documenting AI involvement in qualitative tasks such as coding, thematic analysis, and translation, and checking quotations against participant speech (University of Göttingen, “AI Disclosure for Qualitative Research”).
Choosing a tool or workflow
Dedicated research software and a general-purpose AI workflow can support different research needs. Evaluate the actual product, plan, and data terms rather than relying on broad feature claims.
| What to assess | Questions to ask |
|---|---|
| Evidence traceability | Can you follow a claim back to its transcript passage or recording segment, with context? |
| Data handling | What access controls, redaction options, retention rules, data residency, and contractual terms apply to this plan and dataset? |
| Workflow fit | Do you need per-interview transcription and tagging, cross-project synthesis, or a broader customer-feedback repository? |
| Human review and auditability | Can researchers correct AI output, inspect source material, retain codebooks, and record analytic decisions? |
| Cost and scale | What current usage limits, plan terms, and procurement requirements apply to your expected volume? |
Product descriptions are not independent evaluations. Dovetail describes a customer-intelligence workflow and source-linked analysis (Dovetail, “Product Research”); Condens describes a research repository and analysis platform with evidence-linked AI analysis (Condens); and Looppanel markets interview-focused qualitative analysis (Looppanel, “Qualitative Analysis”). Check whether the claimed features and controls are available in the specific plan and workflow you would use. AWS Marketplace describes Dovetail enterprise pricing as contract-based, but that listing does not provide a comparable price survey across these options (AWS Marketplace, Dovetail listing).
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