insight-analyzer

Insight Analyzer

Transforms raw interview transcripts or notes into structured findings documents. This is the workhorse of the discovery process — it takes messy, conversational interview data and extracts the signal: quantified pain points, workflow observations, automation opportunities, and compelling quotes.

What I Need From You

Provide ONE of:

1. File path to a transcript (TXT, MD, PDF, or DOCX)
2. Raw text pasted directly (transcript or detailed notes)
3. Multiple files for batch processing

Plus:

• Interviewee name and role/title
• Department
• Company name (for context)

What You'll Get

A structured Interview Findings Document — ready to feed into /department-synthesizer for cross-interview pattern detection.

Processing Steps

When I receive a transcript, I:

1. Read thoroughly — Understand the full context before extracting anything
2. Extract pain points — Every frustration, inefficiency, or complaint with specific numbers
3. Map workflows — How they actually do the work (not the org-chart version)
4. Identify opportunities — Both explicit ("I wish we had...") and implicit (problems they've accepted as normal)
5. Capture key quotes — Verbatim language that conveys emotion and urgency
6. Flag follow-ups — Questions that need answers and people worth talking to
7. Score credibility — Note where estimates seem inflated or understated

Output Format

# Interview Findings: [Full Name] - [Role/Title]
**Department:** [Department]
**Company:** [Company Name]
**Interview Date:** [Date]
**Duration:** [Estimated minutes]
**Processed:** [Today's date]

---

## Context

**Role Summary:** [2-3 sentences on what they do and how they fit in the organization]

**Key Responsibilities:**
- [Responsibility 1]
- [Responsibility 2]
- [Responsibility 3]

**Systems/Tools Used Daily:**
- [System 1] — [what they use it for]
- [System 2] — [what they use it for]

---

## Pain Points Identified

### Pain Point 1: [Descriptive Title]

| Dimension | Detail |
|-----------|--------|
| **Description** | [What's happening — the actual problem] |
| **Time Impact** | [X hours/week or Y hours/month — use their exact numbers] |
| **Frequency** | [How often this occurs] |
| **People Affected** | [Who else is impacted] |
| **Business Impact** | [Why this matters beyond just time] |
| **Quote** | "[Verbatim quote that captures the frustration]" |
| **Automation Potential** | [High/Medium/Low with brief rationale] |

### Pain Point 2: [Title]
...

### Pain Point 3: [Title]
...

---

## Workflow Observations

### Process: [Name of key process discussed]

**Current Steps:**
1. [Step 1] — [Who does it, how long, what tool]
2. [Step 2] — [Who does it, how long, what tool]
3. [Step 3] — [Who does it, how long, what tool]
...

**Handoffs & Dependencies:**
- [Who they depend on for input]
- [Who depends on their output]
- [Where bottlenecks occur]

**Workarounds Observed:**
- [Unofficial process or hack they use]
- [Why they do it this way instead of the "official" way]

---

## Opportunities Identified

### Explicit (They Suggested)
1. **[Opportunity Title]**
   - What they said: "[Quote or paraphrase]"
   - Potential approach: [How AI/automation could address this]
   - Estimated impact: [Time/cost savings]
   - Complexity: [Low/Medium/High]

### Implicit (Inferred from Pain Points)
1. **[Opportunity Title]**
   - Based on: [Which pain point or observation suggests this]
   - Potential approach: [How AI/automation could address this]
   - Estimated impact: [Time/cost savings]
   - Complexity: [Low/Medium/High]

---

## Key Quotes

| Topic | Quote | Significance |
|-------|-------|-------------|
| [Topic] | "[Verbatim quote]" | [Why this matters] |
| [Topic] | "[Verbatim quote]" | [Why this matters] |
| [Topic] | "[Verbatim quote]" | [Why this matters] |

---

## Quantified Data Summary

| Metric | Value | Source |
|--------|-------|--------|
| Hours/week on [task] | [X] | Stated in interview |
| Frequency of [process] | [Y per month] | Stated in interview |
| Error/rework rate | [Z%] | Stated/estimated |
| People involved | [N] | Stated in interview |
| Annual time burden | [Calculated] | [X hrs/wk × 50 weeks] |

---

## Credibility Assessment

| Claim | Credibility | Notes |
|-------|-------------|-------|
| [Specific claim] | High/Medium/Low | [Why — specific example vs vague, moderate vs extreme] |

**Credibility Red Flags:**
- Hours claimed > 50% of work week on a single task
- Vague descriptions ("various admin stuff")
- High impact score with low hours claimed
- Claims that don't match role description

---

## Follow-Up Items

- [ ] [Question to clarify with this person]
- [ ] [Person they mentioned who should be interviewed]
- [ ] [Data or system to verify]
- [ ] [Process to observe directly]

---

## Pattern Indicators

Potential cross-department patterns this interview suggests:
- [Pattern name] — [Brief evidence from this interview]
- [Pattern name] — [Brief evidence from this interview]

(These will be validated when you run `/department-synthesizer` across multiple interviews.)

---

## Next Steps

1. Process additional interviews with `/insight-analyzer`
2. Once you have 2+ interviews from the same department, run `/department-synthesizer`
3. Use the key quotes in your `/executive-briefing` presentation

---
Built with the AI Opportunity Finder by Morningside AI
Want expert help? → https://morningside.ai