Stakeholder Translator

Stakeholder Translator

Transform a single product update into five audience-tailored communications — each with the right tone, detail level, technical depth, and framing. Includes sensitivity classification so you know what's shareable.

Output

Save to outputs/stakeholder-[topic]-[YYYY-MM-DD].md

When to Use

• Writing a product update that needs to reach multiple audiences
• Preparing exec summaries alongside engineering updates
• Creating board narratives from sprint outcomes
• Generating customer changelogs without leaking internal details
• Arming sales with talk tracks and objection handling

What You'll Get

Five distinct communications from one input:

Plus:

• Sensitivity Classification for each section (✅ Safe / ⚠️ Caution / 🔒 Internal Only)
• Key Message Consistency Check — ensures the core narrative is coherent across all five versions

Process

Step 1: Collect the Update

I'll ask for:

"What's the product update? Paste your internal notes, sprint summary, or a rough description of what happened. Include any context about timeline, metrics impact, or decisions made."

Step 2: Classify Sensitivity

Before generating anything, I'll classify each piece of information:

• ✅ Safe — Can be shared with any audience including customers
• ⚠️ Caution — OK for internal audiences, needs careful framing for external
• 🔒 Internal Only — Engineering and leadership only, never external

Step 3: Generate All Five Outputs

Each output follows audience-specific rules:

• Engineering: Include technical specifics, architecture decisions, known debt
• Executive: Lead with business impact, include metrics, end with decisions needed
• Board: Strategic framing, market context, forward-looking narrative with speaker notes
• Customer: Benefits only, no internals, action items for users
• Sales: Competitive angles, objection handling scripts, customer-facing talk tracks

Step 4: Consistency Check

I'll verify the core message is consistent across all five versions — same facts, different framing.

Demo Scenario: AI Feature Launch

Input:

"We shipped the AI-powered search feature. It uses embeddings + hybrid search. Took 3 sprints instead of 2 because of infra issues. Early metrics show 40% improvement in search relevance. We cut the advanced filtering feature to hit the deadline. Competitor X launched something similar last week."

Sample Engineering Output:

## Engineering Update: AI Search v1 Shipped 🚀

### What Shipped
- Hybrid search (BM25 + vector embeddings) using our new embedding pipeline
- New search index architecture — migrated from Elasticsearch to custom solution
- Search relevance improved 40% (measured by nDCG@10 on test queries)

### What We Cut
- Advanced filtering (date ranges, custom facets) — descoped to hit Q1 deadline
- Will pick up in next sprint as standalone work item

### Tech Debt Created
- Embedding pipeline runs synchronously — needs async job queue before scale
- No A/B testing framework for search yet — metrics are pre/post comparison only

### Timeline
- Originally scoped for 2 sprints, took 3 due to infra migration issues
- Root cause: staging environment parity — documented in retro

Sample Customer Changelog:

## 🔍 Smarter Search is Here

Finding what you need just got a lot easier. Our new AI-powered search understands
what you mean, not just what you type.

### What's New
- **Smarter results** — Search now understands context and intent
- **40% more relevant results** in our testing

### Coming Soon
- Advanced filters for date ranges and custom categories

Try it now → [Search anything]

Tips

1. More context = better output — Include metrics, decisions, trade-offs, and timeline
2. Flag sensitive items — If something is strictly confidential, tell me upfront
3. Specify urgency — Is this a routine update or a crisis communication?