AI Productivity Initiative

The Future of Knowledge Work

An internal skills ecosystem for AI-augmented productivity at Booking.
Not another tool. Infrastructure that makes us uniquely capable.

See the Vision ↓
The Problem

Knowledge work is broken

And it's not a tools problem — it's a structure problem

8-12 Tools used daily by every PM
28% Work time lost to context management
0 Tools that represent the whole context
∞ Context reconstruction cycles

Context Fragmentation

Knowledge scattered across Slack, Gmail, Docs, Jira, Confluence, Zoom, Glean. Each tool captures a slice. None connects them.

Reconstruction Overhead

Every decision, every meeting, every handoff requires reassembling context from scratch. Hours lost every week.

AI That Doesn't Persist

ChatGPT answers questions. It doesn't do work that lasts. Conversations vanish. Context is lost.

Siloed Craft Knowledge

Each PM reinvents workflows. Best practices don't compound. New PMs start from zero.

The Vision

An Internal Skills Ecosystem

Building infrastructure that makes Booking uniquely capable

🏗️

PM Spine

Common structure for meetings, docs, tasks, analysis. Every PM starts with the same foundation.

⚡

Skills on GitLab

Curated AI capabilities hosted internally. Install what you need. Governed and secure.

🔌

Enterprise Skills

Ask Research, Ask CS, Ask BDX. Booking context that generic AI can never provide.

📊

Layer Model

Individual → Team → Leaders. Start with personal productivity, scale to team intelligence.

Key insight: This is not about using external tools. It's about building internal infrastructure. Skills hosted on GitLab. Deeply integrated with Glean, Research, CS, BDX. Designed for Booking.
Foundation

The PM Spine Structure

A minimal, proven knowledge base structure optimized for AI retrieval

PM-KB/ ├── 1-Projects/ # One folder per project │ └── Project-Name/ # PRDs, briefs, context │ ├── Meetings/ # Chronological log │ └── YYYY-MM/ # NOT by project │ ├── People/ # Stakeholder files │ └── FirstName-LastName.md │ ├── 0-Inbox/ # Quick capture │ └── Claude-Questions.md │ ├── skills/ # Bundled skills ├── _Templates/ # PM artifacts ├── _My-Work-Board.md # Weekly Kanban └── CLAUDE.md # AI instructions

Why This Structure Works

  • Retrieval-optimized — AI can quickly find context without searching everything
  • Meetings are chronological — Not buried in project folders. Time-based retrieval.
  • People are central — Stakeholder context always one query away
  • Minimal but extensible — Start simple. Add folders/skills as needed.
  • Proven in production — Running daily for 6+ months
How It Works

Skills Installation Flow

Skills live on GitLab. Users copy what they need to their local setup.

GitLab Repository

skills/meeting-notes.yaml
skills/morning-brief.yaml
skills/brain-dump.yaml

→

User Copies

Clone repo
or
npx install-skill

→

User's Machine

.claude/skills/
meeting-notes.yaml
morning-brief.yaml

Personal KB grows over time. Start with base skills. Add enterprise skills. Install extension packs. Each PM's setup evolves with their needs.
See It In Action

Before & After

Concrete examples from the working prototype

/process-meeting Process any meeting transcript

Drop a Zoom transcript, say the command, get structured notes with decisions and actions extracted.

Before (Manual)

  • Take notes during meeting
  • Type up summary afterward
  • Manually identify decisions
  • Create action items in Jira
  • Search Glean for context
  • Update project files
~30-45 minutes per meeting

After (With Skill)

  • Drop transcript, say "/process-meeting"
  • AI creates structured notes
  • Decisions extracted automatically
  • Action items with owners
  • Glean context auto-enriched
  • Work Board updated
~2-3 minutes per meeting
# 2026-02-28 BHFS Alignment Sync ## Attendees - [[Christina Müller]] - Engineering Lead - [[Sarah Chen]] - Compliance - [[Anshul Goyal]] - PM ## Decisions Made 1. **PE flow will be modular** - can complete across sessions 2. **Direct/Indirect gating question** confirmed for all partners ## Action Items - [ ] @Anshul: Draft PE matching logic by Friday - [ ] @Christina: Review API contract for PE creation ## Context (from Glean) - Related Jira: BHFS-1234, BHFS-1256 - Prior discussion: Email thread from Feb 25
/morning-summary Start your day with context

One command pulls calendar, tasks, recent context. You know exactly what matters today.

# Friday, February 28, 2026 ## Today's Focus 🔥 **BHFS 5.3 deadline in 2 weeks** - PE matching logic critical path ## Calendar (from Glean) - 10:00 - BHFS Standup - 14:00 - Stakeholder Review with [[Sarah Chen]] - 16:00 - 1:1 with [[Manager Name]] ## Pending from Yesterday - [ ] Review Christina's API proposal - [ ] Respond to compliance questions ## This Week's Progress ✅ PE flow design approved ✅ Presentation drafted ⏳ Matching logic (in progress)
People/ Stakeholder context always available

When you mention someone, AI knows their role, working style, recent interactions, and priorities.

# Christina Müller **Role:** Engineering Lead, Partner Payments **Relationship:** Close collaborator on BHFS ## Working Style - Prefers written proposals before meetings - Values technical precision - Decision-maker for API design ## Recent Interactions - Feb 27: Agreed on modular PE approach - Feb 25: Discussed bank matching logic ## Their Priorities (from Glean) - Q1: Payment platform stability - Concerned about: Migration timeline
Skills Library

Installable Capabilities

Curated skills hosted on internal GitLab

Skill Category What It Does
/morning-brief Base Pull calendar, tasks, recent context → daily priorities
/evening-review Base Summarize accomplishments, queue tomorrow
/process-meeting Base Transcript → notes + decisions + actions
/brain-dump Base Messy thoughts → structured doc (PRD, brief, email)
/ask-research Enterprise Query Booking Research findings across the company
/ask-cs Enterprise Understand customer service insights for your domain
/ask-bdx Enterprise Pull data insights from Snowflake/BDX layer
/draft-prd Craft Structured PRD generation with Booking templates
/stakeholder-update Craft Weekly update formatted for leadership
/dependency-check Craft Analyze brief for cross-team dependencies
Extension Packs (Future): Dynamics Pack (extraction), Goals Pack (OKRs), Visibility Pack (content pipeline), Research Pack (synthesis). Install what you need.
Scale Path

The Layer Model

Focus on Layer 0 now. Design for team and leader layers.

0

Individual PM

PM spine + base skills + enterprise skills. Personal productivity transformation. Context always current. AI contributions persist.

Focus Now
1

Team

Published team context (goals, roadmaps, services). PM writes brief → agent checks other teams. Cross-team dependency intelligence.

Vision
2+

Leaders

Project dashboards pulling from Jira, Docs, Gmail. Configurable tracking: progress, conflicts, risks. Teams push updates; leaders consume.

Vision
Patterns

What We're Building On

Not using these tools — building our own internal equivalent

Claude Code

AI working in structured environments with persistent context and reviewable actions. Proves AI can do durable work, not just chat.

→ We build structured environments for PMs

skills.sh Model

Open ecosystem of 1000+ installable skills for AI agents. Community-driven, composable, versioned. Proves skills ecosystems work.

→ We build our own on internal GitLab

Cursor

IDE-native AI that transformed developer productivity. Met devs where they work. Now the standard for AI-augmented coding.

→ What's the equivalent for PMs?
Our answer: An internal skills ecosystem, deeply integrated with Glean, Research, CS, and BDX. Booking context that generic AI can never provide.
Timeline

Phased Approach

Prove individual value first. Then scale.

Phase 1: Validate Layer 0

Q1-Q2 2026
  • Pilot with 5-10 PMs
  • Refine base skills from real usage
  • Document adoption path
  • Identify "magic moments"
  • Establish GitLab skill repo

Phase 2: Enterprise Skills

Q2-Q3 2026
  • Build Ask Research connector
  • Build Ask CS connector
  • Build Ask BDX connector
  • Partner with data teams
  • Expand skill library to 15+

Phase 3: Design Layer 1

Q3-Q4 2026
  • Define team context format
  • Prototype dependency checking
  • Engineering partnership
  • One team pilot
  • Iterate on team model
What We Need

The Ask

Help us make this real

1

Collaborators (2-3 people)

Help refine the vision. Challenge assumptions. Shape what this becomes. PMs, EMs, or anyone passionate about AI productivity.

2

Pilot Team (5-10 PMs)

Adopt Layer 0. Test the spine structure. Install skills. Give feedback. Help us find what works and what doesn't.

3

Enterprise Team Input

Research, CS, BDX teams: How can we integrate? What APIs exist? What's feasible for enterprise skill connectors?

4

Sponsorship

Support to pursue this as a formal AI productivity initiative. Time, resources, visibility to make it happen.