AI and Team Productivity

ai_team_integration:
  individual_productivity:
    - Coding assistance
    - Writing and editing
    - Research and analysis
    - Task automation

  team_collaboration:
    - Meeting summarization
    - Documentation
    - Knowledge sharing
    - Communication assistance

  process_improvement:
    - Workflow automation
    - Quality checks
    - Status updates
    - Onboarding

team_ai_patterns:
  shared_tools:
    description: "Team uses same AI tools"
    examples:
      - Shared coding assistant
      - Team documentation bot
      - Common prompt library
    benefits:
      - Consistent approach
      - Shared learning
      - Reduced duplication

  ai_augmented_processes:
    description: "AI built into workflows"
    examples:
      - AI PR review comments
      - Automated meeting notes
      - AI-assisted onboarding
    benefits:
      - Reduced manual work
      - Consistent quality
      - Better documentation

  knowledge_amplification:
    description: "AI makes team knowledge accessible"
    examples:
      - Searchable team knowledge base
      - AI answers about codebase
      - Institutional memory preservation
    benefits:
      - Faster onboarding
      - Reduced repeat questions
      - Knowledge preservation

class TeamMeetingAI:
    """AI assistance for team meetings."""

    async def process_meeting(
        self,
        recording: MeetingRecording
    ) -> MeetingOutput:
        # Transcribe
        transcript = await self.transcribe(recording)

        # Generate outputs
        summary = await self._generate_summary(transcript)
        action_items = await self._extract_action_items(transcript)
        decisions = await self._extract_decisions(transcript)

        # Create searchable record
        await self._index_for_search(
            meeting_id=recording.id,
            transcript=transcript,
            summary=summary
        )

        return MeetingOutput(
            summary=summary,
            action_items=action_items,
            decisions=decisions,
            transcript=transcript
        )

    async def answer_meeting_question(
        self,
        question: str
    ) -> str:
        """Search across all meeting records."""
        relevant_meetings = await self.search.find_relevant(question)

        return await self.llm.generate(
            prompt=f"""Based on these meeting notes, answer: {question}

Meeting records:
{self._format_meetings(relevant_meetings)}

Answer:"""
        )

team_documentation:
  auto_documentation:
    - PR descriptions from diffs
    - Code documentation from changes
    - Architecture diagrams from code
    - Runbook generation

  documentation_qa:
    - Answer questions from docs
    - Find relevant documentation
    - Identify outdated content
    - Suggest improvements

  knowledge_capture:
    - Meeting decisions → docs
    - Slack threads → knowledge base
    - Incident retrospectives → runbooks

class OnboardingAssistant:
    """AI-powered onboarding for new team members."""

    async def answer_question(
        self,
        question: str,
        new_hire: User
    ) -> OnboardingAnswer:
        # Search team knowledge
        relevant_docs = await self.knowledge_base.search(question)

        # Check if common question
        similar_questions = await self.faq.find_similar(question)

        # Generate answer
        answer = await self.llm.generate(
            prompt=f"""Help a new team member understand: {question}

Relevant documentation:
{self._format_docs(relevant_docs)}

Similar past questions and answers:
{self._format_faq(similar_questions)}

Provide a helpful, welcoming answer:"""
        )

        # Track for FAQ improvement
        await self.track_question(question, new_hire.id)

        return OnboardingAnswer(
            answer=answer,
            sources=relevant_docs,
            related_topics=self._suggest_related(question)
        )

team_ai_metrics:
  productivity:
    - Time spent on routine tasks
    - Meeting follow-up time
    - Documentation currency
    - Onboarding time to productivity

  quality:
    - Documentation completeness
    - Knowledge accessibility
    - Consistency across team

  satisfaction:
    - Tool adoption rate
    - Team feedback
    - Reduction in friction points

team_ai_rollout:
  start_small:
    - Pilot with willing team
    - Single use case first
    - Gather feedback

  expand_gradually:
    - Add use cases based on success
    - Share learnings across teams
    - Build on what works

  create_champions:
    - Identify AI-savvy team members
    - Empower them to help others
    - Recognize contributions