Understanding Claude as a Work Platform

• The Evolution of AI in Knowledge Work

• What Makes Claude Different

• The Claude Work Stack

• AI Limitations and Responsible Use

• Lab: Same task using different Claude modes

Prompting Claude for Business Thinking

• Anatomy of an Effective Prompt

• Prompting Techniques

  • role prompting
  • structured prompting
  • prompt chaining
  • example-driven prompting
  • constraint-based prompting

• Improving Output Quality

• Create reusable prompts for:
  • executive summaries
  • PRD drafts
  • sales emails
  • SOP creation

From Conversations to Execution

• The modern workflow: Meeting → Decision → Action → Follow-through
• Turning unstructured discussions into clear outcomes and ownership
• Maintaining decision continuity across multiple meetings and stakeholders
• Structuring prompts for consistent, repeatable outputs

Hands-on Lab: Convert a raw meeting transcript into:

• Key decisions taken
• Action items with owners and timelines
• Open questions and dependencies

Multi-Source Synthesis: Making Sense of Fragmented Information

• Working with multiple inputs:
  • meeting notes
  • reports
  • emails
  • research documents
• Identifying:
  • overlaps
  • contradictions
  • missing information
• Building a single source of truth from scattered inputs

Hands-on Lab: Combine multiple documents to create:

• A unified summary
• Key insights and patterns
• Areas requiring clarification or escalation

Creating Business-Critical Artifacts

Moving beyond generic outputs to real enterprise deliverables:

• Decision Logs
  • What was decided, why, and alternatives considered
• Action Trackers
  • Ownership, deadlines, dependencies
• Risk Registers (RAID Framework)
  • Risks, assumptions, issues, dependencies
  • Severity, likelihood, mitigation plans
• Weekly Status Reports
  • Progress, blockers, next steps

Hands-on Lab: From a project discussion, generate:

• A structured action tracker
• A risk register with mitigation strategies

Executive Communication and Decision Support

• Writing for different stakeholders:
  • CXOs
  • business leaders
  • delivery teams
• Structuring executive summaries:
  • context
  • key insights
  • decisions required
  • recommended actions
• Framing trade-offs and priorities clearly

Hands-on Lab: Convert detailed notes into:

• A 1-page executive briefing
• A decision memo with clear recommendations

While Day 1 focused on individual productivity, Day 2 shifts to team-level execution—where most enterprise value is created or lost.

This day enables participants to use Claude not just as a tool, but as a shared intelligence layer across teams—supporting knowledge management, reusable assets, and delegated execution.

Claude Projects: Building a Shared Team Memory

• What Projects Enable

  • shared knowledge base
  • persistent context and instructions across:
    • meetings
    • documents
    • research
  • a single source of truth
  • Reduced duplication and misalignment

• Designing a Team Knowledge System
  Instead of dumping files, structure Projects into:

• Customer / Account Context

  • client background

  • past interactions

  • key priorities

• Product / Solution Knowledge

  • features

  • differentiators

  • use cases

• Internal Knowledge

  • playbooks

  • past proposals

  • lessons learned

• Organizing Knowledge

  • How to store:
    • research
    • meeting notes
    • documentation
    • strategy material

• Using Projects for Cross-Team Work

  • Example workflows:
    • product planning
    • account intelligence
    • operational documentation

• Best Practices

  • Define clear ownership for maintaining Projects

  • Separate reference knowledge vs working drafts

  • Regularly review and prune outdated content

  • Avoid overloading Projects with raw, unstructured data

• Hands-on Lab: Create a team project workspace containing:

  • customer insights
  • product documentation
  • internal playbooks

Claude Artifacts: From Knowledge to Assets

Building Reusable Business Artifacts – Standardize how teams produce outputs by creating repeatable, high-quality artifacts

• What Are Business Artifacts (Enterprise View)

  • Proposal frameworks
  • Product Requirement Documents (PRDs)
  • Strategy briefings
  • Decision memos & frameworks
  • Operational checklists & dashboards
  • Internal playbooks

• Designing High-Quality Artifacts
  Each artifact should define:

  • Purpose (why it exists)

  • Structure (sections and flow)

  • Inputs required

  • Expected outputs

• Turning AI Outputs into Tools

  • reusable templates
  • structured playbooks
  • lightweight decision tools

• Developing an Artifact Library (Team-Level System)

  • proposal builders and templates
  • competitor analysis templates
  • PRD templates
  • strategy briefing templates and frameworks
  • decision memo format
  • operational checklists
• Hands-on Lab: Create a reusable artifact:
  • Define structure
  • Generate a sample output using Claude
  • Refine for clarity and usability

Claude Cowork: Delegating Work to AI as an Assitant/Operator

• • What Cowork Enables

    • Delegating multi-step work
    • Faster turnaround on:
      • research
      • analysis
      • document creation
    • Consistent output quality across teams
  • Learning to assign tasks, instead of asking for answers

  • Tasks Suitable for Delegation to Cowork

    • market research
    • account intelligence reports
    • competitor analysis
    • meeting preparation and briefing decks/packs
    • document comparison and synthesis
    • strategy briefings/summaries
  • Operational Delegation Framework
    Moving beyond basic prompting:
    Task → Context → Output → Review → Refine → Finalize

• Task: What needs to be done

• Context: Relevant background and constraints

• Output: Expected format and structure

• Review: Human validation

• Refine: Iteration based on feedback

• Finalize: Ready for use or sharing
• Human-in-the-Loop Design
  • Review and validate assumptions and key insights
  • Check alignment with business context
  • Ensure clarity and correctness before sharing
• Hands-on Labs:
  • Create a market research briefing
  • Generate a sales account intelligence report
  • Create a leadership briefing pack

Day 3 focuses on moving beyond isolated use cases to organization-wide adoption of Claude. Participants will learn how to design scalable workflows, implement governance, and drive measurable business impact—while ensuring responsible and secure usage.

Responsible Claude Usage & Governance

• Enterprise AI Governance

  • Defining acceptable AI usage boundaries
  • Understanding data sensitivity levels:
    • public
    • internal
    • confidential
  • Establishing prompt hygiene practices:
    • Clarity
    • context completeness
  • avoiding sensitive exposure

• Risk Mitigation

  • hallucination detection and management
  • bias awareness, recognition, and mitigation
  • establishing human validation checkpoints

• Team AI Policies

  • AI usage guidelines for teams
  • prompt governance rules/standards
  • establishing review and approval procedures

Designing Claude-Powered Workflows

• Identifying High-Impact Use Cases

Focus on workflows that are:

• Repetitive and time-consuming
• Documentation-heavy
• Research-driven
• Dependent on synthesis across inputs

• Mapping:
  • Documentation workflows (reports, summaries, proposals)
  • Research workflows (market, competitor, internal analysis)
  • Operational workflows (tracking, reporting, coordination)

• Designing AI + Human Workflows

  • Define clear roles for Claude:
    • Assistant → drafting and summarization
    • Analyst → synthesis and insight generation
    • Researcher → information gathering and comparison
    • Execution Partner → creating structured outputs
  • Define human responsibilities:
    • validation
    • decision-making
    • stakeholder alignment

End-to-End Workflow Design: From Input to Execution

Core Workflow Model
Input → Processing → Output → Decision → Execution → Tracking

• Example: Enterprise Sales Workflow

• Input
  • Client data
  • past interactions
  • industry insights
• Processing (Claude)
  • account intelligence synthesis
  • opportunity analysis
• Output
  • proposal draft
  • executive briefing
  • strategy recommendations
• Decision
  • internal alignment
  • prioritization
• Execution
  • final proposal delivery
  • stakeholder engagement
• Tracking
  • action items
  • follow-ups
  • deal progression

• Design Principles

• Define clear entry and exit points for each stage
• Ensure structured outputs at every step
• Maintain traceability of decisions and actions
• Avoid one-off usage—build repeatable systems

• Hands-on Lab: Map a real business workflow:

• Identify inputs
• Define Claude’s role
• Design outputs
• Establish execution steps

Measuring Business Impact

• How to estimate key metrics:
  • time savings
  • productivity gains
  • workflow acceleration
  • reduction in manual effort

• Estimating Value

  • Compare before vs after workflows

  • Identify bottlenecks removed

  • Measure speed and quality improvements

• Communicating Impact

  • Translate improvements into business outcomes

  • Link AI usage to:

    • revenue impact

    • efficiency gains

    • decision speed

• Driving Organizational Adoption through:

  • AI literacy
    • building foundational understanding across teams
    • moving from awareness to confident usage
  • team enablement
    • role-based training
    • use-case-driven learning
    • hands-on application
  • change management
    • addressing resistance to AI adoption
    • encouraging experimentation with guardrails
    • creating internal champions