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AI Solution Leadership Lab

AI Solution Leadership Lab

Designed for managers and leaders who need to turn business opportunities into responsible, scalable AI-powered solutions. By the end, participants will be able to select high-value use cases, design and build AI workflows and agents with modern tools, evaluate performance and risk, and lead successful deployment and adoption.

5000,00 LE
5000.0 EGP 5000,00 LE
5000,00 LE
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Responsável Rehaf Foula
Última Atualização 18/10/2026
Membros 1
IT & related technologies
  • 1. AI Leadership Context
    1Aulas ·
    • 1.1 Welcome
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  • 2. Opportunity Discovery
    5Aulas ·
    • 2.1 AI Opportunity Signals
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    • 2.2 Workflow Friction Mapping
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    • 2.3 Value Pool Prioritization
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    • 2.5 Strategic Use Case Patterns
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    • 2.6 Executive Problem Briefs
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  • 3. Solution Architecture
    7Aulas ·
    • 3.1 AI Solution Building Blocks
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    • 3.2 Model Capability Mapping
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    • 3.3 Data And Context Design
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    • 3.4 Tool And Agent Boundaries
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    • 3.5 Human Control Points
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    • 3.6 Architecture Tradeoff Review
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    • 3.7 Design A Solution Blueprint
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  • 4. Models And Tools
    9Aulas ·
    • 4.1 Foundation Model Landscape
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    • 4.2 Model Selection Criteria
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    • 4.3 Prompt System Design
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    • 4.4 Structured Output Controls
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    • 4.5 Tool Calling Patterns
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    • 4.7 Agent Framework Choices
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    • 4.8 Automation Platform Options
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    • 4.9 Cost Latency Decisions
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    • 4.10 Select A Production Stack
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  • 5. Data And Knowledge
    8Aulas ·
    • 5.1 Enterprise Data Readiness
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    • 5.2 Knowledge Source Inventory
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    • 5.3 Data Quality Diagnostics
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    • 5.4 Document Processing Flows
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    • 5.5 Retrieval Augmentation Design
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    • 5.6 Embedding And Index Choices
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    • 5.7 Knowledge Access Permissions
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    • 5.8 Data Lineage For AI
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  • 6. Prototype To Product
    10Aulas ·
    • 6.1 Prototype Success Criteria
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    • 6.2 Rapid Experiment Design
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    • 6.3 Prompt And Workflow Tests
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    • 6.4 Evaluation Dataset Construction
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    • 6.5 Quality Metric Selection
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    • 6.6 Human Review Protocols
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    • 6.8 Prototype Demonstration Design
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    • 6.9 Pilot Backlog Planning
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    • 6.10 Product Requirements For AI
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    • 6.11 Build A Validated Prototype
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  • 7. AI Governance
    8Aulas ·
    • 7.1 Governance Operating Principles
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    • 7.2 Risk Classification Methods
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    • 7.3 Privacy And Security Controls
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    • 7.4 Responsible Use Standards
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    • 7.5 Model And Vendor Due Diligence
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    • 7.6 Approval Workflow Design
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    • 7.7 Incident Response For AI
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    • 7.8 Create A Governance Playbook
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  • 8. Production Operations
    9Aulas ·
    • 8.1 Production Readiness Gates
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    • 8.2 Deployment Architecture Choices
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    • 8.3 Observability For AI Systems
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    • 8.4 Monitoring Quality Drift
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    • 8.5 Cost And Capacity Management
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    • 8.7 Release And Rollback Controls
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    • 8.8 Model Lifecycle Management
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    • 8.9 Service Ownership Models
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    • 8.10 Production Launch Review
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  • 9. AI Risk Decisions
    6Aulas ·
    • 9.1 Bias And Fairness Signals
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    • 9.2 Reliability And Hallucination
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    • 9.3 Explainability Requirements
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    • 9.4 Adversarial Threat Scenarios
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    • 9.5 Risk Acceptance Decisions
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    • 9.6 Escalation And Remediation
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  • 10. Adoption And Change
    5Aulas ·
    • 10.1 AI Adoption Readiness
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    • 10.2 Stakeholder Impact Mapping
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    • 10.3 Role Redesign Conversations
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    • 10.5 Enablement And Training Plans
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    • 10.6 Trust And Communication Metrics
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  • 11. Portfolio And Scale
    10Aulas ·
    • 11.1 AI Portfolio Strategy
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    • 11.2 Investment Sequencing Models
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    • 11.3 Platform Versus Product
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    • 11.4 Operating Model Options
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    • 11.5 Capability And Talent Planning
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    • 11.6 Partner Ecosystem Design
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    • 11.7 Benefits Realization Systems
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    • 11.8 Scaling Pattern Selection
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    • 11.9 Executive Decision Forums
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    • 11.10 Build A Scale Roadmap
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  • 12. Executive Capstone
    1Aulas ·
    • 12.1 Key Takeaways
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