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.

5,000.00 LE

负责人	Rehaf Foula
最后更新	2026/10/18
成员	1

课程
评论

IT & related technologies

1. AI Leadership Context

1课程。
- 1.1 Welcome
  
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2. Opportunity Discovery

5课程。
- 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

7课程。
- 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

9课程。
- 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

8课程。
- 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

10课程。
- 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

8课程。
- 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

9课程。
- 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

6课程。
- 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

5课程。
- 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

10课程。
- 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

1课程。
- 12.1 Key Takeaways
  
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