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
5000.0
EGP
5.000,00 LE
| Sorumlu | Rehaf Foula |
|---|---|
| Son Güncelleme | 18.10.2026 |
| Üyeler | 1 |
IT & related technologies
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1. AI Leadership Context1Dersler ·
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1.1 WelcomeYeni
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2. Opportunity Discovery5Dersler ·
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2.1 AI Opportunity SignalsYeni
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2.2 Workflow Friction MappingYeni
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2.3 Value Pool PrioritizationYeni
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2.5 Strategic Use Case PatternsYeni
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2.6 Executive Problem BriefsYeni
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3. Solution Architecture7Dersler ·
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3.1 AI Solution Building BlocksYeni
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3.2 Model Capability MappingYeni
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3.3 Data And Context DesignYeni
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3.4 Tool And Agent BoundariesYeni
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3.5 Human Control PointsYeni
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3.6 Architecture Tradeoff ReviewYeni
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3.7 Design A Solution BlueprintYeni
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4. Models And Tools9Dersler ·
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4.1 Foundation Model LandscapeYeni
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4.2 Model Selection CriteriaYeni
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4.3 Prompt System DesignYeni
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4.4 Structured Output ControlsYeni
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4.5 Tool Calling PatternsYeni
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4.7 Agent Framework ChoicesYeni
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4.8 Automation Platform OptionsYeni
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4.9 Cost Latency DecisionsYeni
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4.10 Select A Production StackYeni
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5. Data And Knowledge8Dersler ·
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5.1 Enterprise Data ReadinessYeni
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5.2 Knowledge Source InventoryYeni
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5.3 Data Quality DiagnosticsYeni
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5.4 Document Processing FlowsYeni
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5.5 Retrieval Augmentation DesignYeni
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5.6 Embedding And Index ChoicesYeni
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5.7 Knowledge Access PermissionsYeni
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5.8 Data Lineage For AIYeni
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6. Prototype To Product10Dersler ·
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6.1 Prototype Success CriteriaYeni
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6.2 Rapid Experiment DesignYeni
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6.3 Prompt And Workflow TestsYeni
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6.4 Evaluation Dataset ConstructionYeni
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6.5 Quality Metric SelectionYeni
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6.6 Human Review ProtocolsYeni
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6.8 Prototype Demonstration DesignYeni
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6.9 Pilot Backlog PlanningYeni
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6.10 Product Requirements For AIYeni
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6.11 Build A Validated PrototypeYeni
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7. AI Governance8Dersler ·
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7.1 Governance Operating PrinciplesYeni
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7.2 Risk Classification MethodsYeni
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7.3 Privacy And Security ControlsYeni
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7.4 Responsible Use StandardsYeni
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7.5 Model And Vendor Due DiligenceYeni
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7.6 Approval Workflow DesignYeni
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7.7 Incident Response For AIYeni
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7.8 Create A Governance PlaybookYeni
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8. Production Operations9Dersler ·
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8.1 Production Readiness GatesYeni
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8.2 Deployment Architecture ChoicesYeni
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8.3 Observability For AI SystemsYeni
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8.4 Monitoring Quality DriftYeni
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8.5 Cost And Capacity ManagementYeni
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8.7 Release And Rollback ControlsYeni
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8.8 Model Lifecycle ManagementYeni
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8.9 Service Ownership ModelsYeni
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8.10 Production Launch ReviewYeni
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9. AI Risk Decisions6Dersler ·
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9.1 Bias And Fairness SignalsYeni
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9.2 Reliability And HallucinationYeni
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9.3 Explainability RequirementsYeni
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9.4 Adversarial Threat ScenariosYeni
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9.5 Risk Acceptance DecisionsYeni
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9.6 Escalation And RemediationYeni
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10. Adoption And Change5Dersler ·
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10.1 AI Adoption ReadinessYeni
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10.2 Stakeholder Impact MappingYeni
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10.3 Role Redesign ConversationsYeni
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10.5 Enablement And Training PlansYeni
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10.6 Trust And Communication MetricsYeni
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11. Portfolio And Scale10Dersler ·
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11.1 AI Portfolio StrategyYeni
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11.2 Investment Sequencing ModelsYeni
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11.3 Platform Versus ProductYeni
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11.4 Operating Model OptionsYeni
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11.5 Capability And Talent PlanningYeni
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11.6 Partner Ecosystem DesignYeni
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11.7 Benefits Realization SystemsYeni
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11.8 Scaling Pattern SelectionYeni
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11.9 Executive Decision ForumsYeni
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11.10 Build A Scale RoadmapYeni
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12. Executive Capstone1Dersler ·
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12.1 Key TakeawaysYeni
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