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
| Responsable | Rehaf Foula |
|---|---|
| Dernière mise à jour | 18/10/2026 |
| Membres | 1 |
IT & related technologies
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1. AI Leadership Context1Leçons ·
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1.1 WelcomeNouveau
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2. Opportunity Discovery5Leçons ·
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2.1 AI Opportunity SignalsNouveau
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2.2 Workflow Friction MappingNouveau
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2.3 Value Pool PrioritizationNouveau
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2.5 Strategic Use Case PatternsNouveau
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2.6 Executive Problem BriefsNouveau
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3. Solution Architecture7Leçons ·
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3.1 AI Solution Building BlocksNouveau
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3.2 Model Capability MappingNouveau
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3.3 Data And Context DesignNouveau
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3.4 Tool And Agent BoundariesNouveau
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3.5 Human Control PointsNouveau
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3.6 Architecture Tradeoff ReviewNouveau
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3.7 Design A Solution BlueprintNouveau
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4. Models And Tools9Leçons ·
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4.1 Foundation Model LandscapeNouveau
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4.2 Model Selection CriteriaNouveau
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4.3 Prompt System DesignNouveau
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4.4 Structured Output ControlsNouveau
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4.5 Tool Calling PatternsNouveau
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4.7 Agent Framework ChoicesNouveau
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4.8 Automation Platform OptionsNouveau
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4.9 Cost Latency DecisionsNouveau
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4.10 Select A Production StackNouveau
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5. Data And Knowledge8Leçons ·
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5.1 Enterprise Data ReadinessNouveau
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5.2 Knowledge Source InventoryNouveau
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5.3 Data Quality DiagnosticsNouveau
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5.4 Document Processing FlowsNouveau
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5.5 Retrieval Augmentation DesignNouveau
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5.6 Embedding And Index ChoicesNouveau
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5.7 Knowledge Access PermissionsNouveau
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5.8 Data Lineage For AINouveau
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6. Prototype To Product10Leçons ·
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6.1 Prototype Success CriteriaNouveau
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6.2 Rapid Experiment DesignNouveau
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6.3 Prompt And Workflow TestsNouveau
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6.4 Evaluation Dataset ConstructionNouveau
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6.5 Quality Metric SelectionNouveau
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6.6 Human Review ProtocolsNouveau
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6.8 Prototype Demonstration DesignNouveau
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6.9 Pilot Backlog PlanningNouveau
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6.10 Product Requirements For AINouveau
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6.11 Build A Validated PrototypeNouveau
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7. AI Governance8Leçons ·
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7.1 Governance Operating PrinciplesNouveau
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7.2 Risk Classification MethodsNouveau
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7.3 Privacy And Security ControlsNouveau
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7.4 Responsible Use StandardsNouveau
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7.5 Model And Vendor Due DiligenceNouveau
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7.6 Approval Workflow DesignNouveau
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7.7 Incident Response For AINouveau
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7.8 Create A Governance PlaybookNouveau
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8. Production Operations9Leçons ·
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8.1 Production Readiness GatesNouveau
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8.2 Deployment Architecture ChoicesNouveau
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8.3 Observability For AI SystemsNouveau
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8.4 Monitoring Quality DriftNouveau
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8.5 Cost And Capacity ManagementNouveau
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8.7 Release And Rollback ControlsNouveau
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8.8 Model Lifecycle ManagementNouveau
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8.9 Service Ownership ModelsNouveau
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8.10 Production Launch ReviewNouveau
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9. AI Risk Decisions6Leçons ·
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9.1 Bias And Fairness SignalsNouveau
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9.2 Reliability And HallucinationNouveau
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9.3 Explainability RequirementsNouveau
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9.4 Adversarial Threat ScenariosNouveau
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9.5 Risk Acceptance DecisionsNouveau
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9.6 Escalation And RemediationNouveau
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10. Adoption And Change5Leçons ·
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10.1 AI Adoption ReadinessNouveau
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10.2 Stakeholder Impact MappingNouveau
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10.3 Role Redesign ConversationsNouveau
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10.5 Enablement And Training PlansNouveau
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10.6 Trust And Communication MetricsNouveau
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11. Portfolio And Scale10Leçons ·
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11.1 AI Portfolio StrategyNouveau
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11.2 Investment Sequencing ModelsNouveau
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11.3 Platform Versus ProductNouveau
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11.4 Operating Model OptionsNouveau
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11.5 Capability And Talent PlanningNouveau
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11.6 Partner Ecosystem DesignNouveau
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11.7 Benefits Realization SystemsNouveau
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11.8 Scaling Pattern SelectionNouveau
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11.9 Executive Decision ForumsNouveau
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11.10 Build A Scale RoadmapNouveau
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12. Executive Capstone1Leçons ·
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12.1 Key TakeawaysNouveau
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