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 |
|---|---|
| Última actualización | 18/10/2026 |
| Miembros | 1 |
IT & related technologies
-
1. AI Leadership Context1Lecciones ·
-
1.1 WelcomeNuevo
-
-
2. Opportunity Discovery5Lecciones ·
-
2.1 AI Opportunity SignalsNuevo
-
2.2 Workflow Friction MappingNuevo
-
2.3 Value Pool PrioritizationNuevo
-
2.5 Strategic Use Case PatternsNuevo
-
2.6 Executive Problem BriefsNuevo
-
-
3. Solution Architecture7Lecciones ·
-
3.1 AI Solution Building BlocksNuevo
-
3.2 Model Capability MappingNuevo
-
3.3 Data And Context DesignNuevo
-
3.4 Tool And Agent BoundariesNuevo
-
3.5 Human Control PointsNuevo
-
3.6 Architecture Tradeoff ReviewNuevo
-
3.7 Design A Solution BlueprintNuevo
-
-
4. Models And Tools9Lecciones ·
-
4.1 Foundation Model LandscapeNuevo
-
4.2 Model Selection CriteriaNuevo
-
4.3 Prompt System DesignNuevo
-
4.4 Structured Output ControlsNuevo
-
4.5 Tool Calling PatternsNuevo
-
4.7 Agent Framework ChoicesNuevo
-
4.8 Automation Platform OptionsNuevo
-
4.9 Cost Latency DecisionsNuevo
-
4.10 Select A Production StackNuevo
-
-
5. Data And Knowledge8Lecciones ·
-
5.1 Enterprise Data ReadinessNuevo
-
5.2 Knowledge Source InventoryNuevo
-
5.3 Data Quality DiagnosticsNuevo
-
5.4 Document Processing FlowsNuevo
-
5.5 Retrieval Augmentation DesignNuevo
-
5.6 Embedding And Index ChoicesNuevo
-
5.7 Knowledge Access PermissionsNuevo
-
5.8 Data Lineage For AINuevo
-
-
6. Prototype To Product10Lecciones ·
-
6.1 Prototype Success CriteriaNuevo
-
6.2 Rapid Experiment DesignNuevo
-
6.3 Prompt And Workflow TestsNuevo
-
6.4 Evaluation Dataset ConstructionNuevo
-
6.5 Quality Metric SelectionNuevo
-
6.6 Human Review ProtocolsNuevo
-
6.8 Prototype Demonstration DesignNuevo
-
6.9 Pilot Backlog PlanningNuevo
-
6.10 Product Requirements For AINuevo
-
6.11 Build A Validated PrototypeNuevo
-
-
7. AI Governance8Lecciones ·
-
7.1 Governance Operating PrinciplesNuevo
-
7.2 Risk Classification MethodsNuevo
-
7.3 Privacy And Security ControlsNuevo
-
7.4 Responsible Use StandardsNuevo
-
7.5 Model And Vendor Due DiligenceNuevo
-
7.6 Approval Workflow DesignNuevo
-
7.7 Incident Response For AINuevo
-
7.8 Create A Governance PlaybookNuevo
-
-
8. Production Operations9Lecciones ·
-
8.1 Production Readiness GatesNuevo
-
8.2 Deployment Architecture ChoicesNuevo
-
8.3 Observability For AI SystemsNuevo
-
8.4 Monitoring Quality DriftNuevo
-
8.5 Cost And Capacity ManagementNuevo
-
8.7 Release And Rollback ControlsNuevo
-
8.8 Model Lifecycle ManagementNuevo
-
8.9 Service Ownership ModelsNuevo
-
8.10 Production Launch ReviewNuevo
-
-
9. AI Risk Decisions6Lecciones ·
-
9.1 Bias And Fairness SignalsNuevo
-
9.2 Reliability And HallucinationNuevo
-
9.3 Explainability RequirementsNuevo
-
9.4 Adversarial Threat ScenariosNuevo
-
9.5 Risk Acceptance DecisionsNuevo
-
9.6 Escalation And RemediationNuevo
-
-
10. Adoption And Change5Lecciones ·
-
10.1 AI Adoption ReadinessNuevo
-
10.2 Stakeholder Impact MappingNuevo
-
10.3 Role Redesign ConversationsNuevo
-
10.5 Enablement And Training PlansNuevo
-
10.6 Trust And Communication MetricsNuevo
-
-
11. Portfolio And Scale10Lecciones ·
-
11.1 AI Portfolio StrategyNuevo
-
11.2 Investment Sequencing ModelsNuevo
-
11.3 Platform Versus ProductNuevo
-
11.4 Operating Model OptionsNuevo
-
11.5 Capability And Talent PlanningNuevo
-
11.6 Partner Ecosystem DesignNuevo
-
11.7 Benefits Realization SystemsNuevo
-
11.8 Scaling Pattern SelectionNuevo
-
11.9 Executive Decision ForumsNuevo
-
11.10 Build A Scale RoadmapNuevo
-
-
12. Executive Capstone1Lecciones ·
-
12.1 Key TakeawaysNuevo
-