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
| Responsible | Rehaf Foula |
|---|---|
| Last Update | 10/18/2026 |
| Members | 1 |
IT & related technologies
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1. AI Leadership Context1Lessons ·
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1.1 WelcomeNew
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2. Opportunity Discovery5Lessons ·
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2.1 AI Opportunity SignalsNew
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2.2 Workflow Friction MappingNew
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2.3 Value Pool PrioritizationNew
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2.5 Strategic Use Case PatternsNew
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2.6 Executive Problem BriefsNew
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3. Solution Architecture7Lessons ·
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3.1 AI Solution Building BlocksNew
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3.2 Model Capability MappingNew
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3.3 Data And Context DesignNew
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3.4 Tool And Agent BoundariesNew
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3.5 Human Control PointsNew
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3.6 Architecture Tradeoff ReviewNew
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3.7 Design A Solution BlueprintNew
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4. Models And Tools9Lessons ·
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4.1 Foundation Model LandscapeNew
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4.2 Model Selection CriteriaNew
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4.3 Prompt System DesignNew
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4.4 Structured Output ControlsNew
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4.5 Tool Calling PatternsNew
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4.7 Agent Framework ChoicesNew
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4.8 Automation Platform OptionsNew
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4.9 Cost Latency DecisionsNew
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4.10 Select A Production StackNew
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5. Data And Knowledge8Lessons ·
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5.1 Enterprise Data ReadinessNew
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5.2 Knowledge Source InventoryNew
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5.3 Data Quality DiagnosticsNew
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5.4 Document Processing FlowsNew
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5.5 Retrieval Augmentation DesignNew
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5.6 Embedding And Index ChoicesNew
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5.7 Knowledge Access PermissionsNew
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5.8 Data Lineage For AINew
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6. Prototype To Product10Lessons ·
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6.1 Prototype Success CriteriaNew
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6.2 Rapid Experiment DesignNew
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6.3 Prompt And Workflow TestsNew
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6.4 Evaluation Dataset ConstructionNew
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6.5 Quality Metric SelectionNew
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6.6 Human Review ProtocolsNew
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6.8 Prototype Demonstration DesignNew
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6.9 Pilot Backlog PlanningNew
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6.10 Product Requirements For AINew
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6.11 Build A Validated PrototypeNew
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7. AI Governance8Lessons ·
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7.1 Governance Operating PrinciplesNew
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7.2 Risk Classification MethodsNew
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7.3 Privacy And Security ControlsNew
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7.4 Responsible Use StandardsNew
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7.5 Model And Vendor Due DiligenceNew
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7.6 Approval Workflow DesignNew
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7.7 Incident Response For AINew
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7.8 Create A Governance PlaybookNew
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8. Production Operations9Lessons ·
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8.1 Production Readiness GatesNew
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8.2 Deployment Architecture ChoicesNew
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8.3 Observability For AI SystemsNew
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8.4 Monitoring Quality DriftNew
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8.5 Cost And Capacity ManagementNew
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8.7 Release And Rollback ControlsNew
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8.8 Model Lifecycle ManagementNew
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8.9 Service Ownership ModelsNew
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8.10 Production Launch ReviewNew
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9. AI Risk Decisions6Lessons ·
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9.1 Bias And Fairness SignalsNew
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9.2 Reliability And HallucinationNew
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9.3 Explainability RequirementsNew
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9.4 Adversarial Threat ScenariosNew
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9.5 Risk Acceptance DecisionsNew
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9.6 Escalation And RemediationNew
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10. Adoption And Change5Lessons ·
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10.1 AI Adoption ReadinessNew
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10.2 Stakeholder Impact MappingNew
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10.3 Role Redesign ConversationsNew
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10.5 Enablement And Training PlansNew
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10.6 Trust And Communication MetricsNew
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11. Portfolio And Scale10Lessons ·
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11.1 AI Portfolio StrategyNew
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11.2 Investment Sequencing ModelsNew
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11.3 Platform Versus ProductNew
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11.4 Operating Model OptionsNew
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11.5 Capability And Talent PlanningNew
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11.6 Partner Ecosystem DesignNew
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11.7 Benefits Realization SystemsNew
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11.8 Scaling Pattern SelectionNew
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11.9 Executive Decision ForumsNew
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11.10 Build A Scale RoadmapNew
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12. Executive Capstone1Lessons ·
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12.1 Key TakeawaysNew
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