IGBS-SBDG · Professional Course

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.

Enroll now — 5.000,00 LE View course

30.1 hours

Guided learning

12 modules

Structured curriculum

80 lessons

Online, self-paced

5.000,00 LE

Course fee

About this course

Course curriculum

Module 1

1. AI Leadership Context

1.1 Welcome

Module 2

2. Opportunity Discovery

2.1 AI Opportunity Signals
2.2 Workflow Friction Mapping
2.3 Value Pool Prioritization
2.4 Opportunity Framing Check
2.5 Strategic Use Case Patterns
2.6 Executive Problem Briefs

Module 3

3. Solution Architecture

3.1 AI Solution Building Blocks
3.2 Model Capability Mapping
3.3 Data And Context Design
3.4 Tool And Agent Boundaries
3.5 Human Control Points
3.6 Architecture Tradeoff Review
3.7 Design A Solution Blueprint
3.8 Architecture Readiness Check

Module 4

4. Models And Tools

4.1 Foundation Model Landscape
4.2 Model Selection Criteria
4.3 Prompt System Design
4.4 Structured Output Controls
4.5 Tool Calling Patterns
4.6 Retrieval Toolchains
4.7 Agent Framework Choices
4.8 Automation Platform Options
4.9 Cost Latency Decisions
4.10 Select A Production Stack

Module 5

5. Data And Knowledge

5.1 Enterprise Data Readiness
5.2 Knowledge Source Inventory
5.3 Data Quality Diagnostics
5.4 Document Processing Flows
5.5 Retrieval Augmentation Design
5.6 Embedding And Index Choices
5.7 Knowledge Access Permissions
5.8 Data Lineage For AI
5.9 Knowledge Pipeline Review

Module 6

6. Prototype To Product

6.1 Prototype Success Criteria
6.2 Rapid Experiment Design
6.3 Prompt And Workflow Tests
6.4 Evaluation Dataset Construction
6.5 Quality Metric Selection
6.6 Human Review Protocols
6.7 Failure Mode Discovery
6.8 Prototype Demonstration Design
6.9 Pilot Backlog Planning
6.10 Product Requirements For AI
6.11 Build A Validated Prototype

Module 7

7. AI Governance

7.1 Governance Operating Principles
7.2 Risk Classification Methods
7.3 Privacy And Security Controls
7.4 Responsible Use Standards
7.5 Model And Vendor Due Diligence
7.6 Approval Workflow Design
7.7 Incident Response For AI
7.8 Create A Governance Playbook
7.9 Governance Effectiveness Check

Module 8

8. Production Operations

8.1 Production Readiness Gates
8.2 Deployment Architecture Choices
8.3 Observability For AI Systems
8.4 Monitoring Quality Drift
8.5 Cost And Capacity Management
8.6 Security Testing Operations
8.7 Release And Rollback Controls
8.8 Model Lifecycle Management
8.9 Service Ownership Models
8.10 Production Launch Review

Module 9

9. AI Risk Decisions

9.1 Bias And Fairness Signals
9.2 Reliability And Hallucination
9.3 Explainability Requirements
9.4 Adversarial Threat Scenarios
9.5 Risk Acceptance Decisions
9.6 Escalation And Remediation
9.7 Risk Decision Check

Module 10

10. Adoption And Change

10.1 AI Adoption Readiness
10.2 Stakeholder Impact Mapping
10.3 Role Redesign Conversations
10.4 Adoption Strategy Check
10.5 Enablement And Training Plans
10.6 Trust And Communication Metrics

Module 11

11. Portfolio And Scale

11.1 AI Portfolio Strategy
11.2 Investment Sequencing Models
11.3 Platform Versus Product
11.4 Operating Model Options
11.5 Capability And Talent Planning
11.6 Partner Ecosystem Design
11.7 Benefits Realization Systems
11.8 Scaling Pattern Selection
11.9 Executive Decision Forums
11.10 Build A Scale Roadmap
11.11 Portfolio Leadership Check

Module 12

12. Executive Capstone

12.1 Key Takeaways

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