This course prepares learners for the AB-731: Microsoft AI Transformation Leader certification exam by exploring how organizations evaluate, implement, and scale generative AI solutions using Microsoft technologies. Learners will examine the business value of generative AI, including prompt engineering, grounding techniques such as Retrieval-Augmented Generation (RAG), and the impact of data quality on AI outcomes. The course also explores how Microsoft AI solutions, including Microsoft 365 Copilot, Copilot Studio, Azure AI services, and Microsoft Foundry, support productivity and innovation across business scenarios. In addition, learners will learn how to match AI solutions to business needs, apply Responsible AI principles, and design governance frameworks that ensure secure and compliant AI usage. Finally, the course covers AI adoption strategy and change management, including building adoption teams, champion programs, and scaling AI responsibly across the organization. By the end of this course, learners will be able to identify AI opportunities, evaluate Microsoft AI solutions, and guide responsible AI adoption within an enterprise environment.
Overview
Skills Learned
After completing this online training course, students will be able to:
Identify the business value of generative AI solutions
Explain the differences between generative AI and traditional AI approaches
Compare AI model types, including pretrained and fine-tuned models
Evaluate cost drivers and ROI considerations for AI solutions
Identify challenges and limitations of generative AI, including reliability and bias
Apply prompt engineering techniques to improve AI outputs
Explain grounding strategies such as Retrieval-Augmented Generation (RAG)
Evaluate the impact of data quality and representative datasets on AI performance
Identify security considerations for AI systems, including authentication and data protection
Identify capabilities and use cases for Microsoft 365 Copilot
Compare Copilot experiences across Microsoft 365 applications
Identify capabilities of Microsoft Copilot Studio and extensibility options
Identify when to build, extend, or use out-of-the-box AI solutions
Identify capabilities of Microsoft Foundry and Azure AI services
Match AI solutions to business and architectural requirements
Explain Responsible AI principles including fairness, reliability, privacy, inclusiveness, transparency, and accountability
Identify governance frameworks and compliance considerations for AI systems
Plan AI adoption strategies including adoption teams and champion programs
Identify licensing and cost considerations for Copilot and Azure AI services
- Business Leaders
- Sales
- HR
- Finance
- Operation
None
01. Generative AI & Business Value Fundamentals
- Generative vs Traditional AI (Use Case Fit)
- AI Model Types – Pretrained vs Fine-Tuned
- Cost Drivers & ROI Considerations
- Opportunities & Challenges
02. Generative AI Techniques & Best Practices
- Effective Prompt Engineering
- Grounding & Retrieval-Augmented Generation (RAG)
- Data Quality & AI Performance
- Secure AI Practices
- Traditional ML Integration
- Technical Foundations for AI Leaders
03. Microsoft 365 Copilot Capabilities & Use Cases
- Mapping Processes to Copilot
- Copilot Versions & Experiences
- Copilot in Microsoft 365 Apps
- Microsoft Copilot Studio & Extensibility
- Integrated Microsoft AI Architecture
- Researcher & Analyst Tools
04. Foundry Tools & Azure AI Services
- Foundry Tools Overview
- Azure AI Services Integration
- Matching AI Solutions to Business Needs
- Security & Scalability Benefits
05. Responsible AI & Governance
- Responsible AI Principles
- AI Governance & Council
- Ensuring Accountability & Compliance
- Operationalizing Responsible AI Governance
06. AI Adoption Strategy & Change Management
- AI Adoption Teams
- AI Champions Program
- Overcoming Adoption Barriers
- Iterative Rollout & Feedback Loops
- Governance, Security & Cost Impacts
- Copilot & Azure AI Licensing
07. Recap and Final Tips
- Recap and Final Tips
SKILLS LEARNED
Skills Learned
After completing this online training course, students will be able to:
Identify the business value of generative AI solutions
Explain the differences between generative AI and traditional AI approaches
Compare AI model types, including pretrained and fine-tuned models
Evaluate cost drivers and ROI considerations for AI solutions
Identify challenges and limitations of generative AI, including reliability and bias
Apply prompt engineering techniques to improve AI outputs
Explain grounding strategies such as Retrieval-Augmented Generation (RAG)
Evaluate the impact of data quality and representative datasets on AI performance
Identify security considerations for AI systems, including authentication and data protection
Identify capabilities and use cases for Microsoft 365 Copilot
Compare Copilot experiences across Microsoft 365 applications
Identify capabilities of Microsoft Copilot Studio and extensibility options
Identify when to build, extend, or use out-of-the-box AI solutions
Identify capabilities of Microsoft Foundry and Azure AI services
Match AI solutions to business and architectural requirements
Explain Responsible AI principles including fairness, reliability, privacy, inclusiveness, transparency, and accountability
Identify governance frameworks and compliance considerations for AI systems
Plan AI adoption strategies including adoption teams and champion programs
Identify licensing and cost considerations for Copilot and Azure AI services
WHO SHOULD ATTEND
- Business Leaders
- Sales
- HR
- Finance
- Operation
PREREQUISITES
None
COURSE OUTLINE
01. Generative AI & Business Value Fundamentals
- Generative vs Traditional AI (Use Case Fit)
- AI Model Types – Pretrained vs Fine-Tuned
- Cost Drivers & ROI Considerations
- Opportunities & Challenges
02. Generative AI Techniques & Best Practices
- Effective Prompt Engineering
- Grounding & Retrieval-Augmented Generation (RAG)
- Data Quality & AI Performance
- Secure AI Practices
- Traditional ML Integration
- Technical Foundations for AI Leaders
03. Microsoft 365 Copilot Capabilities & Use Cases
- Mapping Processes to Copilot
- Copilot Versions & Experiences
- Copilot in Microsoft 365 Apps
- Microsoft Copilot Studio & Extensibility
- Integrated Microsoft AI Architecture
- Researcher & Analyst Tools
04. Foundry Tools & Azure AI Services
- Foundry Tools Overview
- Azure AI Services Integration
- Matching AI Solutions to Business Needs
- Security & Scalability Benefits
05. Responsible AI & Governance
- Responsible AI Principles
- AI Governance & Council
- Ensuring Accountability & Compliance
- Operationalizing Responsible AI Governance
06. AI Adoption Strategy & Change Management
- AI Adoption Teams
- AI Champions Program
- Overcoming Adoption Barriers
- Iterative Rollout & Feedback Loops
- Governance, Security & Cost Impacts
- Copilot & Azure AI Licensing
07. Recap and Final Tips
- Recap and Final Tips
