AB-731: AI Transformation Leader

AB-731: AI Transformation Leader

Offered by Linux Training

The AB-731: AI Transformation Leader course at Linux Training is designed for business leaders, managers, and professionals who want to drive AI adoption and lead digital transformation initiatives within organizations.

This course focuses on leveraging Artificial Intelligence to transform business operations, strategy, and customer experiences, enabling leaders to make data-driven decisions and implement AI at scale.

Course Overview

This program provides a strategic understanding of AI-led transformation, helping learners plan, execute, and manage AI initiatives that deliver measurable business value and competitive advantage.

What You Will Learn

  • Fundamentals of AI for Business Leaders
  • AI Strategy and Roadmap Development
  • Identifying AI Opportunities in Organizations
  • Managing AI Projects and Teams
  • Generative AI and Business Innovation
  • Change Management and Digital Transformation
  • Responsible AI, Governance, and Ethics

Why Choose This Course?

  • Leadership-focused AI program
  • High-demand digital transformation skills
  • No coding required
  • Focus on real-world business impact
  • Guidance from experienced trainers

Career Opportunities

After completing this course, you can explore roles such as:

  • AI Transformation Leader
  • Digital Transformation Manager
  • AI Strategy Consultant
  • Business Innovation Manager
  • Chief AI Officer (Future Role)

Who Can Join?

  • Business leaders and managers
  • Entrepreneurs and decision-makers
  • Consultants and strategists
  • Professionals leading digital initiatives

Lead the Future with AI Transformation

Join Linux Training and gain the strategic knowledge needed to lead AI-driven transformation and innovation in modern organizations.

AB-731: AI Transformation Leader

Modules

1. Identify the business value of generative AI solutions (35–40%)

Identify the foundational concepts of generative AI

  • Describe the differences between generative AI and other types of AI
  • Select a generative AI solution to meet a business need
  • Describe the differences between AI models, including fine-tuned and pretrained models
  • Explain the cost drivers in generative AI usage, including tokens and return-on-investment (ROI) considerations
  • Identify the challenges of using generative AI solutions, including fabrications, reliability, and bias
  • Identify when generative AI solutions can provide business value, including scalability and automation

    • Identify benefits and capabilities of generative AI solutions

  • Describe the impact of prompt engineering
  • Understand techniques of prompt engineering
  • Identify business requirements for grounding solutions
  • Understand how retrieval-augmented generation (RAG) is used for AI solutions
  • Understand the impact of data on AI solutions, including data type, data quality, and representative datasets
  • Describe the importance of secure AI
  • Identify scenarios when machine learning adds value
  • Describe the lifecycle of a machine learning solution
  • Identify security considerations for AI systems, including application security, data security, and authentication requirements

    • 2. Identify benefits, capabilities, and opportunities for Microsoft’s AI apps and services (35–40%)

    Identify benefits and capabilities of Microsoft 365 Copilot and Microsoft Copilot

  • Map business processes and use cases to Copilot
  • Understand differences in capabilities between versions of Copilot
  • Understand capabilities of Microsoft 365 Copilot Chat web and mobile experiences
  • Understand capabilities of the Copilot experience in various Microsoft 365 apps
  • Understand capabilities of Microsoft Copilot Studio
  • Understand capabilities of Microsoft Graph

    • Identify benefits and capabilities of an integrated Microsoft AI solution

  • Map business processes and use cases to Microsoft’s AI apps and services
  • Identify when to use Researcher or Analyst in Copilot
  • Identify when to build, buy, or extend, including the Microsoft 365 Copilot extensibility framework

    • Identify benefits and capabilities of Foundry Tools

  • Map business processes and use cases to Foundry Tools
  • Identify capabilities of Azure AI services, including Azure Vision in Foundry Tools, Azure AI Search, and Microsoft Foundry
  • Match an AI model to a business need
  • Identify the benefits of Microsoft Foundry and Foundry Tools, including scalability and security

    • 3. Identify an implementation and adoption strategy for Microsoft’s AI apps and services (20–25%)

    Align an AI strategy with Microsoft responsible AI policies

  • Explain the importance of responsible AI
  • Establish governance principles for AI use
  • Establish an AI council to guide strategy, oversight, and cross-functional alignment
  • Ensure that AI solutions meet responsible AI standards, including fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability

    • Plan for AI adoption across the organization

  • Establish an adoption team
  • Identify common barriers to adoption
  • Establish an AI champions program
  • Understand potential impacts to data, security, privacy, and cost
  • Understand Copilot license types, including pay-as-you go, monthly, and included with Microsoft 365 subscription
  • Understand Azure AI services subscription models, including pay-as-you-go and prepaid

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