Target Audience
This course is designed for business leaders, business owners, directors, VPs, executives, and decision-makers across marketing, sales, operations, human resources, finance, strategy, and related business functions. It is also suitable for aspiring leaders who want to understand how to adopt and scale AI responsibly using Microsoft technologies. Participants do not need technical or coding expertise. The course is intended for professionals who need to evaluate AI opportunities, communicate the business value of generative AI, guide adoption planning, and make informed decisions about Microsoft 365 Copilot, Copilot Studio, Microsoft Foundry, Azure AI capabilities, and responsible AI practices.
Instructor-Led (Live Virtual/Classroom)
A live course led by a certified instructor, in virtual or classroom format — one full day of guided sessions with projects and case studies.
Course Overview
In AB-731T00-A: Drive AI transformation in your organization, learners explore how to lead AI transformation across teams and business functions. The course focuses on practical strategies for identifying high-impact AI opportunities, aligning AI initiatives with measurable business outcomes, and championing responsible AI practices. The course brings together Microsoft learning paths focused on the business value of generative AI, Microsoft Copilot solutions, Microsoft Foundry tools, responsible AI, and organizational scaling. Microsoft positions this course as a future-facing replacement aligned with current AI transformation strategy and recommends AB-731 as the updated path for business leaders preparing to drive AI adoption.
Last Updated: 2026-09-01
Course Exam Overview
- Program Name: None
- Included: Course, applicable labs, and virtual instructor-led facilitation included; certification exam attempt is managed separately when required.
- Duration: Microsoft states that candidates have 45 minutes to complete this assessment.
- Prerequisites: Candidates should understand how to recognize AI transformation opportunities, identify appropriate AI tools and resources, plan AI adoption, optimize business processes, and drive innovation using Microsoft 365 Copilot and Foundry Tools.
- Exam Format: Proctored Microsoft certification assessment; Microsoft notes that the exam may include interactive components.
- Delivery: Scheduled through Pearson VUE.
- Outcome: Successful candidates earn the AI Transformation Leader certification, validating their ability to guide AI transformation and innovation without requiring coding skills.
Skills You’ll Gain
Job Roles & Industry Outlook
AI Transformation Leader
Guides organizational AI strategy, identifies high-impact opportunities, aligns initiatives with business priorities, and champions responsible adoption.
Business Transformation Manager
Leads change initiatives, connects AI use cases with operational goals, and coordinates adoption across departments and stakeholders.
Business Transformation Manager
Defines technology-enabled business strategies, evaluates Microsoft AI capabilities, and supports executive decisions around AI investment and scale.
Course Includes
• One-day instructor-led training experience aligned to Microsoft Learn AB-731T00-A.• Strategic guidance for business leaders on AI opportunity identification, adoption planning, and transformation leadership.
• Practical discussion of Microsoft 365 Copilot, Microsoft Copilot, Copilot Studio, Microsoft Foundry, Azure AI, and responsible AI practices.
• Business-focused activities to connect AI capabilities with organizational priorities, measurable value, and adoption readiness.
• Preparation alignment for the AI Transformation Leader certification assessment, with exam attempt managed separately when required.
What You'll Learn
Generative AI is changing how organizations innovate, operate, and compete. This module introduces the business foundations of generative AI and helps leaders understand how Microsoft AI capabilities can unlock new opportunities, improve decision-making, and support transformation initiatives.
Lessons:
• What generative AI is and how it differs from traditional AI
• Business value of generative AI solutions
• Generative AI models and solution patterns
• Microsoft Copilot, Azure AI, and intelligent agents
• Cost drivers and business considerations in generative AI
• Challenges, risks, and adoption opportunities
Key Topics:
• Generative AI fundamentals
• Business transformation with AI
• AI value creation
• Cost, risk, and adoption readiness
• Responsible AI adoption considerations
Labs / Practical Exercises (if applicable):
• Identify potential generative AI opportunities in a business function
• Discuss how AI can improve productivity, decision-making, or customer experience
This module focuses on how organizations can make generative AI useful, reliable, and secure. Learners explore the importance of prompts, grounding, trusted data, data quality, security, and the role of machine learning in creating effective AI solutions.
Lessons:
• Prompt engineering for business scenarios
• Grounding AI responses in trusted organizational data
• Data quality and security considerations
• Trustworthy AI solution design
• When to use machine learning as part of an AI solution
Key Topics:
• Prompt design
• Trusted data grounding
• Security and data governance
• Reliable AI outputs
• Business value of machine learning
Labs / Practical Exercises (if applicable):
• Evaluate a prompt for clarity, context, and business usefulness
• Map a business use case to trusted data sources and governance needs
This module introduces how Microsoft Copilot can transform the way people work across Microsoft 365 apps and business processes. Learners explore how Copilot supports routine task automation, insight generation, secure collaboration, and organizational productivity.
Lessons:
• Microsoft Copilot as a productivity and business transformation tool
• Copilot in Microsoft 365 business workflows
• Automating routine tasks and improving information access
• Supporting secure and compliant innovation
• Licensing and extensibility considerations
• Copilot Studio as an extension path for business scenarios
Key Topics:
• Microsoft 365 Copilot
• Microsoft Copilot
• Copilot Studio
• Licensing and extensibility
• Productivity and business impact
Labs / Practical Exercises (if applicable):
• Identify Copilot use cases for a department or role
• Prioritize Copilot scenarios based on value, feasibility, and adoption effort
This module explores how Microsoft Foundry Tools can accelerate innovation and efficiency by enabling secure, compliant AI solutions for real business challenges. Learners review when to use prebuilt tools, models, retrieval-augmented generation, and specialized AI capabilities.
Lessons:
• Microsoft Foundry as a platform for AI solution development
• Azure Vision, Azure Language, Azure Document Intelligence, and Azure AI Search
• Prebuilt tools versus custom or extended AI solutions
• Foundry Models and Foundry Agent Service
• Retrieval-augmented generation for business scenarios
• Selecting the right AI approach for measurable impact
Key Topics:
• Microsoft Foundry Tools
• Azure AI capabilities
• Document intelligence and search
• RAG solution patterns
• Secure and compliant AI solutions
Labs / Practical Exercises (if applicable):
• Match business challenges to appropriate Microsoft Foundry capabilities
• Compare prebuilt, extended, and custom AI solution approaches
This module helps leaders understand how Azure cloud and AI capabilities support business transformation. Learners explore how organizations can unify data, modernize infrastructure, build intelligent apps and agents, and secure AI initiatives through governance and compliance.
Lessons:
• Microsoft AI approach for business transformation
• Unifying the data estate for AI readiness
• Modernizing infrastructure for AI-enabled work
• Building intelligent applications and agents
• Securing AI with governance, compliance, and responsible practices
Key Topics:
• Azure cloud and AI
• Data estate readiness
• Intelligent apps and agents
• Governance and compliance
• Measurable business outcomes
Labs / Practical Exercises (if applicable):
• Assess organizational readiness for AI transformation
• Identify data, infrastructure, and governance dependencies for an AI initiative
This module focuses on converting AI experimentation into dependable business outcomes. Learners examine how to connect AI initiatives with measurable value, prioritize use cases, and define success criteria that support organizational goals.
Lessons:
• Moving from AI experimentation to business value
• Identifying high-impact AI opportunities
• Aligning AI initiatives with strategic goals
• Defining measurable outcomes and success metrics
• Building a business case for AI investment
Key Topics:
• AI value realization
• Business case development
• Process optimization
• Success metrics
• Investment alignment
Labs / Practical Exercises (if applicable):
• Draft a business value statement for an AI use case
• Define measurable outcomes for an AI transformation scenario
This module introduces responsible AI principles and governance practices that help organizations build trust while adopting AI at scale. Learners explore policies, processes, safeguards, and leadership responsibilities for ethical, secure, and scalable AI adoption.
Lessons:
• Responsible AI principles and business leadership responsibilities
• Trust, transparency, fairness, reliability, privacy, and security
• Governance systems for AI adoption
• Designing policies and safeguards
• Responsible AI examples and organizational practices
Key Topics:
• Responsible AI
• AI governance
• Ethical and secure adoption
• Organizational safeguards
• Trustworthy innovation
Labs / Practical Exercises (if applicable):
• Review an AI use case against responsible AI principles
• Identify governance controls needed before scaling an AI solution
This module focuses on full AI adoption across an organization. Learners explore how to define AI strategy, assign responsibilities, empower business users and subject matter experts, and scale AI adoption beyond isolated experiments.
Lessons:
• Planning for organization-wide AI adoption
• Defining AI strategy and leadership responsibilities
• Assigning roles for business, technology, governance, and change management
• Empowering business users and subject matter experts
• Scaling AI from pilots to repeatable operating models
Key Topics:
• AI adoption strategy
• Transformation governance
• Change management
• Business user enablement
• Scaling AI responsibly
Labs / Practical Exercises (if applicable):
• Build a high-level AI adoption roadmap
• Define ownership, responsibilities, and next steps for scaling AI in the organization
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