Course Taxonomy: Data & AI

Microsoft Power Apps Boot Camp

Posted on February 17, 2026 by -

1. Introduction to Power Apps

  • What is Power Apps?
  • Role in the Power Platform
  • Types of apps

2. Canvas Apps vs Model-Driven Apps

  • Key differences
  • When to use each type
  • Strengths and limitations

3. Licensing Requirements

  • Power Apps licenses overview
  • Dataverse usage scenarios
  • When Premium licensing is required

4. Introduction to Canvas Apps

  • Canvas app building blocks
  • Designing screens and layout

5. Working with Canvas App Controls, Power Fx & Data Connections

  • Controls and properties
  • Power Fx fundamentals
  • Connecting to data sources (SharePoint, Excel, Dataverse, etc.)
  • Building simple formulas and logic

6. Introduction to Dataverse & Model-Driven Apps

  • What is Dataverse?
  • Tables, columns, relationships
  • Overview of model-driven design principles

7. Creating Solutions and Tables in Dataverse

  • What are Solutions?
  • Creating custom tables and relationships
  • Option sets, lookups, and data types
  • Understanding primary keys and schema design

8. Creating Model-Driven Apps

  • App design and components
  • Navigation, sitemap, and app configuration
  • Adding forms, views, dashboards

9. Creating and Working with Forms & Views in Model-Driven Apps

  • Form types and customization
  • Business rules and visibility settings
  • View creation and filtering

10. Publishing & Sharing Model-Driven Apps

  • App validation and publishing
  • Sharing apps and managing permissions

Practical Microsoft Copilot for Real Work

Posted on February 17, 2026 by -

Module 1 — Copilot Basics

  • What is Microsoft Copilot?
  • Licensing and prerequisites
  • Prompting fundamentals

Module 2 — Using Copilot in Office Apps

  • Copilot in Word: drafting, summarizing, improving content
  • Copilot in Excel: insights, formulas, visualizations
  • Copilot in PowerPoint: slide generation and editing
  • Copilot in Outlook: email summaries and drafting

Module 3 — Copilot in SharePoint

  • Using Copilot with document libraries

Module 4 — Copilot Chat in Teams

  • Meeting recaps and action items
  • Summarizing chat history

Module 5 — Work Copilot vs Web Copilot

  • Differences between Work Copilot and Web Copilot
  • Generative AI Capabilities

Module 6 — Creating Copilot Agents in Teams

  • Connecting data sources
  • Publishing an Agent to Teams

AI Native Change Agent

Posted on October 29, 2025 by -

Lesson 1 – Change Agent: Guiding Your Organization to True AI Success

Focus: Orientation and foundational mindset

Key topics:

  • Course logistics, setup, and tools (Companion app, BYO-LLM)
  • Working agreements and safety: Be present, not perfect
  • Core skillset of an AI-Native Change Agent
    • AI Fluency
    • Value Maximization
    • AI-Powered Facilitation
    • Solution Lifecycle mastery
  • Course outcome: move projects beyond the “POC graveyard,” guide AI adoption, and amplify organizational impact

Lesson 2 – Advancing AI Fluency

Focus: Translate AI concepts into business language

You will learn to:

  • Translate technical AI trade-offs into business value
  • Differentiate core AI solution patterns:
    • Off-the-shelf vs. Custom build
    • Retrieval-Augmented Generation (RAG)
    • Fine-Tuning
    • AI Agents
  • Evaluate feasibility with filters and risk lenses
  • Identify technical and process red flags
  • Discussion: What gets lost in translation between business and technical teams?

Lesson 3 – Value Maximization

Focus: Unlock hidden value in existing AI assets

You will learn to:

  • Audit AI tools already in use
  • Activate underused capabilities by tracking AI evolution
  • Optimize to improve efficiency and ROI
  • Centralize best practices to scale success and prevent redundancy
  • Concepts introduced:
  • Audit → Activate → Optimize → Centralize framework
  • The 7 AI-Native Success Factors
  • When to build POCs vs. full solutions

Lesson 4 – AI-Powered Facilitation

Focus: Align people and resolve friction in AI initiatives

You will learn to:

  • Recognize stakeholder goals and fears
  • Use AI to generate powerful questions and manage conflict
  • Build psychological safety and constructive dialogue
  • Guide meetings with focus and clarity
  • WIIFM: Learn to keep initiatives moving forward when teams stall or disagree.

Lesson 5 – Sense & Discover

Focus: The first phase of the AI-Native Solution Lifecycle

You will learn to:

  • Identify and analyze key stakeholders
  • Uncover hidden risks and opportunities
  • Apply AI-assisted discovery to find the real business problem
  • Create a living AI-Native Solution Charter
  • Outcome: Get the right people focused on the right AI problem using AI as your discovery partner.

Lesson 6 – Design the Solution

Focus: Building the AI-Native Value Blueprint

Key components:

  • Value Proposal – The why: define success and outcomes
  • AI Solution – The what: approach and interaction design
  • Data Strategy – The fuel: sourcing, securing, managing data
  • Production Operations – The path to production: maintaining and scaling
  • Risk & Compliance – The guardrails: responsible AI and governance
  • Viability Assessment – The payoff: ROI and adoption readiness

Lesson 7 – Deliver the Solution

Focus: Execution and continuous learning

You will learn to:

  • Build resilient, adaptive AI roadmaps
  • Maintain momentum and remove blockers
  • Apply agile learning cycles for rapid iteration
  • Measure value and ensure readiness for deployment
  • Outcome: Confidently guide your team through AI implementation to successful launch.

Lesson 8 – Tell the Story

Focus: Amplifying success with AI-powered storytelling

You will learn to:

  • Identify impactful success stories within your organization
  • Use AI tools to adapt and scale those stories
  • Create narratives that inspire enterprise-wide adoption
  • Discussion: Why do successful AI pilots stay trapped in silos?
  • Outcome: Transform one AI win into a company-wide success story.

Lesson 9 – Expanding Your Impact

Focus: Career and organizational growth

You will learn to:

  • Create a personal roadmap as an AI-Native Change Agent
  • Align your learning and certification goals
  • Apply course tools in real projects
  • Facilitate AI-Native Value Process end-to-end
  • Phases of Impact:
  1. Foundation & Opportunity Identification – Audit readiness, find AI opportunities
  2. Propose & Prepare – Build business case and solution charter
  3. Deliver & Scale – Lead implementation and storytelling to amplify success

AI Agents with Google’s ADK Bootcamp

Posted on October 28, 2025 by -

Part1: Intro to Agents & ADK

  • What are AI agents and why now?
  • ADK core concepts (agent, tool, memory, workflow)
  • Demo: adk init structure walkthrough
  • Hands-on: Create your first agent
  • Discussion: Agent use cases
  • Q&A

Part 2: Models and Tools

  • Connecting to Gemini via Vertex AI
  • Using open models via LiteLLM
  • Defining Python tools and schemas
  • Demo: Agents in action with Gemini + tool use
  • Hands-on: Create a Python tool and link it to your agent
  • Q&A

Part 3: Memory and Multimodality

  • Conceptual flow and memory persistence
  • Types of memory: buffer, summary
  • Implementing memory in ADK
  • Hands-on: Add memory and interact using adk web
  • Discussion: What agents should remember
  • Q&A

Part 4: Agentic Design Patterns

  • RAG and beyond: why patterns matter
  • Demo: Search + LLM retrieval
  • Hands-on: Implement a design pattern of choice
  • Group share: favorite patterns and use cases
  • Q&A

Part 5: Building the Full Agent

  • Review core building blocks
  • Hands-on: Create a "Daily Briefing Agent"
  • Discussion: Day 1 highlights and blockers
  • Q&A

Part 6: MCP and Agent-to-Agent Protocols

  • MCP and A2A intro
  • Use cases for multi-agent systems
  • Demo: Make your agent A2A-compatible
  • Hands-on: Swap tools, connect to MCP server
  • Discussion: Multi-agent  architectures
  • Q&A

Part 7: Agent Evaluation and AgentOps

  • Evaluation strategies for agents
  • AgentOps overview: lifecycle, CI/CD, logging, testing
  • Demo: adk run -v, agent engine, deployment paths
  • Hands-on: Analyze logs, prep for deployment
  • Q&A

Part 8: Agent Security & Wrap-Up

  • Security risks: prompt injection, tool misuse, data leakage
  • ADK mitigation strategies
  • Project: Identify one or two risks that apply directly to the agent you’ve conceptualized or built; explore the different capabilities in ADK to secure your agent.
  • Hands-on: Identify and address risks in your agent
  • Final Q&A + Certification Badge Instructions

Agentic AI on Azure

Posted on October 28, 2025 by -

Part 1: What Is an AI Agent in Azure?

  • Understand the fundamentals of AI agents and Azure’s ecosystem
  • Overview of agent architecture and capabilities
  • Agentic vs traditional automation: key differences
  • Introduction to Azure OpenAI & model capabilities (GPT-4, vision, embeddings)
  • Demo: Agent behavior in the Azure Playground
  • Limits, quotas, and pricing considerations
  • Q&A + Discussion

Part 2: Agent Development with Azure AI Studio

  • Hands-on with Azure’s flagship tool for agent workflows
  • Introduction to Azure AI Studio (aka AI Foundry)
  • Building agents with planning, memory, and tools
  • Using prompts, APIs, functions, and data connectors
  • Demo: Creating a workflow-aware agent project
  • Q&A + Demo Extension

Part 3: Coding and Deploying AI Agents

  • From concept to cloud — deploy your agents
  • Code walkthroughs in C# and Python
  • Calling agents via REST and SDKs
  • Deploying and testing agents in the Azure cloud
  • Monitoring usage, performance, and safety
  • Break + Q&A

Part 4: Integrating Agents into Real Workflows

  • Bring agents to life in real-world use cases
  • Customer support and ticketing systems
  • Finance and risk agents
  • Healthcare process automation
  • Manufacturing workflow optimization
  • Demo: Connecting to enterprise APIs and tools
  • Wrap-up Q&A and feedback

Building Agentic Apps with the OpenAI SDK

Posted on October 28, 2025 by -

Part 1: What Are AI Agents

  • What is an AI agent?
  • How agents differ from chatbots and traditional automation (e.g., RPA)
  • Intro to agentic workflows and autonomy
  • Overview of different AI Agentic frameworks and tools. 
  • Group Discussion: “Where could agents make your work easier or more powerful?”
  • Live Q&A

Part 2 – Anatomy of an AI Agent

  • Core components: memory, planning, tool use, feedback
  • Best practices for Agent design
  • Multi-agent systems and Model Context Protocol
  • Hands-On Exercise: “Map an agent architecture to a sample workflow”(e.g., lead follow-up, report generation, task triage)
  • Live Q&A

Part 3 – Real-World Use Cases + Tools

  • Agents in ops, PM, research, sales, customer service
  • Tool demo: no-code agent builder
  • Tool stack examples using OpenAI and others
  • Group Exercise: “Given a use case, choose the best tool and justify your pick”
  • Discussion: “What blockers would you face deploying this in your org?”
  • Live Q&A

Part 4 – Strategy, Piloting & Ethics

  • Prototyping your first agent
  • Governance, trust, and AI feedback loops
  • Measuring ROI, value, and readiness
  • Hands-On Walkthrough: “Build and run a simple agent using a no code AI Agent tool”
  • Observe and analyze output
  • Live Q&A + Next Steps
  • Summary of takeaways

Intro to the Future of Work using AI Agents

Posted on October 28, 2025 by -

Part 1: What Are AI Agents 

  • What is an AI agent? 
  • How agents differ from chatbots and traditional automation (e.g., RPA) 
  • Intro to agentic workflows and autonomy 
  • Overview of different AI Agentic frameworks and tools.  
  • Group Discussion: “Where could agents make your work easier or more powerful?” 
  • Live Q&A 

Part 2 – Anatomy of an AI Agent 

  • Core components: memory, planning, tool use, feedback 
  • Best practices for Agent design 
  • Multi-agent systems and Model Context Protocol 
  • Hands-On Exercise: “Map an agent architecture to a sample workflow”(e.g., lead follow-up, report generation, task triage) 
  • Live Q&A 

 

Part 3 – Real-World Use Cases + Tools 

  • Agents in ops, PM, research, sales, customer service 
  • Tool demo: no-code agent builder 
  • Tool stack examples using OpenAI and others 
  • Group Exercise: “Given a use case, choose the best tool and justify your pick” 
  • Discussion: “What blockers would you face deploying this in your org?” 
  • Live Q&A 

 

Part 4 – Strategy, Piloting & Ethics 

  • Prototyping your first agent 
  • Governance, trust, and AI feedback loops 
  • Measuring ROI, value, and readiness 
  • Hands-On Walkthrough: “Build and run a simple agent using a no code AI Agent tool” 
  • Observe and analyze output 
  • Live Q&A + Next Steps 
  • Summary of takeaways 

AI Native Foundations

Posted on September 8, 2025 by -

Introduction to AI-Native Foundations

    • Overview of course objectives and outcomes
    • Introduction to the EDGE™ Imperative

Part 1: Grasp the EDGE™ Imperative

    • Explore Exponential, Disruptive, Generative, and Emergent forces transforming work
    • Discuss the impact of these forces on various industries

Part 2: Understanding AI and Related Technologies

    • Simplified explanations of AI, ML, GenAI, LLMs, RAG, and intelligent agents
    • Master safe and effective AI prompting with proven techniques
    • Real-world examples and applications

Part 3: Applying AI-Native Success Factors

    • Introduction to the 7 AI-Native Success Factors
    • Case studies and practical applications to drive value from day one

Part 4: Workflow Improvement and Transformational Thinking

    • Redesign one of your personal workflows using AI
    • Business Brief: Identifying high-impact opportunities
    • Strategies for transformational thinking in AI adoption

Part 5: Roadmap to AI-Native

    • Develop a strategic approach to becoming AI-Native
    • Tools and strategies for implementation

Part 6: The AI-Native Pitch

    • Design a personal 30-60-90 day AI plan
    • Pitch AI use cases with confidence
    • Workshop: Crafting and delivering effective AI pitches

Conclusion and Next Steps

    • Recap of key learnings
    • Strategies for continued AI fluency and confidence
    • Q&A and feedback session

SAFe® Achieving Responsible AI

Posted on March 18, 2025 by -

Self-paced, on-demand eLearning (1 hour)

The eLearning module comprehensively introduces Responsible AI, covering foundational concepts and their alignment with SAFe practices. The eLearning includes several pre-work assignments intended to fully prepare students for the facilitated session — including researching current RAI practices in your organization, reviewing example policies, and ensuring access to a generative AI tool for practical exercises.

Activity 1 – Identifying Stakeholders (~30 minutes)

As future change agents and advocates for RAI, one of the first and most important tasks is to think about the people in an organization who would need to be involved in implementing RAI, what their responsibilities would be, as well as their top concerns. Learners will leave this activity with an actionable stakeholder map for future initiatives.

Activity 2 – Evaluating RAI Policies (~25 minutes)

Get hands-on experience using GenAI applications such as SAFe CoPilot and ChatGPT to apply critical thinking to an example set of policy elements in an RAI plan, identifying improvements needed for organizational fit.

Activity 3 – Communicating the Need for RAI (~30 minutes)

Become empowered to effectively communicate the need for Responsible AI (RAI), the goals of an RAI initiative, and the alignment of an RAI initiative with organizational objectives through a concise “elevator pitch” activity.

Activity 4 – Writing an RAI Epic Hypothesis Statement (~35 minutes)

Practice identifying an actionable element of an RAI implementation that requires a significant effort by one or more Value Streams, and scoping that effort using a SAFe Epic hypothesis statement.

AI for Leaders & Managers

Posted on November 6, 2024 by -

Part 1: Introduction to Leadership Needs Emerging from AI

  • Overview of AI technologies relevant to management
  • Potential impacts on team structures and workflows

 

Part 2: Strategizing AI Integration

  • Identifying AI opportunities in team operations
  • Aligning AI projects with organizational objectives
  • Workshop Activity: Creating an AI integration roadmap for your team

 

Part 3: Leadership in the Age of AI

  • Techniques for leading through technological change
  • Building and leading cross-functional AI integration teams
  • Interactive Session: Role-playing AI-driven change scenarios

 

Part 4: Optimizing Team Performance with AI

  • Leveraging AI for decision support and performance enhancement
  • Case Studies: Successful team transformations through AI

 

Part 5: Ethical Leadership and AI Governance

  • Navigating ethical issues in AI application
  • Developing an AI governance framework for your team

 

Part 6: Workshop on AI and Team Dynamics

  • Managing changes in roles and skills due to AI adoption
  • Fostering a culture of innovation and continuous learning

 

Part 7: Metrics and Measurement

  • Defining and tracking success metrics for AI initiatives
  • Tools for monitoring AI impact on team effectiveness

 

Part 8: Q&A, Discussion, and Action Planning

  • Addressing specific challenges faced by participants
  • Developing personal action plans based on workshop insights
  • Wrap-up and feedback session