Big Data & Artificial Intelligence 101

Get ready to dive into the cool world of Microsoft Copilot and see how it can totally change your workflow!
Explore ways to enhance productivity with Microsoft Copilot. This hands-on course introduces how AI features in Word, Excel, Outlook, Teams, and PowerPoint can assist with automating tasks, generating content, and analyzing data within Microsoft 365.

Learning Objectives:
Participants will have the opportunity to:
- Understand the capabilities and limitations of Microsoft Copilot.
- Utilize Copilot in Microsoft Word to generate, summarize, and refine content.
- Leverage Copilot in Excel for data analysis, formula generation, and insights.
- Enhance email and scheduling efficiency with Copilot in Outlook.
- Improve collaboration and meeting productivity using Copilot in Teams.
- Use Copilot in PowerPoint to create AI-generated presentations.
- Implement best practices for AI-assisted work automation.

Topic Outline:
Introduction to Microsoft Copilot
- Overview of Microsoft Copilot and its AI capabilities
- Understanding Copilot¿s integration across Microsoft 365 apps
- Ethical and security considerations when using AI-powered tools
Copilot in Microsoft Word: AI-Powered Writing and Editing
- Content generation and refinement
- Summarizing and reformatting documents
- Hands-on: Creating and enhancing reports with Copilot
Copilot in Microsoft Excel: AI for Data Analysis
- Automated data insights and visualization
- Generating and explaining complex formulas
- Hands-on: Analyzing datasets and creating reports with Copilot
Copilot in Microsoft Outlook: Smarter Emails and Scheduling
- AI-assisted email drafting and summarization
- Scheduling and managing meetings efficiently
- Hands-on: Automating email and calendar tasks with Copilot
Copilot in Microsoft Teams: Enhancing Collaboration
- AI-powered meeting notes and action items
- Summarizing chat discussions and key takeaways
- Hands-on: Using Copilot to improve team collaboration
Copilot in Microsoft PowerPoint: AI-Generated Presentations
- Creating presentations with AI-generated content and design
- Automating slide formatting and improvements
- Hands-on: Building a professional presentation with Copilot
Best Practices for Using Copilot in Workflows
- Optimizing Copilot for everyday tasks
- Understanding AI limitations and when to verify outputs
- Hands-on: Applying Microsoft Copilot in a real-world work scenario

Training material provided: Yes (Digital format)

Get the lowdown on using AI safely with practical tips to keep your projects responsible and on point.
This half-day course introduces the practical foundations of Safe AI: using, developing, deploying, and overseeing AI systems in ways that reduce avoidable harm while preserving their usefulness. Participants will examine common AI risks, including inaccurate outputs, inappropriate disclosure of sensitive information, bias, security threats, unsafe automation, excessive reliance on AI, and poorly controlled agentic behavior.

Learning Objectives:
After completing this course, participants will be able to:

• Explain what “Safe AI” means in an organizational context.
• Recognize that AI risk depends on the task, information, capabilities, users, and consequences, not simply on which model is being used.
• Identify common risks associated with generative and agentic AI.
• Recognize hallucinations, misleading outputs, and inappropriate levels of confidence.
• Apply practical techniques for verifying AI-generated information.
• Protect confidential, personal, proprietary, and otherwise sensitive information when using AI.
• Recognize common security concerns involving AI systems and AI-enabled workflows.
• Understand bias, fairness, and other risks associated with using AI-supported recommendations or decisions.
• Distinguish between low-risk AI assistance and higher-risk AI actions or decisions.
• Apply appropriate human oversight to AI-assisted work.
• Understand the additional risks introduced when AI systems can access tools, files, applications, and external systems.
• Use a simple risk-based framework to determine appropriate safeguards before using or deploying AI.

Curriculum:

1. What Does “Safe AI” Mean?

• What Safe AI means in practical terms
• AI safety versus AI security versus responsible AI
• Why AI safety concerns have increased
• Generative AI, predictive AI, and agentic AI
• The difference between AI producing information and AI taking actions
• Safety as a shared responsibility
• Different responsibilities for:
• Everyday AI users
• Managers and process owners
• Developers and technical teams
• Security, privacy, legal, compliance, and governance functions

2. Understanding AI Risk
Rather than treating all AI use as equally risky, participants learn to evaluate the context.

1. What is the AI being asked to do?
2. What information does it have access to?
3. What can it do?
4. Who will rely on the result?
5. What happens if it is wrong?
6. Hallucinations, Accuracy, and Reliability

• What an AI hallucination is
• Why fluent responses are not necessarily correct
• Fabricated facts, citations, sources, or calculations
• Missing context and incorrect assumptions
• Out-of-date information
• Confidence versus correctness
• Why AI can produce different answers to similar questions
• Risks of summarizing incomplete source material
• Risks of relying on AI-generated analysis without verification
• 3. Data Privacy and Confidential Information
• Public versus internal versus confidential information
• Personal and sensitive personal information
• Customer and employee information
• Intellectual property and trade secrets
• Credentials, passwords, and security information
• Contracts, legal material, and regulated information
• Uploaded files and attachments
• Connected applications and repositories
• Organizational policies governing approved AI systems
• 4. AI Security and Adversarial Risks
• AI systems as a new organizational attack surface
• Malicious or manipulated inputs
• Prompt injection
• Indirect prompt injection from documents, websites, and other sources
• Malicious instructions embedded in content
• Social engineering using AI
• AI-generated phishing and impersonation
• Unsafe code or commands generated by AI
• Excessive permissions
• Connecting AI systems to organizational tools
• 5. Bias, Fairness, and AI-Supported Decisions
• Where bias can enter AI systems
• Bias in data
• Bias in prompts and task definitions
• Bias in model outputs
• Automation bias in human reviewers
• Why AI recommendations may appear objective even when they are not
• Representativeness and missing populations
• High-impact decisions
• Appropriate human review
• Documenting decision criteria
• Testing AI systems across relevant scenarios
• 6. Safe Use of AI Agents and Automation

The risks change when an AI system can act, not merely answer.
Agentic risk factors

• Access to files
• Access to email and messaging
• Database access
• Ability to modify records
• Ability to execute code
• Ability to make purchases
• External communications
• Repeated or automated execution
• 7. Human Oversight and Avoiding Over-Reliance
• AI as an assistant rather than an unquestioned authority
• Confirmation bias
• Loss of human expertise through excessive delegation
• When human judgment matters most
• Reviewing outputs versus merely approving them
• Appropriate escalation
• When subject-matter expertise is required
• Maintaining accountability

Training material provided: Yes (Digital format)

Date:
19 November 2026 10am- 1pm
20 November 2026 10am- 1pm

Come hang out and learn how to make ChatGPT your new go-to helper for everyday stuff!
ChatGPT Work can help users gather and synthesize information, work with files and available tools, create documents, analyze data, and complete multi-step workplace tasks.
Users define the desired outcome, provide relevant context and source materials, set boundaries, and review and refine the resulting work.This three-hour workshop introduces practical foundations for using ChatGPT Work in organizational settings. Participants will learn how to identify suitable tasks, provide effective instructions and context, use files and approved connected tools, monitor progress, and verify results through an iterative process guided by human judgment.
The session combines demonstrations, guided practice, and realistic workplace scenarios. Participants will complete a task from initial assignment to a reviewed deliverable, with particular attention to defining task boundaries, validating outputs, protecting sensitive information, and establishing approval checkpoints before consequential actions.
Activities use the files and tools available in the course environment. Access to connected applications and organizational resources depends on account permissions and workspace settings.

Learning Objectives:
Participants will have the opportunity to:

1. Distinguish between asking AI for an answer and delegating a task that requires multiple steps, sources, or tools.
2. Identify tasks suited to ChatGPT Work and recognize when Chat or Codex may be more appropriate.
3. Write clear instructions that define the goal, context, constraints, source materials, success criteria, and expected output.
4. Provide files and use available, approved tools to support a workplace assignment.
5. Guide multi-step tasks using checkpoints, clarification, and targeted follow-up instructions.
6. Review and refine generated deliverables, independently verifying important claims and calculations.
7. Apply organizational policies for sensitive information, tool access, and approval of consequential actions.
8. Complete a practical end-to-end workflow for an everyday workplace task.

Topic Outline:
1. Introduction to ChatGPT Work- Choosing between Chat, ChatGPT Work, and Codex- Moving from asking a question to assigning a task- Examples of suitable workplace assignments- Capabilities, limitations, and available tools
2. Giving ChatGPT Work a Good Assignment- Defining the goal, deliverable, and success criteria- Providing background, source materials, audience, and constraints- Establishing scope, clarification needs, and approval checkpoints- Guided practice: preparing a clear task assignment
3. Working with Files, Information, and Tools- Providing documents, spreadsheets, presentations, and other materials- Synthesizing information across sources- Using approved connected tools within access boundaries- Requesting evidence and traceability while protecting sensitive information
4. Guiding Multi-Step Tasks- Reviewing the proposed approach and setting useful checkpoints- Handling ambiguity, missing information, and incorrect assumptions- Monitoring progress and redirecting the task- Refining results through targeted follow-up instructions
5. Reviewing Results and Using Them Responsibly- Checking accuracy, completeness, relevance, and source support- Verifying calculations, dates, names, and other critical details- Using AI-assisted critique alongside independent verification- Following organizational policies and approving consequential actions
6. Guided End-to-End Practice- Assigning a realistic workplace task using provided materials- Monitoring progress and addressing clarification needs- Reviewing and revising the resulting deliverable- Identifying opportunities to apply the workflow in participants’ roles