Duration: 1 day (9:00 AM - 4:30 PM) Target: Professionals across all functions who want to use AI effectively in their daily work Style: Hands-on BYOD workshop with guided exercises Goal: Build practical AI literacy and hands-on skills for immediate workplace application. This is a skills-based workshop — not a strategic leadership or project management course.
This is Course 1 of a three-part executive education series:
| Practical AI for Professionals | AI-Driven Business Innovation | AI Leadership & PM | |
|---|---|---|---|
| Question | "How do I use AI effectively?" | "Which AI project should we fund?" | "How do I deliver this AI project?" |
| Audience | All professionals | Executives, strategists | Project managers, implementers |
| Focus | Personal AI skills & literacy | Strategic investment decisions | Project execution & leadership |
| Style | Hands-on BYOD workshop | Strategic framework simulation | Crisis simulation & role-play |
| Outcome | AI-literate professional | Strategic AI portfolio decisions | AI project leadership skills |
Recommended Progression:
- Practical AI for Professionals → Build foundational AI literacy and hands-on skills
- AI-Driven Business Innovation → Learn to evaluate, prioritise, and invest in AI initiatives
- AI Leadership & PM → Learn to lead, manage, and deliver AI projects
9:00 - 10:30 Session 1: AI Foundations & Orientation
10:30 - 11:00 Morning Tea
11:00 - 12:30 Session 2: Prompt & Context Engineering
12:30 - 1:15 Lunch & Networking
1:15 - 2:30 Session 3: AI-Powered Data Analysis
2:30 - 3:00 Afternoon Tea
3:00 - 4:00 Session 4: AI in Your Workflow
4:00 - 4:30 Personal Action Planning & Wrap-up
9:00-9:15 — Welcome & Introductions (15 min)
- Course overview and objectives
- Participant introductions: name, role, "one thing you've tried with AI"
- Set expectations: this is a workshop, not a lecture
9:15-9:50 — What AI Actually Is (35 min)
- Demystifying AI: what's happening "under the hood" (conceptual, not technical)
- Types of AI tools professionals encounter: chatbots, copilots, generators, analysers
- What AI is good at vs. what it's bad at
- Framework 1: AI Task Suitability Spectrum — a mental model for deciding when to use AI
9:50-10:30 — Exercise 1: AI Tool Test Drive (40 min)
- Same workplace task across 3 AI tools (ChatGPT, Claude, Gemini)
- Compare outputs: quality, style, accuracy, hallucinations
- Group discussion: what surprised you? What would you trust? What wouldn't you?
- Output: Personal notes on tool strengths and preferred tool for different tasks
11:00-11:30 — The Art of Effective Prompting (30 min)
- Why "garbage in, garbage out" applies to AI
- Framework 2: RTCF Prompt Framework
- Role — Who should the AI be? (e.g., "You are an experienced financial analyst")
- Task — What should it do? (e.g., "Summarise this quarterly report")
- Context — What background does it need? (e.g., "This is for a board presentation")
- Format — How should the output look? (e.g., "3 bullet points, executive language")
- Common prompt patterns: chain-of-thought, few-shot examples, iterative refinement
- Anti-patterns: vague instructions, missing context, not specifying constraints
- Context windows: what they are, why they matter, how to work within them
11:30-12:30 — Exercise 2: Prompt Engineering Workshop (60 min)
- Round 1 — Baseline (15 min): Attempt 3 workplace scenarios with naive prompts, note results
- Round 2 — Apply RTCF (20 min): Redo the same scenarios using the framework, compare improvement
- Round 3 — Advanced Techniques (15 min): Chain-of-thought reasoning, iterative refinement, multi-turn conversations
- Debrief (10 min): Share best prompts, discuss what worked and why
- Output: Personal prompt template library for common work tasks
Workplace Scenarios (participants choose 3):
- Draft a professional email responding to a difficult client
- Summarise a long policy document into key points
- Create a project status update from raw notes
- Generate interview questions for a specific role
- Analyse pros and cons of a business decision
- Write a process document from a verbal explanation
1:15-1:45 — Using AI for Data Work (30 min)
- What AI can do with your data: summarise, find patterns, visualise, explain
- Framework 3: AI-Assisted Analysis Workflow
- Prepare — Clean your question (what do you actually want to know?)
- Upload — Get data into the AI (copy/paste, file upload, describe it)
- Question — Ask progressively deeper questions
- Verify — Cross-check AI findings (does this match what you know?)
- Refine — Iterate on analysis based on results
- Present — Have AI help format findings for your audience
- When to trust AI analysis vs. when to verify manually
- Privacy and confidentiality: what data should never go into AI tools
1:45-2:30 — Exercise 3: Data Analysis Challenge (45 min)
- Provided dataset: RetailFlow customer service data (CSV)
- 6 months of ticket data: volume, response times, categories, satisfaction scores
- Task: Use AI to answer 3 business questions:
- What are the top 3 drivers of customer dissatisfaction?
- Are there patterns in when response times blow out?
- What would you recommend to the customer service manager?
- Work individually or in pairs
- Debrief (10 min): Compare findings across groups — did AI give everyone the same answers?
- Output: A one-page insight brief generated with AI assistance
3:00-3:20 — Low-Code AI Integration (20 min)
- Beyond chat: AI embedded in the tools you already use
- Microsoft 365 Copilot, Google Workspace AI, Notion AI
- Automation platforms: Zapier, Make, Power Automate
- Specialist tools: AI for writing, presentations, spreadsheets, meeting notes
- Framework 4: AI Opportunity Finder
- High-volume, repetitive — emails, scheduling, data entry → automate
- Information synthesis — research, summaries, meeting notes → AI-assist
- Content generation — drafts, reports, presentations → AI-draft, human-edit
- Analysis & patterns — data review, trend spotting, anomaly detection → AI-analyse
- Identifying the "low-hanging fruit" in your own role
- Responsible use: company policies, data governance, human oversight
3:20-4:00 — Exercise 4: Your AI-Enhanced Workflow (40 min)
- Part 1 — Map (10 min): Pick one recurring weekly task from your role. Map the current steps.
- Part 2 — Redesign (15 min): Redesign the workflow with AI assistance at each applicable step. Use the AI Opportunity Finder to identify where AI adds value.
- Part 3 — Test (10 min): If time permits, try one step live with an AI tool
- Part 4 — Share (5 min): 2-minute lightning presentations — "My task, my AI redesign, what I'd try first"
- Output: A before/after workflow diagram for one real task from your job
4:00-4:15 — Framework Synthesis (15 min)
- Recap the 4 frameworks:
- AI Task Suitability Spectrum — Know when to use AI
- RTCF Prompt Framework — Know how to talk to AI
- AI-Assisted Analysis Workflow — Know how to use AI with data
- AI Opportunity Finder — Know where AI fits in your work
- Key principles: human-in-the-loop, verify before you trust, start small, iterate
4:15-4:30 — Personal Action Planning (15 min)
- This week: What is the ONE AI tool or technique you'll try?
- This month: What workflow will you redesign with AI?
- This quarter: What AI skill will you develop further?
- Course feedback
- Pointer to next courses in the series: Business Innovation and Leadership & PM
What: A mental model for quickly assessing whether a task is suitable for AI assistance When taught: 9:15-9:50 AM (35 min) Practiced: Exercise 1 — AI Tool Test Drive (9:50-10:30 AM, 40 min)
Four zones:
- AI-Led — Routine, high-volume, pattern-based tasks (drafting, summarising, formatting)
- AI-Assisted — Tasks requiring judgment but benefiting from AI support (analysis, research, editing)
- Human-Led, AI-Supported — Complex tasks where AI handles sub-tasks (strategy, design, negotiation prep)
- Human-Only — Tasks requiring empathy, ethics, relationships, or novel judgment (performance reviews, crisis leadership, creative vision)
Key question: "Where does this task sit on the spectrum, and what does AI handle vs. what do I handle?"
What: A repeatable structure for writing effective prompts When taught: 11:00-11:30 AM (30 min) Practiced: Exercise 2 — Prompt Engineering Workshop (11:30-12:30 PM, 60 min)
Four components:
- Role — Set the AI's expertise and perspective
- Task — Define the specific action clearly
- Context — Provide relevant background and constraints
- Format — Specify the desired output structure
Key principle: "The quality of your output is directly proportional to the quality of your input."
What: A systematic 6-step process for using AI to analyse data When taught: 1:15-1:45 PM (30 min) Practiced: Exercise 3 — Data Analysis Challenge (1:45-2:30 PM, 45 min)
Six steps:
- Prepare — Define the question before touching the tool
- Upload — Get data into the AI appropriately
- Question — Ask progressively deeper questions
- Verify — Cross-check against what you know
- Refine — Iterate based on findings
- Present — Format for your audience
Key principle: "AI finds patterns fast, but humans decide what patterns mean."
What: A classification model for spotting AI opportunities in daily work When taught: 3:00-3:20 PM (20 min) Practiced: Exercise 4 — Your AI-Enhanced Workflow (3:20-4:00 PM, 40 min)
Four opportunity types:
- Automate — High-volume, repetitive tasks
- Assist — Information synthesis and research
- Generate — Content drafting and creation
- Analyse — Data review and pattern detection
Key question: "Which parts of my weekly work are high-volume, synthesis-heavy, content-heavy, or data-heavy?"
What: Compare AI tools by giving them identical workplace tasks Framework used: AI Task Suitability Spectrum Materials needed: BYOD with access to at least 2 AI tools (ChatGPT, Claude, Gemini) Output: Personal notes on tool strengths, weaknesses, and preferences
What: Progressive skill-building through 3 rounds of increasingly sophisticated prompting Framework used: RTCF Prompt Framework Materials needed: BYOD, prompt worksheet with 6 workplace scenarios Output: Personal prompt template library for common tasks
What: Analyse a real dataset using AI to answer business questions Framework used: AI-Assisted Analysis Workflow Materials needed: BYOD, RetailFlow customer service dataset (CSV), analysis worksheet Output: One-page insight brief generated with AI assistance
What: Map a real task from your role and redesign it with AI integration Framework used: AI Opportunity Finder Materials needed: BYOD, workflow mapping template Output: Before/after workflow diagram for one real workplace task
Before the course (48 hours prior):
- Ensure access to at least 2 AI tools (free tiers are fine):
- ChatGPT: chat.openai.com
- Claude: claude.ai
- Google Gemini: gemini.google.com
- Read the pre-reading: "AI for the Rest of Us" (approx. 20 minutes)
- Think about: one repetitive task in your role you'd like to improve
During the course (BYOD REQUIRED):
- Laptop (not tablet — you'll need multiple browser tabs)
- Active accounts on at least 2 AI platforms
- Note-taking capability
- A real task or dataset from your work (optional, for Exercise 4)
Take home:
- Frameworks reference sheet (PDF)
- Personal prompt template library (created during Exercise 2)
- Workflow redesign (created during Exercise 4)
- Personal action plan (completed during wrap-up)
- Links to further learning resources
- Send pre-reading to participants
- Send "tech check" email: ensure participants can access AI tools
- Prepare RetailFlow dataset (CSV) for Exercise 3
- Print frameworks reference sheet (1 per participant)
- Print workflow mapping templates (1 per participant)
- Test all AI tools — ensure they're accessible from the venue network
Technology (BYOD REQUIRED):
- Participants MUST bring laptops with AI tool access
- Venue wifi must allow access to AI platforms (check corporate firewalls)
- Projector and screen for facilitator
- No spreadsheet software required (AI tools handle analysis)
Room Setup:
- Tables for 4-6 people (collaborative work)
- Power outlets accessible to all participants
- Main presentation screen visible from all seats
Materials to Print:
- Frameworks reference sheet (1 per participant)
- Prompt Engineering worksheet with 6 scenarios (1 per participant)
- Data Analysis worksheet (1 per participant)
- Workflow mapping template (1 per participant)
- Energy is highest in the morning — that's where the conceptual foundation goes
- Post-lunch session uses data analysis (interactive, keeps energy up)
- Final session is personal and reflective — participants work on their own tasks
- Monitor pace: some participants will be AI-experienced, others brand new
- Have "stretch challenges" ready for advanced participants in each exercise
Participants take home:
- Frameworks reference sheet (PDF)
- Prompt template library (personal, created during course)
- Workflow redesign diagram (personal, created during course)
- Action plan (personal, completed during course)
- Links to next courses in the series
Different from the other two courses:
| AI for Professionals | Business Innovation | Leadership & PM | |
|---|---|---|---|
| RetailFlow use | Dataset for analysis practice | Strategic investment decisions | Operational project management |
| What participants see | Customer service CSV data | Company financials & AI initiatives | Live pilot with crises |
| Depth | Surface (data exercise) | Strategic (portfolio allocation) | Deep (full case simulation) |
RetailFlow in Exercise 3 (Data Analysis):
- Participants receive a CSV of 6 months of customer service data
- They use AI tools to find patterns, generate insights, and make recommendations
- This is the same company they'll encounter in the other two courses
- Builds familiarity for participants who continue the series
This course is deliberately personal and practical where the other two are strategic and organisational:
- No investment decisions (that's Business Innovation)
- No project management (that's Leadership & PM)
- No frameworks for leading teams or managing stakeholders
- Pure focus: "How do I, personally, use AI better in my job starting Monday?"
The gap this fills: participants in the Leadership and Innovation courses consistently requested more hands-on, "how-to" AI skills training. This course delivers exactly that, and allows the other courses to stay focused on strategy and leadership.
Part of the Executive AI Education series developed by Dr. Michael Borck, Curtin Business School.
Series:
- Course 1: Practical AI for Professionals (this course)
- Course 2: AI-Driven Business Innovation
- Course 3: AI Leadership & Project Management
This course equips professionals with practical AI skills they can apply immediately in their daily work.