Training Your Team on AI: A Manager's Playbook

June 18, 2025•6 min read•Team 400

Your team needs to understand AI. Not because it's trendy, but because AI is becoming part of how work gets done. The question isn't whether to train your team on AI—it's how to do it effectively.

After running AI training programs for Australian businesses, here's what actually works and what doesn't.

The AI Training Trap

Most AI training fails for predictable reasons:

Too theoretical: Three hours on neural network architectures. Nobody remembers it. Nobody can apply it.

Too generic: "Here are 50 AI tools!" Without context on which matter for their work.

Too hype-driven: Focuses on what AI might do someday, not what it can do today for their specific job.

One-and-done: A single workshop, then nothing. Skills aren't built in a day.

Wrong audience: Training everyone the same way. The needs of a marketing manager differ from a software developer.

Effective AI training is role-specific, practical, and ongoing.

Different Roles, Different Training

Executive Leadership

What they need to know:

What AI can and can't do (calibrated expectations)
How to evaluate AI investments
Governance and risk considerations
Competitive implications

Format: Half-day workshop + quarterly updates Focus: Strategy, not technical details

Middle Managers

What they need to know:

Identifying AI opportunities in their domain
Managing AI-augmented teams
Setting realistic expectations
Measuring AI impact

Format: Full-day workshop + monthly working sessions Focus: Application and change management

Knowledge Workers

What they need to know:

Using AI assistants effectively (prompting, evaluation)
Which AI tools are approved and how to use them
When AI helps and when it doesn't
Quality control and verification

Format: 2-3 hour workshop + hands-on labs Focus: Daily productivity

Technical Teams

What they need to know:

AI development fundamentals
Integration patterns
Security and privacy considerations
Model evaluation and monitoring

Format: Multi-day training + ongoing mentorship Focus: Building and maintaining AI systems

The Training Framework

Phase 1: Demystification (Week 1)

Goal: Everyone understands what AI is and isn't.

Content:

What AI actually is (pattern recognition, not magic)
What LLMs can and can't do
Common misconceptions
Real examples from your industry

Outcome: People can have sensible conversations about AI without buzzword bingo.

Phase 2: Hands-On Basics (Weeks 2-4)

Goal: Everyone can use AI tools effectively.

Content:

Using ChatGPT/Claude/Copilot for their role
Effective prompting techniques
Evaluating AI outputs
Knowing when to trust and when to verify

Format:

Interactive workshops
Guided exercises with real work tasks
Peer learning sessions

Outcome: People are comfortable using AI assistants for appropriate tasks.

Phase 3: Role-Specific Application (Weeks 5-8)

Goal: Each team applies AI to their specific workflows.

Content:

Department-specific use cases
Workflow integration
Quality control processes
Approved tools and guidelines

Format:

Team-level workshops
Use case development
Pilot projects

Outcome: Teams have identified and are testing AI applications in their work.

Phase 4: Ongoing Development (Continuous)

Goal: Skills keep developing as AI evolves.

Content:

New tools and capabilities
Lessons learned from pilots
Advanced techniques
Cross-team knowledge sharing

Format:

Monthly lunch-and-learns
Communities of practice
Curated updates on relevant developments

Outcome: Organisation builds cumulative AI capability.

What to Actually Teach

Prompt Engineering (Everyone)

The single most valuable skill. Not just "how to write prompts" but:

Understanding what the model knows and doesn't know
Providing context effectively
Iterative refinement
Task decomposition
Output format specification

Exercise: Take a real work task. Write a prompt. Get output. Refine prompt. Compare results. Repeat.

Critical Evaluation (Everyone)

AI outputs need verification. Teach:

What kinds of errors LLMs make (hallucination, outdated info, logical errors)
How to fact-check AI claims
When to trust and when to verify
Domain-specific verification techniques

Exercise: Give them AI-generated content with deliberate errors. Can they find them?

Tool Selection (Managers)

Which AI for what purpose:

General assistants (ChatGPT, Claude) vs. specialised tools
Approved vs. shadow AI (security implications)
Build vs. buy decisions
Cost-benefit analysis for AI tools

Exercise: Evaluate three AI tools for a specific department need. Recommend one with reasoning.

Process Redesign (Managers)

AI doesn't just automate existing processes—it enables new ones:

Identifying AI-amenable tasks
Redesigning workflows with AI in the loop
Human-AI handoff design
Quality control integration

Exercise: Map an existing process. Redesign it assuming AI assistance. What changes?

Common Training Mistakes

Mistake 1: All Theory, No Practice

Knowing that GPT-4 has 1.7 trillion parameters is useless. Being able to write a prompt that generates a useful first draft of a customer email is valuable.

Ratio should be: 20% concepts, 80% hands-on.

Mistake 2: Fear-Based Training

"AI will take your job unless you learn this!"

This creates anxiety and resistance, not learning. Better framing: "AI can make your job easier and more interesting. Here's how."

Mistake 3: Ignoring the Skeptics

Some people will be skeptical. That's healthy. Don't dismiss them—engage them.

Give skeptics challenging tasks where AI genuinely helps. Let them discover value themselves. They often become your best advocates.

Mistake 4: No Follow-Up

Training without application decays fast. People need:

Immediate opportunities to apply learning
Ongoing support when they get stuck
Regular reinforcement and updates

Mistake 5: Forgetting the Guardrails

AI training without usage policies is incomplete. Cover:

What data can be put into AI tools
What tools are approved
What decisions require human oversight
How to report problems

Measuring Training Effectiveness

Don't just count attendance. Measure impact:

Adoption metrics:

% of team using AI tools regularly
Types of tasks being AI-assisted
Tool usage patterns

Productivity metrics:

Time saved on specific tasks
Output quality changes
Error rates before/after

Confidence metrics:

Self-reported comfort with AI
Willingness to try new applications
Ability to evaluate AI outputs

Survey at baseline, 30 days, and 90 days post-training.

Building Internal Champions

Don't try to train everyone from scratch. Build a network of AI champions:

Identify enthusiastic early adopters
Give them deeper training
Empower them to support their teams
Create channels for knowledge sharing
Recognise and reward their contributions

Champions scale your training effort and provide peer learning that formal training can't match.

Our Training Approach

We offer AI training programs tailored to Australian businesses:

Executive briefings on AI strategy
Manager workshops on AI application
Team training on productivity tools
Technical training for developers

Training is most effective when paired with actual AI implementation projects—people learn best when applying skills to real problems.