Building an AI Literacy Program for Your Organisation

AI adoption fails when only a few people understand it. Success requires AI literacy across your entire organisation, from the CEO to the front-line staff.

But building AI skills does not require enterprise training budgets. This guide provides a practical framework for SMEs to develop AI capabilities at every level.

What is AI Literacy?

AI literacy is not about everyone becoming a data scientist. It is about everyone understanding:

• What AI can and cannot do (realistic expectations)
• How AI applies to their role (practical relevance)
• When to trust and question AI outputs (critical thinking)
• How to work effectively with AI tools (hands-on skills)

The Four Levels of AI Literacy

Level 1: AI Awareness

Who needs this: Everyone in the organisation Goal: Understand what AI is and why it matters to the business

Key Topics:

• What AI is (and is not)
• Current AI capabilities and limitations
• How competitors are using AI
• Your organisation's AI strategy
• Basic AI safety and ethics

Time Investment: 2-3 hours

Level 2: AI User

Who needs this: Anyone who will use AI tools in their role Goal: Effectively use AI tools while maintaining quality and safety

Key Topics:

• How to prompt AI effectively
• When to use AI vs manual approaches
• How to verify AI outputs
• Data privacy and security practices
• Approved tools and acceptable use policy

Time Investment: 4-6 hours plus ongoing practice

Level 3: AI Champion

Who needs this: 1-2 people per department Goal: Lead AI adoption within their team, identify use cases, train others

Key Topics:

• Advanced prompting techniques
• Workflow integration patterns
• Use case identification
• Basic troubleshooting
• Change management basics
• Training delivery skills

Time Investment: 2-3 days of training plus ongoing development

Level 4: AI Practitioner

Who needs this: IT team, data team, dedicated AI roles Goal: Implement, customise, and maintain AI solutions

Key Topics:

• AI architecture and infrastructure
• API integration and development
• Model evaluation and selection
• Security and compliance
• Vendor management
• Custom solution development

Time Investment: Ongoing professional development

Building Your Training Program

Step 1: Assess Current State

Before training, understand where people are:

Quick Survey Questions:

1. Have you used AI tools like ChatGPT or Claude? (Yes/No)
2. How confident are you using AI for work tasks? (1-5)
3. What concerns do you have about AI? (Open text)
4. What tasks would you like AI help with? (Open text)

Step 2: Define Target State

For each role or department, define:

• Which literacy level is appropriate
• Which specific skills are most important
• What tools they will use
• How success will be measured

Example Role Mapping:

Step 3: Create Learning Pathways

Structure training in progressive modules:

Pathway A: General AI Literacy (All Staff)

1. What is AI? (30 minutes, video)
2. AI at [Company Name] (30 minutes, presentation)
3. Our AI Policy (15 minutes, document)
4. Hands-On: Your First AI Conversation (1 hour, workshop)

Pathway B: AI User Skills (Tool Users) Prerequisites: Pathway A

1. Prompting Fundamentals (1 hour, workshop)
2. Your Department's AI Tools (2 hours, hands-on)
3. Quality Checking AI Outputs (1 hour, workshop)
4. Practice Projects (ongoing)

Pathway C: AI Champion Development (Champions) Prerequisites: Pathways A and B

1. Advanced Prompting (4 hours, workshop)
2. Use Case Identification (2 hours, workshop)
3. Change Management for AI (2 hours, workshop)
4. Training Others (2 hours, workshop)
5. Ongoing: Monthly champion meetings

Step 4: Deliver Training

Training Delivery Options for SMEs:

Recommended Approach:

• Use free online resources for awareness
• Run internal workshops for user skills
• Invest in external training for champions
• Partner with vendors for tool-specific training

Step 5: Reinforce and Sustain

Training without reinforcement fades quickly.

Reinforcement Activities:

• Weekly AI tips via email or Slack
• Monthly "AI wins" sharing sessions
• Quarterly skill refreshers
• Annual full program updates
• Continuous access to learning resources

Practical Training Resources

Free Resources

For Awareness:

• LinkedIn Learning AI courses (often free with library card)
• Google's AI for Everyone course
• YouTube explainers from reputable sources
• Vendor documentation and tutorials

For User Skills:

• OpenAI's prompt engineering guide
• Anthropic's Claude documentation
• Microsoft Copilot learning paths
• Vendor-specific training materials

Budget Training Options

$0 - Internal Only:

• Champions train their teams
• Use vendor documentation
• Run lunch-and-learn sessions
• Create internal guides and templates

$500-2000 - Enhanced:

• Udemy/Coursera team licenses
• Bring in a speaker for kick-off
• Purchase premium course access
• Tool-specific certifications

$5000+ - Comprehensive:

• External workshop facilitation
• Custom training development
• Certification programs
• Ongoing coaching

The Champion Model

AI Champions are your force multipliers. Invest in them heavily.

Champion Responsibilities

• Stay current on AI developments
• Identify AI use cases in their area
• Provide first-line support to colleagues
• Train new team members
• Collect feedback and success stories
• Participate in monthly champion meetings

Selecting Champions

Look for people who:

• Are curious and experimental
• Have credibility in their department
• Can explain complex things simply
• Are patient with less technical colleagues
• Have capacity to take on this role

Supporting Champions

• Dedicate 10-20% of their time to the role
• Provide access to premium learning resources
• Include in AI project discussions
• Recognise their contributions publicly
• Create a champion community for peer support

Measuring Training Effectiveness

Leading Indicators

Track these during training:

• Training completion rates
• Quiz/assessment scores
• Workshop participation
• Questions asked

Lagging Indicators

Track these after training:

• AI tool adoption rates
• Self-reported confidence levels
• Use case submissions
• Quality of AI outputs
• Time savings reported
• Incident rates

Sample Metrics Dashboard

Common Training Mistakes

Mistake 1: One-Size-Fits-All Executives need different training than front-line staff. Customise by role.

Mistake 2: All Theory, No Practice Include hands-on exercises with real work examples, not abstract demos.

Mistake 3: Training Then Forgetting Skills decay without use. Build in reinforcement and ongoing practice.

Mistake 4: Ignoring the Skeptics Address concerns directly. Forced enthusiasm backfires.

Mistake 5: No Post-Training Support Provide help resources for when people get stuck after training.

90-Day Implementation Plan

Days 1-30: Foundation

• Survey current AI literacy levels
• Define target levels by role
• Identify AI Champions
• Create acceptable use policy
• Gather training resources

Days 31-60: Core Training

• Roll out awareness training to all staff
• Train champions in advanced skills
• Begin user training by department
• Set up support channels

Days 61-90: Reinforcement

• Launch weekly AI tips
• Hold first "AI wins" session
• Collect feedback and adjust
• Plan quarterly refresh

Next Steps

1. Assess your current AI literacy levels with a simple survey
2. Identify potential champions in each department
3. Create your acceptable use policy (essential before training)
4. Start with awareness training for all staff
5. Build from there based on feedback and adoption

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Want to assess your organisation's AI skills readiness? Skills and training is one of six pillars in our AI Readiness Assessment. Take the free assessment to see how you score and get personalised recommendations.