“We need to train everyone on AI.”

I hear this regularly, and it’s true—but misleading. Yes, most people need some AI capability. But the specific capabilities needed vary dramatically by role.

The accounts payable clerk needs different AI skills than the marketing strategist. The customer service representative needs different capabilities than the data analyst. The frontline supervisor needs different learning than the executive.

One-size-fits-all AI training wastes resources, frustrates learners, and fails to build the capabilities that actually matter for each role.

Differentiated pathways are the answer.

The Case for Differentiation

Why differentiate rather than standardise?

Relevance Drives Learning

People learn when content connects to their actual work. Generic AI training feels abstract and forgettable. Role-specific training feels relevant and applicable.

When learners see direct connection to their daily tasks, engagement and retention increase dramatically.

Time Is Limited

Everyone has limited learning time. Using that time on skills irrelevant to a role is waste. Differentiated pathways focus precious time on what matters most for each person.

Capability Needs Vary

Different roles genuinely need different AI capabilities:

• A writer needs content generation skills
• An analyst needs data interpretation skills
• A manager needs team enablement skills
• A strategist needs evaluation and judgment skills

These aren’t variations on a theme. They’re fundamentally different capability sets.

Adoption Follows Relevance

People adopt AI for tasks where they see value. Role-specific training emphasises the highest-value applications for each role, accelerating meaningful adoption.

A Framework for Role-Based Pathways

Structure pathways around three dimensions:

Dimension 1: Foundation (Common)

Some AI understanding is valuable for everyone:

• What AI is and isn’t
• Basic capabilities and limitations
• Organisational AI policies
• General ethical considerations
• Safety and security basics

This foundation can be common across roles—typically 2-4 hours of learning.

Dimension 2: Core Application (Role-Specific)

The primary AI applications for each role:

For a marketing professional:

• Content generation and refinement
• Campaign ideation
• Audience analysis
• Performance reporting

For a finance professional:

• Data analysis and interpretation
• Report generation
• Pattern identification
• Forecasting support

For a customer service professional:

• Response drafting
• Issue summarisation
• Knowledge base searching
• Sentiment analysis

This layer is highly differentiated—typically 4-8 hours depending on role complexity.

Dimension 3: Advanced Application (Role-Specific)

Deeper capabilities for those who need them:

Advanced marketing:

• AI-assisted strategy development
• Cross-channel campaign orchestration
• Predictive analytics application

Advanced finance:

• Complex modelling assistance
• Anomaly detection
• Scenario planning

Advanced customer service:

• Escalation prediction
• Process optimisation
• Training content development

This layer is optional for most, required for some—typically 4-12 hours for those who need it.

Role Category Examples

Let me illustrate pathways for common role categories:

Knowledge Workers (Writers, Analysts, Researchers)

Foundation: AI basics, organisational policies

Core:

• Effective prompting for their domain
• Content generation and refinement
• Research assistance
• Analysis and synthesis
• Quality evaluation and verification

Advanced:

• Workflow integration
• Complex research methodologies
• Advanced analysis techniques
• Peer coaching

Time investment: Foundation (3 hours) + Core (6 hours) + Advanced (8 hours optional)

Customer-Facing Roles (Service, Sales, Support)

Foundation: AI basics, customer-context policies

Core:

• Response drafting
• Information lookup
• Issue summarisation
• Standard query handling
• Escalation recognition

Advanced:

• Complex issue resolution
• Predictive service
• Upselling assistance
• Quality improvement

Time investment: Foundation (3 hours) + Core (4 hours) + Advanced (4 hours optional)

Managers and Supervisors

Foundation: AI basics, management implications

Core:

• Supporting team AI adoption
• Workflow identification
• Performance in AI-augmented work
• Change management
• Policy application

Advanced:

• Strategic AI application
• Cross-team coordination
• Capability building
• Innovation fostering

Time investment: Foundation (3 hours) + Core (6 hours) + Advanced (6 hours optional)

Technical Roles (Developers, Engineers, IT)

Foundation: AI basics, technical context

Core:

• Code assistance tools
• Documentation generation
• Testing support
• Technical research
• Architecture considerations

Advanced:

• AI system integration
• Model evaluation
• Custom implementation
• Security considerations

Time investment: Foundation (3 hours) + Core (8 hours) + Advanced (12 hours optional)

Leadership and Strategy Roles

Foundation: AI basics, strategic implications

Core:

• AI opportunity evaluation
• Risk assessment
• Investment decisions
• Organisational readiness
• Communication and change leadership

Advanced:

• Transformation planning
• Vendor evaluation
• Governance design
• Future workforce planning

Time investment: Foundation (3 hours) + Core (4 hours) + Advanced (4 hours optional)

Building Role-Based Pathways

A systematic approach to creating differentiated pathways:

Step 1: Role Analysis

For each role category:

• What tasks comprise the role?
• Which tasks could AI assist?
• What value would AI assistance provide?
• What skills are needed to realise that value?

This analysis grounds pathways in actual work.

Step 2: Skill Mapping

From task analysis, identify required skills:

• Foundation skills (common)
• Core skills (role-specific)
• Advanced skills (for some)

Map skills to learning outcomes.

Step 3: Content Development

For each pathway:

• Foundation module (adaptable template)
• Core modules (role-specific)
• Advanced modules (optional depth)

Develop or curate content for each module.

Step 4: Delivery Design

How will each pathway be delivered?

• Asynchronous foundations
• Cohort-based core development
• Coaching for advanced capabilities

Match delivery to content and learner needs.

Step 5: Assessment and Tracking

How will you measure success?

• Completion tracking by pathway
• Skill assessment at key points
• Application observation
• Role-specific outcomes

Track progress differentiated by role.

Pathway Assignment

How do people get assigned to pathways?

Self-Selection With Guidance

Provide descriptions of each pathway and let people choose. Include diagnostic questions to guide selection.

Works when: People understand their roles well and will choose appropriately.

Manager Assignment

Managers assign team members to appropriate pathways based on role requirements.

Works when: Managers understand the pathways and their people’s needs.

Automatic by Role Code

HR systems assign pathways based on job codes or role classifications.

Works when: Role classifications are accurate and pathways align cleanly.

Hybrid Approach

Combination: Foundation assigned universally, core assigned by role, advanced self-selected.

Works when: You want both standardisation and individual choice.

Managing Complexity

Differentiation creates complexity. Manage it:

Modular Design

Build pathways from modular components that can be reused:

• Same foundation across pathways
• Shared modules where roles overlap
• Role-specific modules only where needed

Modularity reduces development burden.

Clear Documentation

Maintain clear documentation of:

• What each pathway includes
• Who should take each pathway
• How pathways progress
• Prerequisites and sequences

Documentation enables administration.

Technology Support

Use LMS capabilities:

• Pathway management and assignment
• Progress tracking by pathway
• Automated progression
• Reporting and analytics

Technology reduces manual overhead.

Realistic Scope

You don’t need a unique pathway for every job title. Categories that share 80%+ of AI skill needs can share pathways.

Aim for 5-10 pathway variations, not 50.

Continuous Evolution

Pathways need ongoing maintenance:

Regular Review

• Are skills still relevant as AI evolves?
• Are role definitions changing?
• What feedback are you getting?
• What outcomes are you seeing?

Review and update at least annually.

Feedback Integration

Collect and act on feedback:

• Relevance of content to actual work
• Gaps in coverage
• Quality of delivery
• Suggestions for improvement

Learners often see what designers miss.

New Role Coverage

As new roles emerge or existing roles transform, develop new pathways or adjust existing ones.

Pathways should evolve with the organisation.

The Investment and Return

Differentiated pathways require more upfront investment than one-size-fits-all programs. The investment includes:

• Analysis to understand role needs
• Multiple content development streams
• More complex administration
• Ongoing pathway management

But the return typically exceeds the investment:

• Higher relevance drives better learning
• Better learning drives better adoption
• Better adoption drives better outcomes
• Less time wasted on irrelevant content

The math works. Invest in differentiation.

Not everyone needs the same AI skills. Design your learning programs to reflect that reality.

AI consultants Melbourne can help organisations build targeted capabilities through tailored pathways for different role needs—more efficient than generic one-size-fits-all approaches.

Differentiation isn’t complexity for its own sake. It’s precision in service of outcomes.

Build the pathways that serve each role. Watch capability develop where it matters.