“We need AI training.”

It’s the most common request L&D hears. But the request often comes without understanding of where the organisation actually stands—what readiness exists, what gaps need addressing, what order of development makes sense.

Assessment should precede training. Here are frameworks for understanding AI readiness in 2026.

Why Assessment Matters

Jumping into AI training without assessment produces problems:

Misaligned content. Training that’s too basic for some, too advanced for others, and disconnected from actual needs.

Missed foundations. AI fluency builds on prerequisites. Without assessment, you don’t know what foundations are missing.

Wasted investment. Training people in capabilities they already have or can’t yet apply.

Change management failures. Not understanding resistance sources leads to adoption struggles.

Strategic disconnection. Training that doesn’t connect to how the organisation actually needs to use AI.

Assessment creates the foundation for effective development investment.

Framework 1: Individual AI Readiness

Assess where individuals stand on AI capability:

Dimension 1: AI Awareness

• Understanding of what AI is and isn’t
• Awareness of AI applications relevant to their work
• Knowledge of AI tools available to them
• Understanding of AI limitations and risks

Assessment methods: Knowledge assessments, surveys, conversations

Dimension 2: AI Tool Proficiency

• Ability to use relevant AI tools effectively
• Quality of AI-assisted outputs
• Efficiency of AI collaboration
• Breadth of tool familiarity

Assessment methods: Practical demonstrations, output evaluation, usage analytics

Dimension 3: AI Application Judgment

• Ability to identify appropriate AI use cases
• Judgment about when to use versus not use AI
• Understanding of output evaluation requirements
• Recognition of AI-specific risks

Assessment methods: Scenario-based assessment, decision evaluation, case analysis

Dimension 4: AI Adaptation Mindset

• Openness to AI-augmented work
• Willingness to experiment with AI tools
• Growth orientation toward AI capability
• Change readiness for AI transformation

Assessment methods: Surveys, behavioural observation, adoption patterns

Individual Readiness Scoring

Create levels that enable targeting:

Level 1: AI Unaware - Minimal understanding of AI and no tool usage Level 2: AI Aware - Basic understanding but limited application Level 3: AI Exploring - Beginning to use tools but inconsistently Level 4: AI Applying - Regular effective use of AI tools Level 5: AI Integrating - Sophisticated integration of AI into workflow Level 6: AI Leading - Helping others develop AI capability

Different levels require different development.

Framework 2: Organisational AI Readiness

Beyond individuals, assess organisational conditions:

Dimension 1: Leadership and Strategy

• Clarity of AI vision and strategy
• Leadership commitment to AI adoption
• Resource allocation for AI capability building
• Alignment of AI direction across organisation

Assessment methods: Leadership interviews, strategy review, budget analysis

Dimension 2: Culture and Change Readiness

• Organisational openness to technological change
• Risk tolerance for experimentation
• Learning culture strength
• Historical change management success

Assessment methods: Culture assessments, change history analysis, employee surveys

Dimension 3: Infrastructure and Tools

• Availability of AI tools to employees
• Quality of technology infrastructure
• Integration of AI tools with existing systems
• Support for AI tool usage

Assessment methods: Technology audit, user experience assessment, support analysis

Dimension 4: Governance and Policy

• Clarity of AI usage policies
• Data governance maturity
• Risk management for AI
• Compliance and ethics frameworks

Assessment methods: Policy review, governance assessment, risk analysis

Dimension 5: Skills and Capability

• Current workforce AI capability levels
• Learning capacity for AI development
• Key talent for AI leadership
• Capability development infrastructure

Assessment methods: Skills assessment, learning capacity analysis, talent review

Organisational Readiness Scoring

Rate each dimension and identify gaps:

Nascent: Beginning stages, significant development needed Developing: Some progress, substantial gaps remain Established: Solid foundation, refinement opportunities exist Advanced: Strong capability, optimisation focus Leading: Best-in-class, continuous innovation

Different organisational readiness levels require different intervention strategies.

Framework 3: Role-Specific AI Readiness

Different roles have different AI readiness requirements:

Role Analysis Components

For each key role:

AI opportunity mapping: What AI applications are relevant to this role?

Required AI capabilities: What AI skills does effective role performance require?

Current capability status: How do incumbents compare to requirements?

Development priority: How urgently do gaps need addressing?

Priority Role Categories

Typically prioritise assessment for:

High AI impact roles: Roles where AI significantly changes work Large population roles: Roles with many incumbents where development scales Strategic roles: Roles critical to organisational strategy Customer-facing roles: Roles where AI affects customer experience

Framework 4: Use Case Readiness

Assess readiness for specific AI applications:

For each priority AI use case:

Business value: What value does this use case create?

Technical feasibility: Is the AI technology mature enough?

Data availability: Is required data accessible and quality?

Skill requirements: What capabilities do users need?

Current readiness: How close is the organisation to effective implementation?

Gap analysis: What’s missing and how difficult to address?

Development path: What sequence of investments closes gaps?

Use case assessment connects AI readiness to specific business applications.

Assessment Implementation

How to conduct AI readiness assessment:

Phase 1: Scope and Plan

• Define assessment purpose and scope
• Identify priority populations and use cases
• Select assessment methods
• Plan data collection

Phase 2: Data Collection

• Deploy surveys and assessments
• Conduct interviews and focus groups
• Gather usage and performance data
• Analyse existing documentation

Phase 3: Analysis

• Score individuals and organisation against frameworks
• Identify patterns and themes
• Compare to requirements and goals
• Prioritise gaps by impact and urgency

Phase 4: Recommendations

• Translate findings into development priorities
• Recommend intervention strategies
• Suggest resource allocation
• Outline timeline and milestones

Phase 5: Communication

• Report findings to stakeholders
• Build understanding and support
• Align leadership on priorities
• Communicate to affected populations

From Assessment to Action

Assessment produces value only through action:

Development planning: Assessment results should directly inform learning strategy

Targeting: Different readiness levels get different development

Resource allocation: Investment follows priority gaps

Progress tracking: Reassessment measures development impact

Continuous improvement: Assessment repeats to track evolving readiness

For organisations seeking structured AI readiness assessment and development, AI consultants Sydney include assessment frameworks and targeted development pathways based on identified gaps.

Common Assessment Pitfalls

Avoid these mistakes:

Assessment without action: Collecting data that doesn’t drive decisions

One-time snapshot: Treating assessment as single event rather than ongoing practice

Self-assessment only: Relying entirely on self-report without validation

Generic assessment: Using general frameworks without organisational customisation

Assessment fatigue: Over-surveying employees without demonstrating value

The Strategic View

AI readiness assessment isn’t just operational—it’s strategic:

Investment justification: Assessment evidence supports resource requests

Priority setting: Assessment identifies where investment has most impact

Progress demonstration: Repeated assessment shows capability development

Competitive positioning: Understanding readiness relative to industry and competitors

Assessment provides the foundation for strategic AI capability development.

Getting Started

If you haven’t conducted AI readiness assessment:

1. Define scope. What populations and use cases are highest priority?

2. Select frameworks. Which assessment dimensions matter most for your context?

3. Choose methods. What combination of surveys, assessments, and qualitative methods will work?

4. Gather data. Collect information systematically.

5. Analyse and report. Draw conclusions and communicate findings.

6. Plan action. Convert assessment into development strategy.

Don’t launch AI training without understanding where you’re starting from.

Assessment creates the foundation for effective AI capability development. Build that foundation before investing heavily in training.

You’ll spend resources more wisely and develop capability more effectively.