“We’re ready to roll out AI training.”

When I hear this, my first question is always: “How do you know what you’re ready for?”

Many organisations launch AI initiatives without understanding their starting point. They design programs based on assumptions about workforce readiness that turn out to be wrong. The result: programs that don’t address actual gaps and fail to build needed capabilities.

Readiness assessment should precede program design. Here’s how to do it well.

The Dimensions of AI Readiness

AI readiness isn’t one thing. It spans multiple dimensions:

Technical Readiness

Can people use AI tools technically?

• Basic digital literacy levels
• Experience with AI tools (personal or professional)
• Comfort with new technology
• Device and connectivity access

Technical readiness varies widely. Don’t assume it.

Conceptual Readiness

Do people understand AI fundamentally?

• Mental models of what AI is
• Understanding of AI capabilities and limitations
• Awareness of AI in their industry
• Clarity on AI applications in their work

Conceptual gaps create confusion and misuse.

Attitudinal Readiness

How do people feel about AI?

• Enthusiasm vs. anxiety
• Perceived threat vs. opportunity
• Trust in AI outputs
• Willingness to experiment

Attitudes shape adoption more than capabilities often do.

Contextual Readiness

Is the environment ready to support AI use?

• Manager attitudes and support
• Time available for learning
• Policies and permissions
• Tool access
• Use cases identified

Individual readiness without contextual support produces frustration.

Skill Readiness

Do people have foundational skills AI builds on?

• Critical thinking
• Written communication
• Problem decomposition
• Quality judgment

AI proficiency builds on foundational capabilities.

Assessment Methods

Multiple methods provide comprehensive understanding:

Surveys

Surveys offer broad reach and quantifiable data:

Technical readiness questions:

• “How often do you use AI tools in your personal life?” (frequency scale)
• “Rate your comfort with learning new digital tools” (comfort scale)
• “Have you used any AI tools for work purposes?” (yes/no, with specifics)

Conceptual readiness questions:

• “In your own words, what is artificial intelligence?” (open-ended)
• “Which of the following can AI currently do well?” (knowledge check)
• “How might AI be relevant to your work?” (open-ended)

Attitudinal questions:

• “AI will make my work easier” (agreement scale)
• “I’m worried AI might affect my job security” (agreement scale)
• “I’m curious to learn more about AI tools” (agreement scale)

Contextual questions:

• “I have time to learn new skills during work hours” (agreement scale)
• “My manager supports experimentation with new tools” (agreement scale)
• “I know what AI tools are approved for work use” (yes/no)

Survey design matters. Poorly designed surveys produce misleading data.

Skills Assessments

Directly assess capabilities:

Technical assessments:

• Navigate to an AI tool and complete a basic task
• Generate a response to a provided prompt
• Evaluate an AI output for accuracy

Knowledge assessments:

• Identify appropriate AI use cases (scenario-based)
• Recognise AI limitations (multiple choice)
• Understand organisational policies (comprehension check)

Assessments show what people can do, not just what they think they can do.

Interviews and Focus Groups

Qualitative methods reveal nuance:

Interview topics:

• Current technology comfort and challenges
• Perceptions of AI in work context
• Concerns and hopes about AI
• Support needs and preferences

Focus group discussions:

• Group dynamics around AI topics
• Shared concerns and misconceptions
• Variation within teams
• Cultural factors

Qualitative data explains why readiness is what it is.

Observational Assessment

Watch actual behaviour:

• How do people respond when AI is mentioned?
• What questions do they ask?
• How do they react to AI demonstrations?
• What’s the informal conversation about AI?

Observation reveals what surveys and interviews might miss.

Manager Assessment

Managers observe their teams:

• Team comfort with technology
• Learning agility observations
• Support needs they’ve identified
• Contextual factors in their area

Manager perspectives complement self-assessment.

Assessment Design

A comprehensive assessment requires thoughtful design:

Coverage

Ensure assessment covers:

• All readiness dimensions
• All relevant populations
• Different organisational levels
• Geographic and functional diversity

Incomplete coverage creates blind spots.

Sampling

Balance comprehensiveness with practicality:

• Census surveys for broad data (short, easy to complete)
• Sample interviews for depth (representative selection)
• Targeted assessments for critical populations
• Focus groups for areas of particular interest

Not everyone needs to complete everything.

Anonymity and Safety

Honest responses require safety:

• Anonymise data appropriately
• Communicate how data will be used
• Emphasise developmental purpose
• Separate from performance evaluation

People won’t reveal concerns if they fear consequences.

Benchmarking

Context helps interpret results:

• Industry benchmarks where available
• Internal comparisons across populations
• Historical comparisons if previous assessments exist
• Comparison to program requirements

Raw numbers mean more with context.

Making Sense of Results

Assessment produces data. Now what?

Aggregate Analysis

Look at organisation-wide patterns:

• Overall readiness profile
• Dimension-by-dimension breakdown
• Distribution of readiness levels
• Key gaps and strengths

Aggregate analysis guides overall strategy.

Segment Analysis

Break down by relevant segments:

• Function/department
• Role level
• Location
• Tenure
• Age (handled sensitively)

Segment analysis reveals where differentiation is needed.

Correlation Analysis

Look for relationships:

• What predicts technical readiness?
• What’s associated with positive attitudes?
• What contextual factors matter most?
• Where do multiple readiness gaps cluster?

Correlations guide intervention design.

Qualitative Integration

Numbers don’t tell the whole story:

• What explains the patterns?
• What concerns emerged in interviews?
• What contextual factors matter?
• What do managers observe?

Qualitative data gives meaning to quantitative patterns.

From Assessment to Action

Assessment should drive action:

Gap Prioritisation

Not all gaps are equally important. Prioritise based on:

• Impact on strategic AI objectives
• Size of the gap
• Addressability through intervention
• Consequences of not addressing

Focus resources on highest-priority gaps.

Program Design Implications

Assessment should shape program design:

• High technical readiness: Less basic instruction, faster pace
• Low technical readiness: More fundamentals, extended practice
• High anxiety: More safety building, explicit concern addressing
• Strong foundation skills: Focus on AI-specific capabilities
• Varied readiness: Differentiated pathways

Design programs that start where people actually are.

Contextual Interventions

Some gaps aren’t training problems:

• Manager support gaps need manager development
• Time constraints need workload management
• Policy confusion needs better communication
• Tool access needs provisioning

Address contextual barriers alongside capability building.

Communication Approach

Assessment reveals what needs communicating:

• Address misconceptions identified
• Speak to concerns surfaced
• Build on positive attitudes discovered
• Tailor messages to different segments

Assessment data shapes communication strategy.

Ongoing Readiness Tracking

Readiness isn’t static. Track it over time:

Periodic Reassessment

• Annual comprehensive assessment
• Quarterly pulse surveys on key indicators
• Ongoing informal observation

Track trends, not just snapshots.

Leading Indicators

What signals readiness change?

• Training completion and engagement
• Tool adoption metrics
• Manager feedback
• Support request patterns

Leading indicators enable proactive response.

Lagging Indicators

What shows readiness improvement?

• Capability assessment scores
• Application in actual work
• Productivity and quality outcomes
• Employee confidence measures

Lagging indicators confirm progress.

Common Assessment Mistakes

Avoid these pitfalls:

Assessing too late. Assessment after program design limits its value. Assess first.

Over-relying on surveys. Surveys capture what people will tell you, which isn’t everything. Use multiple methods.

Ignoring context. Individual readiness without supportive context goes nowhere. Assess the environment too.

Treating readiness as binary. Readiness exists on spectrums. Understand distributions, not just averages.

Assessing once and forgetting. Readiness changes. Track it over time.

Paralysis by analysis. Assessment should enable action, not delay it. Assess enough to act wisely, then act.

Getting Started

If you haven’t assessed readiness, start now:

1. Define the readiness dimensions relevant to your context
2. Design assessment approach matching your resources
3. Develop or adapt assessment instruments
4. Deploy to representative population
5. Analyse and interpret results
6. Translate findings to program implications
7. Build ongoing tracking approach

Assessment isn’t optional. It’s the foundation for effective AI capability building.

Know where you’re starting. Only then can you plan how to get where you’re going.