Welcome to our AI Competency Model for organisations. This has been designed to be sufficiently psychometrically robust to support our range of psychometric solutions.
Our AI Competency Model
There are eight AI competencies in our AI competency model:
- Understanding AI competency
- Prompting AI competency
- Evaluation AI competency
- Decision-making AI competency
- Ethical awareness AI competency
- Workflow use AI competency
- Credibility judgement AI competency
- Confidence AI competency
The AI Competency Framework defines eight observable capabilities that capture real-world performance when using AI. These capabilities represent the application layer of AI skill.
Together, they describe how individuals:
- interact with AI
- interpret outputs
- make decisions
- integrate AI into workflows
Corporate Capability
This AI competency framwork is for executive teams, HR leaders, and organisations implementing AI responsibly. Our aim is to translate psychometric constructs into measurable skills that drive:
- AI oversight capability
- Evidence-based leadership
- Hiring fairness
- Strategic judgement under automation
In addition to this organisational AI competency model, there are aligned AI competency models for schools and AI competency models for individuals.
All are based upon the MosAIc universal AI skills framework.
Universal MosAIc AI Skills framework
The MosAIc AI Skills Framework defines the nine measurable cognitive and judgement constructs required in the AI era. MosAIc is a construct-based skills library which supports AI readiness and school-level cognitive development.
Cognitive Control AI skills
At a higher level, there are three over-arching, core sets of AI skills: the first if Cognitive Control AI skills:
- AI Inference Evaluation and AI Assumption Detection skills
- and AI Analytical Reasoning skills
- AI Structured Decision-Making skills
Adaptive Intelligence AI skills
The second is Information Judgment AI skills:
- AI Attention Control, AI Cognitive Flexibility and AI Learning Agility skills
- and AI Ethical Judgement skills
Information Judgement AI skills
The third is Adaptive Intelligence AI skills:
- AI Output Validation skills
- and Bias Recognition skills
- AI Information Credibility skills
- and AI Data Interpretation skills
AI Skills Framework
This AI skills framework defines the underlying skill structure that supports AI capability. It identifies nine core skills:
- Analytical Reasoning skills
- Cognitive Flexibility skills
- Ethical Judgement skills
- Information Credibility skills
- AI Output Validation skills
- Structured Decision-Making skills
- Bias Recognition skills
- Learning Agility skills
- Attention Control skills
These nine skills describe the foundational abilities that influence how individuals engage with AI systems. Whilst this model explains why individuals differ in their performance, it does not directly measure how well they perform in practice.
An AI competency model is required for this.
Who is our AI competency model designed for?
Schools & MATs competency framework
It is a schools AI competency framework designed to:
- Build AI literacy across pupils and staff
- Identify risk areas in AI use
- Support curriculum and governance
Organisations & HR Leaders
It is an AI organisational competency framework designed to:
- Assess AI capability across teams
- Reduce decision risk
- Embed responsible AI use
Individuals & Professionals
It is an AI competency framework for individual development to:
- Build future-ready skills
- Understand your AI strengths and blind spots
- Improve decision-making with AI
Why are a Skills Framework and a Competency Model Necessary?
A single-layer model is insufficient to capture AI capability.
- A skills model alone explains underlying ability but not performance
- A competency model alone measures behaviour but not its causes
The combination provides a complete system:
- Mosaic pillars → underlying capability structure
- AI competency framework → observable performance
This distinction mirrors established psychometric practice:
- latent traits vs behavioural indicators
- constructs vs outcomes
How is each AI Competency defined?
1. Understanding AI competency
How AI systems generate outputs:
- recognising probabilistic generation
- understanding limitations of training data
- awareness of hallucination risk
Critically, it is not technical depth that matters, but functional understanding.
An individual does not need to build a model, but must understand enough to interpret its outputs appropriately.
2. AI Prompting competency
Effective prompting involves:
- specifying constraints
- refining inputs
- recognising when outputs require adjustment
Prompting is often mischaracterised as a technical skill. Whereas, in practice, it is a function of:
- clarity of thinking
- ability to structure information
- iterative reasoning
It is less about “knowing tricks” and more about cognitive flexibility and precision.
3. AI Evaluation competency
Evaluation is the capacity to assess the quality of AI-generated outputs.
The AI Evaluation competency includes:
- identifying inaccuracies
- detecting omissions
- assessing relevance
It is one of the most critical capabilities, and one of the least developed.
A key risk in AI use is false confidence in plausible outputs. Evaluation mitigates this risk.
4. AI Decision-Making competency
AI systems do not make decisions. They provide inputs into decisions.
Our AI Decision-Making competency involves:
- integrating AI outputs with other information
- weighing uncertainty
- making informed judgements
It is particularly important in high-stakes contexts, where over-reliance on AI can lead to significant consequences.
5. AI Ethical Awareness competency
The Ethical awareness competency is the ability to:
- recognise these issues
- anticipate potential risks
- act responsibly
AI use raises ethical considerations across multiple domains:
- fairness
- bias
- accountability
- transparency
It is not a compliance exercise, but a judgement capability.
6. AI Workflow Use competency
The AI Workflow Use competency, which is defined as how effectively individuals integrate AI into their work processes, includes the following:
- knowing when AI adds value
- avoiding unnecessary use
- combining AI with human judgement
Effective workflow use is characterised by selective and purposeful application.
7. AI Credibility Judgement competency
Our AI Credibility judgement competency, defined as the ability to determine whether an AI output should be trusted, involves the following:
- assessing source reliability
- recognising signals of uncertainty
- identifying when verification is required
This capability is central to managing risk in AI-assisted environments.
8. AI Confidence competency
The AI Confidence competency, defined as how individuals perceive their own capability, is a vital competency since it influences:
- willingness to use AI
- susceptibility to over-reliance
- openness to revision
An individual’s AI confidence must be calibrated because:
- overconfidence leads to error
- underconfidence limits effectiveness
How do the AI Competencies Interact?
These eight AI competencies do not operate in isolation. One can influence another, as follows:
- Prompting influences evaluation
- Understanding AI shapes credibility judgement
- Evaluation informs decision-making
- Ethical awareness constrains decisions
Weakness in one area can undermine performance across other AI competencies.
Mapping AI Competency Model to AI Skills Framework
The relationship between the two models can be understood as follows:
- Analytical Reasoning → Evaluation, Decision-making
- Information Credibility → Credibility judgement
- Cognitive Flexibility → Prompting, Workflow use
- Ethical Judgement → Ethical awareness
- Bias Recognition → Evaluation, Credibility judgement
- Attention Control → Prompting, Workflow use
- Learning Agility → Understanding AI, Workflow use
- AI Output Validation → Evaluation
- Structured Decision-Making → Decision-making
This mapping demonstrates how underlying skills translate into observable behaviours.
Why Most AI Competency Models Fail?
A common limitation of existing AI competency models is their reliance on broad, loosely defined constructs.
Terms such as:
- critical thinking
- creativity
- collaboration
are frequently used, but rarely operationalised.
This creates several problems:
- lack of measurement precision
- difficulty in assessment
- limited practical application
By contrast, our psychometric AI Competency Framework defines capabilities in a way that is:
- observable
- differentiable
- measurable
Implications for Organisations
For organisations, the primary concern is not whether employees can use AI, but whether they can use it effectively and safely.
Key risks include:
- uncritical acceptance of outputs
- inappropriate use in decision-making
- failure to detect bias or error
A structured competency model allows organisations to:
- identify capability gaps
- understand risk exposure
- support targeted development
Implications for Professional Practice
At an individual level, AI capability is becoming a core component of professional effectiveness.
However, self-assessment is often unreliable.
Individuals tend to:
- overestimate their evaluation ability
- underestimate the complexity of AI outputs
- rely on surface plausibility
A structured framework provides a more accurate basis for understanding capability.
A Shift in Perspective
The introduction of AI requires a shift in how capability is conceptualised.
The key distinction is between:
- knowing how AI works
- and knowing how to use AI well
The former is informational. The latter is behavioural.
Conclusion
The AI Skills Competency Framework is concerned with the latter.
AI capability is not a single skill.
It is a structured combination of:
- cognitive abilities
- behavioural competencies
- judgement processes
The Mosaic skills framework provides the underlying skill architecture. The AI Skills Competency Framework provides the observable performance model.
Together, they offer a coherent way to understand what effective AI use looks like.
As AI becomes more embedded in everyday work and decision-making, this distinction will become increasingly important.
Where Most People Go Wrong
- Over-trusting AI outputs
- Using AI without checking results
- Confusing speed with accuracy
AI Literacy Training Options
You can find our full AI Literacy Training and AI Skills Development program here. There are modules for:
FAQ
What are AI skills?
They are the abilities needed to use, evaluate, and apply AI effectively.
Why measure AI capability?
Because it identifies strengths and development areas that improve performance.