Competencies and occupational evidence
How can you build a reliable competency matrix without confusing job requirements with demonstrated acquired competencies?
1960 € per session, excl. VAT
In short
A 7-hour day, excluding breaks, to turn activity descriptions into a competency framework and then build a matrix that HR can use. Generative AI facilitates extraction and harmonisation; participants check sources, characterise gaps and prepare for professional validation before any use.
Why this training?
Similar titles can cover different skills, while the same competency appears in several formulations. Without a shared definition and supporting evidence, the matrix gives a misleading impression of precision and conceals development needs. Generative AI can suggest groupings and rewordings, but it does not know employees’ actual acquired competencies. The approach separates requirements, observations and hypotheses to make every assigned level traceable and open to challenge. Running case study: The fictional organisation Virelia Maintenance is preparing for multi-skilling across its field service and planning teams. Three job descriptions and eight fictional activity files contain inconsistent wording and incomplete evidence. Participants must produce a matrix that enables discussion of development needs. Transfer to the workplace: Reuse the matrix within a limited occupational scope, using authorised, anonymised data. Organise an HR–manager review, measure documentation coverage and rating disagreements, then set an update frequency.
Objectives of this training
- Define the occupations and uses covered by the mapping
- Extract observable competencies from documented activities
- Build a proficiency scale with behavioural criteria
- Complete a matrix distinguishing evidence from missing information
- Measure the quality of the framework and prioritise gaps for examination
Who is it for and what are the prerequisites?
Roles and tasks concerned
- HR, competency development and training managers
- Managers and subject matter leads participating in competency reviews
Prior skills
- Know the activities and responsibilities within an occupational scope
- Be able to formulate a request to generative AI and check its response
- Use filters and simple tables in a spreadsheet
What you will work on
Define HR uses
Objective: Separate mapping, assessment and individual decision-making. Occupational scope, granularity, confidentiality and validation responsibilities.
Extract expected capabilities
Objective: Move from tasks to observable competencies. Extraction request with source references; distinction between activity, knowledge and competency; no additions without evidence.
Harmonise the framework
Objective: Establish consistent definitions and expected levels. Action–context labels, synonyms, granularity; levels based on autonomy and complexity.
Build the documented matrix
Objective: Distinguish demonstrated proficiency from missing information. Job requirement, dated evidence, observed level, unassessed status and comparable gap.
Check rating quality
Objective: Measure coverage and consistency. Checking sources, documented cells, remaining duplicates and agreement between assessors.
Prepare the professional review
Objective: Turn gaps into development topics. Occupational criticality, additional evidence, training and updating; no automated sensitive decision-making.
What you take away
- Scope note and rules for HR use
- Competency framework and proficiency scale
- Requirements–evidence–gaps matrix with sources
- Quality control checklist and validation sheet
Method and assessment
- Short inputs followed by immediate practice on the running case study.
- Individual work followed by work in pairs, with peer review of outputs.
- Facilitated group discussion: what works, what needs correcting and what remains a human decision.
- Preparation: Arrange access to a generative AI assistant authorised by the organisation and to a spreadsheet. Identify a priority HR use; exercises use only the fictional documents provided or approved anonymised documents.
Observable criteria: Each competency describes an observable capability, with no unjustified duplication; Each proficiency level has an explicit distinguishing criterion; Each rating refers to evidence; missing evidence is flagged; The percentage of documented cells and disagreements are calculated; Priority gaps are submitted for human validation, with no automated sensitive decision-making.
Frequently asked questions
Is an existing framework required?
No, the fictional descriptions allow an initial framework to be built within a defined scope.
Does AI assign individual levels?
Its suggestions remain hypotheses; a qualified professional validates the ratings against evidence.
Can real interview records be imported?
No identifying data during the exercises; permissions and conditions must be checked with your legal department.
Does an empty cell indicate a deficiency?
No, it flags missing information that must be documented before any conclusion is drawn.
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