Selecting and implementing AI applications
Selects AI applications based on work need, cost, risk and fit with existing systems and guides adoption through to actual use.
How hrmforce measures this
- Assessment method
- Structured interview · rho 0.42 (SD 0.19)
- hrmforce instrument
- Structured interview, Portfolio
- Competency (50-framework)
- Innovative capacity
- Trainability
- medium
- Demand outlook 2026 to 2030
- rising
Behaviour-based interview with fixed questions and a rating scale per question.
Behavioural anchors
| Level | Behaviour at this level |
|---|---|
| N1 Guided | Tests a designated AI application in own work and reports what proved usable and what did not. works under supervision and follows instruction · routine, one variable at a time · own task |
| N3 Proficient | Compares applications on requirements, risk and cost, runs a pilot and guides colleagues through adoption. sets own approach and seeks input proactively · several variables, some ambiguity · own team or process |
| N5 Leading | Determines which AI applications the organisation uses, decides on scaling up and accounts for the choices made. sets the standard and the policy · strategic, under high uncertainty · organisation, value chain or profession |
N2 and N4 are deliberately not anchored. Raters place them between the anchors, following the O*NET convention.
Underlying skills
These skills inherit the assessment route and the behavioural anchors of this construct.
| T | Skill | Definition | Demand outlook 2026 to 2030 |
|---|---|---|---|
| V | Setting up retrieval augmented generation RAG · Retrieval augmented generation | Connects a model to an own document collection so answers rest on retrievable sources. | rising |
| V | Using embeddings Embeddings · Semantic search | Converts text into numeric vectors and searches on meaning instead of exact words. | rising |
| T | Using a vector database Vector database | Stores vectors in a vector database and configures searches on similarity and filters. | rising |
| V | Fine tuning a model Fine tuning | Trains an existing model further on own examples and judges whether that works better than a good prompt. | rising |
| V | Setting up an AI agent AI agent | Sets up an agent that performs steps with defined tools and determines where a human must approve. | rising |
| V | Selecting an AI application on a business case AI business case | Weighs expected benefit, cost, risk and support effort of an AI application against alternatives without AI. | rising |
| V | Running an AI pilot AI pilot · Proof of concept | Sets up a bounded trial with success criteria, measures the outcome and decides on continuing or stopping. | rising |
| V | Connecting AI to source systems AI integration | Connects an AI application through interfaces to existing systems and arranges permissions, logging and data scope. | rising |
| G | Guiding AI adoption AI adoption | Guides employees in new AI use with explanation, practice, ground rules and attention to resistance. | rising |