AI Governance · Module 4 · Organisation and Leadership

Competence, Roles and the AI Architect

The lesson names the people an AI programme needs. Participants define the AI architect in relation to the established architecture roles, separate roles that build from roles that verify, examine the human oversight officer and four new roles that follow the usage modes, read Article 4 on AI literacy as amended, and work with a competence framework and individual competence records that a readiness gate can check.

Lesson 14 of 26Chapter 1475 minutes

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Learning objectives

After the lesson the participant can:

Key concepts

AI architect
Owns the architectural treatment of the model as a derived, probabilistic, degrading component whose behaviour must be evaluated and not specified: the AI Decision Records, the evaluation strategy, technical input to classification and the oversight path. A governance position, accountable for decisions and not for tooling.
AI system owner
The accountable person for one system in use, whose role the deployer duties of Article 26 give legal content. In Nordvik Region's description the owner may suspend the system at any time but may not change evaluation thresholds, which model risk sets.
Agent operator
Runs Mode 5 agents in production: works the approval queue for actions above the agent's threshold, reviews overreach signals and may revoke the agent's credentials, which cuts off further actions because the agent acts only through its own scoped identity.
AI literacy under Article 4
As amended by the Digital Omnibus on AI, providers and deployers take measures to support AI literacy among staff, without guaranteeing a specific level for any individual. The chapter reads it as role-specific and to be evidenced, and as the floor for high-risk systems.
Competence record
A per-person record linking each competence to evidence of practice, with a validity date and revalidation triggers such as a major provider version. The readiness check reads it before a high-risk system goes live.

Interactive exercises

Do them here and get feedback at once. Choose a card and then where it belongs, or drag it.

Interactive exercise 1 · Match

Roles around the AI architect

Match each role with what the chapter says it does.

Interactive exercise 2 · Scenario

A major provider update and a competence record

Apply the chapter's competence record and readiness check to the situation at Nordvik Region.

Teaching plan

10 minOpening: participants name who in their organisation decides whether a model is good enough, and which existing architecture role that person formally holds.
20 minTeacher presentation of the role map: the AI architect, builders, verifiers, oversight staff and the four new roles, then Article 4 as amended and management-body competence under NIS 2 and DORA.
10 minGroup reading of Nordvik Region's nine-hour learning path for oversight officers: why each module ends in a demonstration that writes to the competence record.
25 minExercise in pairs: write an AI system owner role description and a competence record for one oversight officer.
10 minDebrief: pairs present their may-not and does-not lines and their revalidation triggers; the group discusses hire versus upskill for the gaps found.

Discussion

  1. The softened wording of Article 4 changes less in architecture than it appears to. What would an organisation lose if it dismantled role-specific competence records because of the amendment?
  2. Caseworkers who become skilled oversight officers need that skill recognised, or oversight is felt as a burden added to a full caseload. How is oversight work recognised where you work?
  3. The chapter proposes a cooling-off period, for example twelve months, before a person validates a system they helped to build. Which moves between building and verifying happen in your organisation without one?

Written exercise

Exercise · 25 minutes

Role description and competence record

For one AI system in your organisation, or Nordvik Region's benefits-triage system, write a role description for the AI system owner in the form of ROLE-AI-OWNER: the holder and reporting line, five accountabilities with the article each derives from where one applies, the decision rights including what the owner may suspend, one thing the owner may not change, what the owner does not do, and the competence and literacy level required with a refresh interval. Then write a competence record for one person who oversees the system: the role and authority, three competence areas each with evidence of practice and not attendance alone, a validity date and at least two events that force revalidation.

Deliverable: A one-page AI system owner role description and one competence record with evidence per competence, validity date and revalidation triggers.

Self-test

Choose an answer to see whether it is right, or open the answer below each question.

  1. What did the Digital Omnibus on AI change in Article 4 of the AI Act, as the chapter describes it?

    Show the answer

    A. It replaced measures to ensure a sufficient level of literacy with measures to support literacy
    The 2024 text required measures to ensure a sufficient level; the amended text asks for measures to support literacy without guaranteeing a specific level. The duty still applies to every AI system and no certificate is required.

  2. Why does the evaluation engineer role separate from the AI engineer once a programme has several systems?

    Show the answer

    C. Because of separation of duties: whoever tuned a model to pass an evaluation should not certify it
    The chapter grounds the split in separation of duties, a regulatory expectation in financial services and sound practice everywhere. The evaluation engineer reports to model risk or the platform team.

  3. What is the chapter's strategic answer to the thin and expensive market for AI competence?

    Show the answer

    B. Upskill more than hire, and hire for scarce specialisms and the first AI architect
    Existing staff know the domain, the data and the politics, most roles are existing roles with an AI syllabus added, and the enabling team is itself an upskilling engine. Hiring is for specialisms upskilling cannot supply fast enough.