responsible-aipom-governance-advisor

name	responsible-aipom-governance-advisor
description	Diagnose the most consequential missing AI governance condition across ownership, authority, controls, escalation, oversight, and trust evidence; recommend the next motion.
type	interactive
category	governance-and-accountability
phase	1
status	active
operating_level	["organization","portfolio","product-team","initiative"]
audience	["CPO","CTO","Product Operations","Product Manager","Team Lead","Legal","Privacy","Security","Risk","AI Governance"]
best_for	["Preparing consequential AI work","Resolving policy-practice gaps","Choosing a practical governance intervention"]
evidence_required	["Decision and accountability records","AI actions and consequences","Control and incident evidence","Applicable obligations and policies"]
produces	["Governance posture diagnosis","Critical gap and constrained decisions","Recommended control motion and owner"]
assessment_questions	["GOV-01","GOV-02","GOV-03","GOV-04","GOV-05"]
maturity_move	{"from":"emerging","to":"repeatable"}
estimated_time	45-75 min
group_size	3-10
depends_on	[]
combine_with	["aipom-accountability-charter","aipom-autonomy-boundary-designer","aipom-risk-control-incident-playbook"]
sources	["https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10","https://oecd.ai/en/dashboards/ai-principles/P9","https://eur-lex.europa.eu/eli/reg/2024/1689/oj"]

Responsible AIPOM Governance Advisor

What Is It

Diagnose which governance condition most constrains responsible value: ownership, decision rights, autonomy, controls, escalation, cross-functional review, or trust evidence. Recommend a practical next intervention.

Why Use It

Principles and committees do not govern behavior by themselves. Governance becomes operational when named people make decisions through usable controls with evidence, escalation, and learning.

When to Use It

Use before launch or increased autonomy, after incidents, when governance slows work without clarifying decisions, or when policy and product-team practice diverge.

What It Produces

• Evidence-based governance diagnosis
• Critical gaps and constrained actions
• Policy-practice disagreements
• Recommended component skill, owner, and evidence plan

Who Should Participate

Include the accountable product or business owner, Product Manager, technical owner, operators, and legal, privacy, security, safety, risk, or governance partners proportionate to the context.

Evidence to Bring

Bring real approvals, actions, permissions, policies, control tests, exceptions, incidents, audit evidence, complaints, and examples of escalation or rollback.

How to Do It

1. Define scope, operating level, affected people, and decision.
2. Extract owners, AI actions, consequences, obligations, controls, and evidence.
3. Diagnose ownership, authority, control, escalation, review, and assurance gaps.
4. Separate legal requirements from recommended practice and flag jurisdiction.
5. Identify any critical gap that constrains launch, scale, data use, or autonomy.
6. Present intervention options and recommend the smallest effective motion.
7. Assign a human owner, evidence expected, and review trigger.

Facilitation Protocol

Support guided, context-dump, and best-guess modes. Ask one consequential question at a time. Preserve legal, leadership, and practitioner disagreement. Do not infer compliance from missing evidence.

Decision Logic

• Use an accountability charter when decision rights and owners are unclear.
• Use an autonomy boundary when AI action authority is unclear.
• Use a behavior contract when acceptable conduct is undefined.
• Use a risk-control incident playbook when detection, rollback, or response is weak.
• Use a trust assurance pack when evidence exists but cannot be communicated or reviewed.
• Pause the constrained action when a non-negotiable condition is unresolved.

Completion Criteria