M — Model

Design AI policies, risk frameworks, and decision flows

Definition

Model is the design and policy architecture stage of COMPEL. Before any AI system is built or deployed, the Model stage requires that its governance context is fully defined: what policies apply, what risks exist, how humans interact with the system, and what data it depends on. Retrofitting governance onto AI systems after deployment is substantially more expensive and less effective than building it in from the start.

Purpose

The purpose of Model is to enforce a design-first discipline. Every AI initiative must pass Gate M — the Design Approval gate — before any production investment begins. This gate verifies that solution architecture is sound, data readiness is confirmed, human-AI collaboration points are explicitly defined, and the policy framework is in place. Organizations that skip this stage consistently produce AI systems that fail audits, accumulate technical and ethical debt, and require costly remediation.

Key Activities

• AI Policy Framework authoring — drafting organization-wide policies on acceptable use, data handling, and human oversight
• System Registry architecture — designing the AI system registry schema, lifecycle states, and documentation requirements
• Risk Framework design — building the risk taxonomy, scoring methodology, and escalation criteria for AI systems
• Human-AI collaboration modeling — defining interaction patterns, override mechanisms, and human-in-the-loop requirements
• Data readiness validation — assessing data quality, lineage, access controls, and bias potential for each planned system
• Decision flow documentation — mapping the decision chains that AI systems will influence or automate
• Bias testing and red teaming protocol design
• Incident response procedure design — creating escalation paths, communication templates, and recovery playbooks for AI-related incidents
• Vendor and third-party AI evaluation — assessing external AI tools, APIs, and services against organizational governance standards and risk appetite

Outputs

• AI Acceptable Use Policy — governing document for permitted AI applications and prohibited uses
• AI System Registry Schema — data model, lifecycle states, and documentation templates
• Enterprise Risk Taxonomy for AI — risk categories, severity criteria, and escalation thresholds
• Human-AI Collaboration Blueprints — interaction models per system class with override and audit trail specifications
• Data Readiness Reports — structured assessment per AI system with gap remediation plans
• Decision Log Templates — standardized formats for capturing AI-influenced decision chains with accountability at each node
• Vendor Risk Assessment Criteria — evaluation framework for third-party AI tools and services against governance standards

Quality Gates

• Design documents approved by oversight body and risk owner
• Risk framework defined with taxonomy, scoring methodology, and escalation criteria
• AI system registry populated with all in-scope systems documented

Standards Alignment

• ISO/IEC 42001:2023: Clause 6 (Planning), Annex A — A.4, A.5, A.6, A.7
• NIST AI RMF 1.0: MAP (AI risk identification, impact analysis), GOVERN (documentation standards)
• EU AI Act 2024/1689: Article 13 (Transparency), Article 14 (Human oversight), Article 17 (Quality management system)
• IEEE 7000: Ethical design requirements, value-sensitive design processes, and impact assessment

Abdelalim, T. (2025). “Model Stage — COMPEL AI Transformation Framework.” COMPEL by FlowRidge. https://www.compel.one/stage/model