AI Management System

ISO/IEC 42001:2023 Implementation.

Create an enterprise AI management system with accountable ownership, AI inventory, risk/impact governance, lifecycle controls and measurable oversight.

Business context

What this capability solves

AI Management System implementation support from organizational gap assessment through audit and certification readiness.

EBP Integra delivery principle

Advice, controls, technology, governance, evidence and operating procedures are designed together so the capability can be sustained after implementation.

Deep-dive capabilities

Capability model

The service can be scoped as a focused engagement or combined into a broader enterprise programme.

AIMS Gap Assessment

Assess governance, AI use cases, responsibilities, lifecycle controls, risk and evidence.

AI System Inventory & Classification

Create a governed inventory with ownership, purpose, risk tier and lifecycle status.

Policy & Governance

Develop AI policy, roles, committees, approval gates and exception authority.

Risk & Impact Management

Implement AI risk/impact assessment, treatment, human oversight and monitoring.

Lifecycle & Supplier Controls

Govern data, models, development, deployment, third parties, incidents and changes.

Internal Assurance & Readiness

Support internal audit, corrective actions, management review and certification readiness.

Reference operating model

How the capability fits together

Final scope, control ownership and delivery model are confirmed during discovery.

Governance & Scope

Objectives, applicability, decision rights, owners, policies, risk appetite and acceptance criteria.

Assessment & Design

Evidence collection, gap/risk analysis, target controls, architecture, procedures and prioritized roadmap.

Implementation

Controls, documentation, workflows, integrations, configuration, training and remediation.

Assurance & Operations

Testing, evidence, management reporting, escalation, review cadence and continuous improvement.

Controls & governance

Defined management-system scope and context
Leadership roles and governance
Risk/opportunity assessment
Documented policies, procedures and controls
Competence, awareness and communication
Monitoring, internal audit, corrective action and management review

Priority use cases

Enterprise AI governance
Regulated AI adoption
AI provider/deployer assurance
AI management certification
Multi-business AI portfolio
Agentic AI governance

Key deliverables

AIMS gap report
AI inventory
AI policy/governance model
Risk/impact framework
Lifecycle procedures
Audit/readiness pack

Integration considerations

AI inventory
Model/agent lifecycle
Data governance
Security and privacy controls
Vendor/AI supply chain
GRC/evidence systems

Implementation

Phased delivery

Each phase produces decision-ready evidence and clear ownership for the next stage.

1. DiscoverConfirm scope, stakeholders, obligations, evidence, dependencies and risk drivers.

2. Assess & DesignAnalyze current state, define target controls and agree the implementation roadmap.

3. ImplementDeploy controls, documents, workflows, training and required technology/integration.

4. Assure & OperateValidate effectiveness, close residual gaps, hand over and establish recurring governance.

Outcome and KPI framework

AI inventory coverageRisk assessment coverageHigh-risk action closureControl evidence completenessInternal audit findings