ISO/IEC 42001 – AI Management System (AIMS) for Responsible AI Deployment

This topic is part of the SG Systems Global regulatory & operations glossary.

Updated November 2025 • ISO/IEC 23894, ISO/IEC TR 24028, EU AI Act • Governance, Quality, IT, Manufacturing, Compliance

ISO/IEC 42001 is the first-in-class management-system standard dedicated to artificial intelligence. It defines the scope, policies, organisational roles, lifecycle processes, risk integration, monitoring, documentation, and continuous improvement required to manage AI systems responsibly—and show auditors you did. If you’re embedding AI in regulated manufacturing, pharma, or medical-device operations, ISO/IEC 42001 is your bridge between innovation and defensible compliance.

“Having AI in your process isn’t enough. What matters is how you organise, govern, monitor and improve it.”

1) Where ISO/IEC 42001 Lives Across the Lifecycle

ISO/IEC 42001 applies from strategic planning through to system retirement. At the strategic level it addresses policy, organisational structure, and roles for AI governance. In the design phase it links risk and trust frameworks into AI lifecycle processes. During development and deployment it supervises how models, data pipelines, integrations, and human oversight are managed. In operation it defines monitoring, incident response, audit readiness, and performance metrics. At retirement it mandates evidence retention, decommissioning logic, and lessons-learned feedback loops. In regulated settings the AIMS framework ensures AI doesn’t slip outside your established governance, validation, or quality systems.

2) Regulatory Anchors & System Controls

ISO/IEC 42001 is a management-system standard—you can be audited or certified (or get ready for auditing) against it. It is closely aligned with standards such as ISO 9001, ISO 13485, and the EU AI Act. When regulators ask for governance, roles, risk oversight, change-control, monitoring, and accountability in AI systems, ISO/IEC 42001 gives you the checklist you need. Operators in pharma, device, and food industries will find that its structure dovetails with their existing QMS, CSV, and audit frameworks—and thus reduces organisational friction rather than adding chaos.

3) The Standard Path—From Policy to Continuous Improvement

Define policy: establish AI-governance policy and align it with corporate quality, compliance, risk, and data-governance mandates. Assign roles:Implement processes: embed the AI lifecycle into your management system—with links into change control, risk registers, model registries, quality events. Operate and monitor: track key performance indicators (KPIs), override rates, model drift, incident logs, audit findings. Audit and improve: perform internal audits, management reviews, correct nonconformities and update processes. AIMS ensures you don’t do AI as a project but as a system that repeats, scales and improves defensibly.

4) Governance & Data Integrity First

ISO/IEC 42001 emphasises that governance and data integrity form the foundation of AI management. AIMS expects a data-governance framework that ensures data quality, lineage, traceability, and version-control. It expects a model-registry that tracks architecture, version, validation outcome, owners and retirement plans. It expects that human oversight roles, access controls, and audit logs are aligned with your 21 CFR Part 11 and Annex 11 controls. Without governance and data integrity, AIMS remains aspirational—not auditable.

5) AI Risk Integration—Linking to ISO/IEC 23894

ISO/IEC 42001 prescribes that your AI-system management intersects risk management. It expects you to integrate risk assessments from ISO/IEC 23894, define risk-controls for each AI use case, classify AI systems by criticality, and maintain a risk-register of AI systems. When AI drives critical decisions (batch release, dose changes, label print, quality exceptions), those systems must be elevated into the AIMS framework with stricter governance, monitoring and escalation. This way, AI risk does not live in a silo—it becomes part of your corporate risk ecosystem.

6) Roles & Responsibilities – Clear, Not Ambiguous

ISO/IEC 42001 highlights that ambiguous roles are a failure mode. At minimum you should define: executive sponsor (AI governance board), AI-risk owner, data-governance lead, model owner/steward, compliance reviewer, human-in-loop operator, and decommissioning owner. Every AI system must map to one or more of those roles—and each role must have documented responsibilities. Model endpoints may change overnight; if no one owns them, they drift out of control. AIMS demands clear ownership, documented sign-off, and visible accountability chain.

7) Monitoring & Performance – Metrics That Matter

AIMS requires you to monitor—not just produce AI. Key metrics include: number of AI systems in production by risk category, percentage of systems with current risk/trust assessments, model‐drift events, override rates, incident counts attributable to AI, audit‐finding clearance times for AI systems, user training/competency rates, and vendor/model change frequency. Without metrics, your management system cannot prove it’s working. Dashboard visibility and periodic review are critical for demonstrating oversight, stability and improvement.

8) Change Control & Model Lifecycle Management

ISO/IEC 42001 treats AI models as assets with lifecycle stages: concept, development, deployment, operation, review, retirement. Your AIMS must define change-control processes, versioning, model registry transitions, decommissioning criteria, and handover of retired models—so that each AI system retains traceability, accountability and audit readiness throughout its alive-and-sunset phases. If models are treated like code snippets and left alone after launch, your management system does not meet the standard.

9) Continuous Improvement & Governance Loop

AIMS expects that you don’t just “build once” and forget. Your management system must include internal audits, management review, corrective/preventive action (CAPA), lessons-learned, and updates to processes and roles. Model performance, user feedback, incident reports, and external benchmark changes feed governance meetings that decide on updates, retirements, or scaling strategies. In regulated manufacturing, this is the mechanism that links AI deployments back into your existing QMS and regulatory-compliance ecosystem.

10) Supplier & Third-Party Model Management

ISO/IEC 42001 recognises that many AI systems will be built or supplied by third parties. Your AIMS must include onboarding controls for vendor models: supplier risk assessment, transparency of model behaviour, change-notification obligations, performance metrics, decommissioning rights, and conditions for fallback. You must treat vendor-AI the same as in-house AI—ownership, monitoring, escalation, and retirement. If your supplier model is “black-box” and uncontrolled, your management system fails the standard’s intent.

11) Documentation, Evidence & Audit Trail

AIMS demands documented policies, roles, model registries, risk assessments, verification/validation plans, monitoring logs, incident reports, performance dashboards, training records, and retirement summaries. Audit-trail readiness means you can reconstruct how a decision was influenced by an AI model—including version, date, data used, user override, and final outcome. If you rely solely on spreadsheets or ad-hoc logs, you lack the structured evidence that ISO/IEC 42001 requires.

12) Metrics & Key Performance Indicators (KPIs)

Your AIMS KPIs must show you are running AI as a system, not an experiment. Measure how many AI use cases are in scope, how many are classified high-risk, how many have trust-assessments, how many had incidents in the last year, how many models were retired or changed, what the average time to close a non-conformity was, what the training completion rate is for roles with AI responsibilities, and what the vendor-model performance trends show. If you can’t report a handful of credible numbers to management each quarter, your management system isn’t mature.

13) Validation & System Readiness

Where AI systems integrate with regulated operations (MES, LIMS, eBR/eMMR, manufacturing execution), ISO/IEC 42001 expects readiness evidence: system lifecycle (data pipelines, models, integrations) must be validated or qualified consistent with your CSV or IQ/OQ/PQ practices. AI governance, data integrity, version control, monitoring, and audit trails must interface with your validation artifacts, change-logs, and periodic review schedules. Without this alignment, audit questions will outpace your answers and your AI will remain a liability—not an enabler.

14) How ISO/IEC 42001 Fits Operationally Across Systems

Governance (Enterprise & AI Steering): AIMS becomes the charter of your AI governance board: policy approval, risk-tier definition, model portfolio oversight, and quarterly reviews. All AI decisions route through this structure. Execution (MES/WMS/LIMS): Each AI-enabled module is classified under AIMS, receives an owner, risk tier, monitoring plan, and version control. Quality (QMS & Risk): AI workflows appear in your risk registers, audit programs, and CAPA systems; misbehaviour triggers the same processes as any other non-conforming event. Vendor & IT Platforms: Cloud, on-prem, hybrid models all subject to AIMS controls: supplier onboarding, performance monitoring, change control, retirement. This is how regulated operations scale AI without losing control.

15) FAQ

Q1. Can we be certified to ISO/IEC 42001 today?
Yes—ISO/IEC 42001 is a management-system standard and certification bodies are starting to offer accreditation. However, many organisations begin by establishing alignment and using internal audits and gap-analyses before formal certification.

Q2. How does ISO/IEC 42001 relate to ISO/IEC 23894 and TR 24028?
ISO/IEC 42001 defines the management-system and governance framework for AI (the “how”). ISO/IEC 23894 covers AI risk management (the “what we must manage”), while ISO/IEC TR 24028 covers AI trustworthiness properties (the “what it means to be trustworthy”). Together they form a comprehensive stack for regulated AI deployment.

Q3. Do we need a separate AIMS if we already have ISO 9001 or ISO 13485?
Yes—while your existing QMS covers quality processes broadly, AI introduces new dimensions: model governance, data pipelines, monitoring of drift/performance, and vendor-model management. ISO/IEC 42001 extends your management-system into the AI domain, reusing the governance model but adding the specific controls required for AI systems.

Q4. Who should lead an AI Management System implementation?
Implementation should be led by a cross-functional team: senior leadership (governance), quality/risk (process integration), IT/data (platforms), operations (execution), regulatory/compliance (audit readiness). The AI-risk owner should own the framework and be part of your leadership structure—not siloed in innovation or R&D.

Q5. What’s the first step for regulated companies adopting ISO/IEC 42001?
Conduct a gap-analysis: inventory all AI and AI-adjacent systems, classify by risk tier, map roles/owners, check whether each has a model registry, risk assessment, human-in-loop plan, monitoring metrics, incident escalation, and retirement logic. Then build your AIMS roadmap—governance, policies, piloting high-risk systems, metrics dashboards, and integrate with your QMS and validation frameworks.

Related Reading
• AI Risk & Trust: ISO/IEC 23894 | ISO/IEC TR 24028 | GxP
• Quality & Systems: ISO 9001 | ISO 13485 | CSV | VMP
• Execution & Records: MES | eBR | eMMR | Deviation/NCR | CAPA