Implementation service
Establish structured governance for the responsible development, provision and use of AI.
The business problem
AI adoption inside most organizations has outpaced the governance around it. Models and third-party AI services are procured by individual teams, embedded into customer-facing processes, and only later surface as a question from a customer, a regulator or the board - by which point nobody can say with confidence what AI is in use, who owns it, what data it touches, or what happens when it behaves unexpectedly.
Responsible AI principles do not solve this. Principles describe intent; a management system describes who decides, on what basis, with what record. ISO/IEC 42001:2023 provides that structure, and FaizZab implements it as working governance rather than a statement of values.
Service scope
The AI Management System is built to cover AI you develop, AI you provide to others, and AI you consume from third parties - three different risk profiles that most governance efforts collapse into one.
Establish the organizational context for AI, the interested parties whose expectations apply, and a scope boundary that reflects the roles the organization actually plays - developer, provider, deployer or user of AI systems.
Stand up the decision-making body that approves AI use cases, sets tolerance, and resolves escalations, with defined membership, authority and meeting cadence rather than an informal review by whoever is available.
Write an AI policy that states what the organization will and will not do with AI, and set measurable AI objectives connected to business and risk outcomes rather than aspirational language.
Assign accountability for each AI system across its lifecycle - business owner, technical owner, data owner and risk reviewer - so that responsibility does not evaporate between the team that built it and the team that runs it.
Build and maintain a register of AI systems in use covering purpose, data sources, model or vendor, deployment context, affected parties and criticality. Without this, no other AI control can be applied consistently.
Define how AI-specific risk is identified and evaluated - including performance drift, bias, explainability limits, misuse, data quality and dependency on third-party models - with criteria and thresholds that produce comparable results.
Implement a repeatable impact assessment covering effects on individuals, groups and society, applied at a defined trigger point in the lifecycle so it informs decisions rather than documenting them afterwards.
Embed controls across the lifecycle - data sourcing and quality, design, testing and validation, release approval, monitoring, change management and retirement - with gates that a system must pass before it advances.
Extend governance to procured AI: due diligence on model providers, contractual expectations, data handling terms, evaluation before deployment, and ongoing oversight of vendor model changes you do not control.
Establish how AI use is disclosed, how affected parties raise concerns or contest outcomes, and how those channels are resourced and evidenced.
Define what is measured after deployment - accuracy, drift, incidents, complaints, human-override rates - along with thresholds and the escalation path when a system moves outside expected behaviour.
Run internal audit, management review and corrective action over the AIMS so AI governance improves with experience instead of ossifying at first implementation.
Methodology
Find AI actually in use across product, operations and back office - including embedded vendor AI that was never treated as an AI decision.
Stand up the AI governance body, policy and accountability model so subsequent decisions have somewhere to land.
Build the AI risk methodology and impact assessment framework, and validate them on live use cases rather than hypothetical ones.
Insert control gates into the existing development, procurement and change processes so governance operates where work happens.
Run the monitoring, review and improvement cycle, and build the record set that demonstrates the AIMS is functioning.
What you receive
Outcome
Move AI governance from principles into operating practice.
Commercial value
Buyers and supervisors increasingly ask how AI is governed. A functioning management system answers that with records instead of assurances.
Clear gates and tolerances let teams deploy AI faster, because the answer to "can we use this?" stops being a case-by-case negotiation.
Most AI risk now enters through vendors. Governing procured AI is what separates a real AIMS from an internal-model-only exercise.
Questions
Yes. The standard addresses organizations that develop, provide or use AI systems. Organizations that consume AI still make decisions about deployment context, data exposure and affected parties, and those decisions need governance.
Yes, and it usually should be. Governance forums, risk methodology, internal audit, management review and corrective action can operate as a single integrated management system rather than two parallel structures.
The AIMS provides the governance structure that regulatory obligations can be mapped onto. Interpretation of any specific legal obligation should be validated with qualified legal counsel.
Related
An independent evaluation of AI governance maturity against ISO/IEC 42001:2023 - AIMS boundary, use-case inventory quality, AI risk and impact evidence, accountability effectiveness, third-party oversight and prioritized certification gaps.
Explore this serviceEnd-to-end implementation of an ISO/IEC 27001:2022 Information Security Management System - governance, risk methodology, Statement of Applicability, controls, evidence and the internal audit and management review cycle that keeps it alive.
Explore this serviceDesign and operation of a practical risk management process - identification, assessment, treatment, ownership, monitoring and reporting - connected to the controls and decisions it is meant to influence.
Explore this serviceDesign and operation of third-party risk management - supplier due diligence, criticality tiering, risk assessment, contractual controls, ongoing monitoring and periodic review.
Explore this serviceImportant
Implementation support does not guarantee certification. Certification against ISO/IEC 42001 is issued by independent certification bodies. FaizZab is not a certification body and does not certify AI management systems.
Tell us your obligation, your timeline and where you are today. We will confirm whether this is the right engagement for you.