Readiness assessment
Evaluate whether your AI governance can withstand management-system and assurance scrutiny.
The business problem
Organizations that have written an AI policy often assume AI governance is handled. The assessment usually shows otherwise: the inventory is incomplete, impact assessments were produced once for a flagship model and never for the twenty vendor tools in daily use, and nobody can produce evidence that a deployed system is still performing within tolerance.
This assessment establishes, from evidence, whether your AI governance would hold up under a management-system audit, a customer assurance review or a regulatory enquiry - and where it would not.
Service scope
Each area is assessed against records, live systems and interviews with the people who build, buy and operate AI - not against the policy document alone.
Evaluate whether AI decisions are made by an accountable body with real authority, on documented criteria, with recorded outcomes - or whether governance exists only as a policy nobody consults.
Test whether the declared AI management system boundary covers the AI the organization develops, provides and consumes, including AI embedded inside procured platforms.
Sample business functions to find AI in use that is missing from the inventory, and assess whether recorded entries carry enough detail - purpose, data, owner, criticality - to be usable for risk decisions.
Determine whether AI risk assessments exist for material systems, whether they address AI-specific failure modes rather than generic IT risk, and whether resulting decisions were recorded.
Review completed impact assessments for depth, timing and consequence: were they performed before deployment, did they consider affected individuals and groups, and did they change any decision?
Test whether named owners understand and exercise their responsibilities, particularly at the handover between build teams and operational teams where AI accountability commonly lapses.
Assess due diligence, contractual terms, pre-deployment evaluation and ongoing monitoring for procured AI, including how the organization learns about vendor model changes.
Evaluate whether AI use is disclosed where it should be, and whether channels for concerns, contest or human review exist, are reachable and are actually resourced.
Request post-deployment monitoring records for live AI systems and assess whether performance, drift and incidents are measured against defined thresholds - or not measured at all.
Rank every gap by the barrier it presents to certification and by AI risk exposure, separating what must be fixed first from what can follow.
Methodology
Analyse the AI policy, governance records, inventory and completed assessments before engaging teams.
Interview business functions to surface unregistered AI, including embedded vendor capability treated as ordinary software.
Select material AI systems and trace them end to end from approval through deployment to current monitoring.
Request the records that should exist for each sampled system and assess whether they can be produced and whether they demonstrate control.
Produce a maturity rating per area, a prioritized gap register and a sequenced remediation plan for leadership.
What you receive
Outcome
Identify AI governance weaknesses before formal assessment.
Commercial value
Almost every assessment discovers AI in production that governance did not know about. That discovery alone usually justifies the exercise.
Customers and supervisors are moving from asking whether you have an AI policy to asking for proof it operates. This establishes whether you can answer.
AI governance can consume unlimited effort. Prioritization by certification barrier and risk exposure keeps spend proportionate.
Questions
If there is genuinely no governance in place, implementation is usually the better entry point. The readiness assessment is most valuable where AI governance already exists and its real condition is unclear.
Yes. Staff use of generative AI is treated as part of the AI estate, because it carries data exposure and output-reliance risk that governance is expected to address.
The assessment evaluates governance, risk, impact and evidence practices. Technical model validation is a separate discipline and is scoped independently where required.
Related
Implementation of an ISO/IEC 42001:2023 AI Management System - AI governance structure, system inventory, AI risk and impact assessment, responsible lifecycle controls, third-party AI oversight and performance monitoring.
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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 serviceAdvisory support to design and integrate the GRC operating model - governance framework, compliance architecture, risk and control integration, obligation management, management reporting and evidence governance.
Explore this serviceImportant
Readiness and advisory support only. FaizZab does not issue ISO/IEC 42001 certification and a readiness assessment does not guarantee a certification outcome. Legal interpretation of AI regulation should be validated with qualified legal counsel.
Tell us your obligation, your timeline and where you are today. We will confirm whether this is the right engagement for you.