AIAC-02 · Industrial track
AI Risk & Quality — Industrial Systems
For engineers and quality professionals deploying AI in robotics, manufacturing, and industrial automation, where system failures carry physical and operational consequences.
- Reference
- AIAC-02
- Level
- Practitioner
- Examination
- 3.5 hours
- Delivery
- Remote, proctored
- Credential
- Valid 3 years
§ 1 — Intended candidates
Who this certification is for
Quality engineers, controls and automation engineers, functional safety practitioners, and operations leaders introducing AI into physical systems.
- Prerequisites
- Recommended: two or more years in engineering, quality, or operations in an industrial setting. The Foundation track is recommended but not required.
- Status
- In development · First cohort October 2026
§ 2 — Examination domains
What the examination covers
Domain weightings are published with the full syllabus ahead of the first cohort.
- 01
AI in physical systems
Where machine learning enters industrial control, inspection, and robotics, and how that changes the failure surface.
- 02
Safety-adjacent risk
The relationship between AI assurance and existing functional safety and machinery practice — including where AI methods do not yet substitute for them.
- 03
Quality systems integration
Fitting AI risk controls into established quality management processes rather than running them alongside.
- 04
Data and drift in production
Sensor drift, environment change, and the monitoring evidence needed to show a deployed model still performs as qualified.
- 05
Supplier and component assurance
Assessing vendor-supplied models and embedded AI components where internal visibility is limited.
§ 3 — Assessment
How candidates are assessed
Scenario-based examination set in an industrial deployment context, including a risk profile, a monitoring plan, and a qualification evidence package.
Successful candidates receive a credential with a unique identifier that any employer can check against the public record. See credential verification.
Curriculum mapping indicates alignment of learning content with published frameworks. It does not constitute endorsement by, or certification under, any standards organization or regulator.
§ 4 — Further reading
Background reading
- What is AI assurance? A practitioner's definition
AI assurance is the practice of producing credible, independently reviewable evidence that an AI system works as intended. A working definition, and how it differs from governance and compliance.