AI for Quality, Audit and Compliance
Processes, use cases and architecture
Document review, evidence retrieval, potential gap detection and action-plan preparation. Versioned RAG links each suggestion to a document and requirement. Deterministic rules check required fields; auditors assess the relevance of findings.
Required data and applications
Document management, procedures, applicable frameworks, audit reports, nonconformities and actions. Retain each item’s date, version and owner.
Human accountability and limitations
Preparation time, retrieved evidence, false findings and action closure time. Failure to retrieve a document does not establish noncompliance; a generated answer is not audit evidence.
Measuring benefits
The pilot compares a baseline with the target process on representative cases. Business owners define quality thresholds and the right to revert to manual processing before launch.
Bringing the right expertise together
FAQ
How should an AI programme be measured?
Compare outcomes with a baseline: quality, end-to-end time, total cost, adoption and incidents. Measure review effort and errors, not just the number of generated answers.