Proven Use Cases for Regulated Industries

The problem
A high volume of routine credit card requests arrived by email daily. Each followed a predictable pattern but was handled entirely by hand, consuming significant analyst time and creating inconsistency in how requests were resolved.

The approach
Generative AI was used to classify incoming requests by type, generate standard responses and attach relevant documentation. Non-standard cases were routed directly to a human for review before any response issued.

Governance note
Every request is logged for audit. No response issues without human sign-off on exception cases. The analyst remains accountable for every outcome the process produces.

Manufacturing

Laboratory Quality Control

The problem
A mandatory second-analyst verification step on laboratory reports was repetitive and time-consuming, yet essential for catching reporting errors before sign-off. The volume made consistent manual checking increasingly difficult to sustain.

The approach
A model trained on historical correct and incorrect reports was introduced as a second verification layer, flagging anomalies for human review. It did not replace the verification step. It made the step more reliable.

Governance note
The final call remains with the qualified analyst. The model reduces the cognitive load of repetitive checking without removing human accountability from the process. Documentation supports audit at every stage.

The problem
Manual minute taking and the review of long policy documents and legislation was absorbing significant administrative time, with no consistency in output quality across the team.

The approach
Copilot was used to produce first-draft minutes from meeting transcripts and summaries of lengthy policy documents. All outputs were reviewed and approved by a named individual before circulation.

Governance note
No minutes or policy summaries are distributed without human review. The AI produces a working draft. The accountable person owns what goes out.

Further and Higher Education

Assessment Design

The problem
Authoring levelled revision questions and multiple-choice assessments by hand was taking substantial tutor time. The volume required was difficult to sustain alongside direct teaching and learner support responsibilities.

The approach
GenAI was used to produce levelled multiple-choice items from curriculum materials and source papers. Every item was reviewed by the tutor before it was used in any assessment context.

Governance note
No AI-generated item was used without tutor review and sign-off. Academic standards and assessment integrity remained the tutor's responsibility at every point.