Business Problem First
A specific business problem should always be defined before selecting AI technology. Deploying tools without a clear operational goal often leads to confusion rather than efficiency.
Data Quality & Structure
AI models require reliable inputs. We examine:
- Completeness and consistency of records
- Freshness and duplication issues
- The balance between structured (databases) and unstructured (documents) information
Access Permissions & Privacy
AI workflows must respect existing information access rules. If a model can read all documents, it may expose sensitive data to unauthorised users. We carefully consider the implications regarding personal and sensitive information in relation to the UK GDPR and Data Protection Act 2018.
Hallucination and Quality Control
AI systems can produce incorrect or misleading outputs. Therefore, we plan for:
- Source verification mechanisms
- Clear escalation paths when confidence is low
Crucial Requirement: Human Review
We clearly state that AI outputs require appropriate human review. Automation should assist, not replace, final operational judgement.
Use-Case Prioritisation Framework
Ideas should be evaluated case by case. A helpful evaluation considers:
Pilot Planning
A limited pilot may help evaluate usefulness and workflow fit before wider rollout. This allows teams to test output quality and operational risks safely.
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