ILLUSTRATIVE DELIVERY SCENARIOS

See what practical AI governance work can look like.

These scenarios demonstrate scope, methods and deliverables. They are illustrative examples, not claims about named or confidential clients, completed engagements or guaranteed results.

Public sector · Illustrative

Municipal AI inventory and governance baseline

Starting point: Different departments use embedded AI, public generative tools and supplier features without a shared register or approval path.
Work: Interview service owners, map use cases, classify priority risk, review procurement controls and define governance roles.
Deliverables: AI register, ownership matrix, classification model, interim acceptable-use rules, supplier questions and 90-day roadmap.
Expected value: Leadership gains visibility, high-priority uses receive deeper review and employees know where to ask for approval.

Health & social services · Illustrative

Copilot risk and human-oversight review

Starting point: A team wants to use a copilot for summaries and drafting in a context involving sensitive information and professional judgement.
Work: Review data flow, access, retention, supplier terms, output verification, user training, fallback and prohibited uses.
Deliverables: Impact assessment, approved-use boundaries, human-review checklist, test plan, incident path and pilot decision memo.
Expected value: A limited pilot can proceed with clearer safeguards, defined accountability and evidence for the next decision.

SME · Illustrative

Secure generative AI adoption programme

Starting point: Employees already use several AI tools, but accounts, sensitive-data rules, supplier choices and output checks are inconsistent.
Work: Discover current use, approve a smaller toolset, define access and data rules, train roles and establish periodic review.
Deliverables: Tool register, policy, vendor reviews, role-based training, champion network, metrics and management reporting template.
Expected value: Useful adoption continues with fewer uncontrolled tools, clearer behaviour and a repeatable process for new requests.

Why the examples are labelled: Trust requires precise claims. SundAI separates illustrative scenarios from verified client work and will only publish named outcomes with appropriate permission and evidence.

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