Workshop contents
Session 1 · Layer 5
Guardrails and Human Authority
What it may decide, and who signs
AI capability is no longer the constraint. The question is now: under what conditions, with what evidence, and with whose authority.
Step 5.1 — What could go wrong?
| Scenario | What went wrong |
|---|---|
| AI recommends cancelling all First Class shipping | No cost or contract constraint applied, so it breaks customer commitments |
| AI sends the alert to the wrong executive | No access control, so information leaks |
| AI bases a recommendation on corrupted data | No data validation, so garbage in, garbage out |
| AI recommendation violates a vendor contract | No legal or compliance context, so liability |
| AI optimises for speed and ignores carbon | No sustainability guardrail, so misaligned KPIs |
Facilitator asks the room
Which of these has already happened in your organisation — or could happen tomorrow?
Step 5.2 — The governance layer
Guardrails for supply chain AIWorkshop ScenarioClick the text, then ⌘C or Ctrl+C
DECISION AUTHORITY: - AI MAY: Generate reports, send alerts, draft recommendations - AI MAY (with approval): Adjust shipping mode allocation up to 10% - AI MAY NOT: Change carrier contracts, override SLA commitments, communicate to external partners DATA RULES: - Must use data less than 24 hours old - Must flag if any data source is missing or incomplete - Must show confidence level for predictions EVIDENCE REQUIREMENTS: - Every recommendation must cite specific data points - Must show "what if" comparison (current vs recommended) - Must include risk assessment for each action ESCALATION: - If late delivery rate exceeds 60%: escalate to COO immediately - If recommended action exceeds budget by >5%: requires CFO approval - If pattern is unprecedented: flag for human review, do not auto-act AUDIT: - Log every decision, recommendation, and data source - Weekly governance review of all AI-generated actions
Lesson. Guardrails define what AI may do, under what conditions, and with whose authority. Without them, no AI deployment is trustworthy enough to operate.
Step 5.3 — Executive self-assessment: the AI Readiness Rubric
Each participant evaluates one of their own AI initiatives against six dimensions.
| Dimension | Level 1 · Ad hoc | Level 2 · Structured | Level 3 · Enterprise |
|---|---|---|---|
| Instructions | Vague, generic prompts | Specific, formatted, role-based | Constraints, reasoning direction, output standards |
| Context | None, general knowledge only | Dataset provided | Business rules, history, KPIs, constraints |
| Tools | Chat only | Data analysis | Multi-tool workflow: query, visualise, draft, send |
| Operation | Human runs every request | Scheduled tasks | Agent monitors and acts within defined authority |
| Governance | None | Basic access control | Decision authority, evidence rules, escalation, audit |
| Evidence | “AI said so” | Data-backed | Cited, compared, confidence-scored, peer-reviewed |
Facilitator exercise, 3 minutes. Pick one AI initiative you are responsible for. Where does it sit on this rubric today? Where does it need to be in six months — and what is the first gap to close?
How to read the figures on this page
Source Dataread straight from the DataCo dataset
Derived Evidencecalculated from those values
Workshop Scenarioinvented for the exercise; not in the dataset