Financial servicesBoard Chair

The CEO Learns About AI from a Customer

A near-miss becomes a lightweight governance rhythm with owners, an inventory and a clear escalation path.

The scenario

A customer reports an AI-generated error before management has a complete view of the system. The chair asks for a governance routine that creates visibility without a bureaucracy that stops useful work.

How AI enters the workflow

Frame the decision
Human
The accountable leader defines the outcome, constraints and what must remain human.
Prepare evidence
AI
AI organises the relevant material, assumptions and options with sources where available.
Test the workflow
Human + AI
A small team tests the proposed workflow on representative work and records failures.
Make the call
Human
A named person approves the decision, boundaries and measure of success.
Learn and improve
Human + AI
Results, feedback and exceptions feed the next review rather than disappearing in a project report.

Ask about this workflow

ADA, the taskforce deputy, explains exactly how human and AI share the work — ask anything.

The human + agent taskforce

Team leader — approves every deliverable

SENTINEL

Governance Agent

ATLAS

Strategy Agent

PRISM

Readiness & Assessment Agent

Taylor

Orchestrator Agent

ORION

Infrastructure Agent

Agents propose and execute. The human leader always approves the final result.

Outcomes

Metric	Before	After
Time to a defensible first decision	Fragmented	Visible and repeatable
Human accountability	Implicit	Named at each hand-off
Learning signal	Anecdotal	Reviewed every cycle

Key takeaways

Start with a bounded business decision, not a technology demonstration.
Make the human owner, evidence and escalation route visible before scaling.
Treat feedback as a design input: it improves the system and the team's judgment.