# AI-enabled roles: Board discussion guide

## Core thesis
AI adoption without work redesign will not just create inefficiency. It can quietly damage the organisation's capability pipeline.

## Who this is for
For chief executives, CIOs, CHROs, board members and transformation leaders who need to turn AI activity into accountable workforce and operating model change.

## The issue in one sentence
If AI removes the junior work, organisations need a new way to develop senior judgement.

## Five board questions
1. Which tasks are we automating, augmenting or removing?
2. Which roles currently own AI-enabled work?
3. What capability are people losing if those tasks disappear?
4. Who is accountable for AI quality, risk and escalation?
5. What will we measure beyond productivity?

## Warning signs
- AI use is spreading through individual initiative rather than accountable ownership.
- Junior staff are skipping the work they used to learn from.
- Teams are creating prompts, bots and workflows without shared quality standards.
- Productivity gains are visible in pockets but not scaling across functions.
- Leaders are measuring time saved but not quality, risk, trust or capability transfer.

## Two-week diagnostic outputs
A useful AI work exposure diagnostic should produce:

- Work exposure map
- Capability risk heatmap
- Role redesign priorities
- Governance gap summary
- 90-day action roadmap

## Minimum viable operating model
1. Work exposure: which tasks, decisions and workflows are changing?
2. Capability risk: which work teaches judgement and should not simply disappear?
3. Role redesign: which new responsibilities need formal owners?
4. Governance: who approves, monitors, escalates and retires AI use?
5. Measurement: how do we track productivity, quality, risk, trust and capability transfer?

## First executive action
Do not start with another AI tool rollout. Start by mapping exposed work across three high-impact functions, then identify where capability, governance and role ownership need to change first.