Business / Product Leader
Accountable owner of each AI use case — and its failures.
You are accountable for
- Shipping AI value with a risk profile the enterprise can carry
- Clear rules: what needs review, what is pre-approved
- Customer trust in AI-touched journeys
What keeps you up at night
- Being the named owner of the use case in the incident report
- A six-week review queue killing a two-week opportunity
- Quality decay after launch nobody was measuring (the Klarna lesson)
Decisions you own
- Own the use case in the registry: purpose, tier, human-oversight design
- Define outcome-quality metrics beyond deflection and cost
- Decide where humans sit in the loop — and make review real work, not a rubber stamp
Your layers of the stack
4Application GovernanceDoes the system behave — and can we prove it?7People GovernanceWhat are our people doing with AI today?1Enterprise GovernanceWho is accountable for AI — and for which AI?
Where to go first
Questions you should be asking
- What may this AI never do, and where is that enforced?
- What does good look like in production, and who watches it weekly?
- When the AI is wrong, what does the customer experience?