VP Design — AI & Trust

What you can expect

• Trust is designed as a system — states, controls, recoverability, and auditability — not cosmetic reassurance.
• AI governance becomes usable — human-in-the-loop, autonomy gradients, explanation cues, and incident-ready flows.
• Decision velocity without quality loss — clear decision rights, critique rituals, and lightweight quality gates.
• Risk-aware UX in regulated contexts — privacy/security constraints translated into shippable patterns and states.
• Scale coherence across teams — pattern governance that prevents drift across squads and vendors.

What I do in the first 30 / 60 / 90 days

• Map decision rights for AI-enabled UX, including risk acceptance and escalation paths.
• Audit critical journeys for state coverage: failure, recovery, overrides, and evidence.
• Define trust metrics: drop-offs, dispute/support debt, incident classes, and leading indicators.

• Ship first improvements in the highest-risk flows (state-first), with measurable outcomes.
• Implement quality gates for AI features (explainability, override, audit trail UX).
• Establish pattern ownership and exception rules to reduce fragmentation.

• Scale the governance loop across teams (cadence, decision artefacts, pattern evolution rules).
• Stabilize incident-ready operating model: monitoring signals + response playbooks.
• Institutionalize trust as a product KPI, not a post-launch concern.

Evidence links

• Design Governance OS
• Proof of work (search)
• Banking case studies