What Is AI Trust?
AI trust is the demonstrable confidence that stakeholders — customers, regulators, employees, and boards — place in an organization's use of AI. It is the outcome that governance is meant to produce.
Trust as an Outcome
Trust is what stakeholders extend when they believe an organization will use AI competently, safely, and accountably. It must be earned through evidence — not assumed through brand or asserted through marketing.
The Three Audiences for AI Trust
Customers want to understand how AI affects them. Regulators want to see controls and evidence. Boards want a defensible view of risk and oversight. A trust posture must speak to all three.
- Customers: explain how AI is used in decisions that affect them
- Regulators: demonstrate controls, evidence, and oversight
- Boards: provide a defensible view of risk and accountability
How Trust Is Measured
The AI Trust Index™ is one framework for measuring trust across ten domains. Internal metrics — inventory coverage, classification completeness, oversight cadence, incident frequency — provide the operational view.
Why It Matters
Trust is becoming a precondition for AI adoption — by customers, by procurement teams, and by regulators. Organizations that can evidence trust will deploy more AI, faster, in more environments.
"Governance is the work. Trust is the result. Both must be visible to be valuable."
- Trust is the outcome that governance is meant to produce.
- Three audiences: customers, regulators, boards.
- Trust must be measured, not asserted.
- Evidenced trust is a precondition for sustained AI adoption.