To make people believe in your AI, start with assurance mechanisms that actually work

For AI to deliver on its potential users must believe in it. But trust in AI is fragile. One hallucinated fact, one biased recommendation, and confidence evaporates.

So what builds durable trust in AI?

New data from the 2025 KPMG study "Trust, attitudes and use of artificial intelligence" reveals a compelling answer: formal assurance mechanisms embedded at the organizational level. In this global survey, 84% of respondents said they would be more willing to trust an AI system if it allows for human intervention to correct, override, or challenge recommendations and output.

That sounds like a mandate.

The anatomy of AI trust

Trust in AI is layered. It's shaped by visible performance (Is the answer correct?), but also by invisible guarantees (Was the answer generated ethically? Is the system reliable under stress?).

Organizations deploying AI must now think like regulated industries. Trust doesn’t come from a single line of code, it comes from the architecture around it.

The assurance mechanisms that matter most include:

• Monitoring system reliability

• Human oversight and accountability

• Responsible AI policies and employee training

• Compliance with international AI standards

• Independent third-party audits
  

AI assurance starts inside the organization

The most effective trust mechanisms are not just external regulations. They're internal commitments. When organizations monitor their own AI reliability, provide human oversight, and implement formal policies for ethical use, they signal seriousness.

The public responds to this. According to the KPMG survey, in markets from Japan (69%) to Nigeria (89%) people say these measures increase their trust. That range spans cultures, industries, and AI maturity levels.

Signaling trust to end users

Not all assurance mechanisms are equal in visibility. An international standard or policy may be invisible to end-users, while human oversight is more immediate. That’s why hybrid models combining internal safeguards with external transparency are emerging as the gold standard.

If your AI system flags legal issues but clearly states “reviewed by a licensed attorney,” the assurance is not abstract, it’s personal. It says: You can trust this answer, because a qualified human stands behind it.

This is exactly what platforms like Pearl AI are doing with Expert verification. When a real, credentialed professional validates an AI-generated answer, users not only trust the output, they trust the process that produced it.

Responsible AI isn’t a vibe. It’s a set of measurable behaviors.

• Are your AI agents connected to real Experts when stakes are high?

• Do you monitor accuracy and intervene when confidence is low?

• Is there human accountability when things go wrong?
  

If the answer is yes, then you’re building systems that deserve trust, not just hope for it.

The industry must move beyond risk disclaimers and into verifiable assurance. AI systems that cannot explain their decisions, trace their outputs, or offer a path to human validation will be left behind. Not because they can’t perform, but because they can’t be trusted.

The future of AI assurance is hybrid

Human + machine is not just a collaboration model. It’s an assurance architecture.

AI alone is fast but fallible. Humans alone are reliable but slow. Together, they create a model where trust is scalable. That’s why 80% of people say they trust AI more when human Experts are in the loop. And it’s why organizations that adopt hybrid assurance will lead the next wave of responsible AI adoption.

We should embrace this future, not fight it.

Final thought

AI trust won’t be won through better branding or shinier UX. It will be won through rigorous, accountable, transparent systems that stand up to scrutiny.

Organizations that want users to trust their AI must do more than promise integrity. They must prove it with assurance mechanisms that are visible, verifiable, and human-backed.

In AI, the most powerful signal of trust isn't the output.

It's the infrastructure that got you there.