Business leaders love certainty. They want to know how things work, why they work, and what might happen when they don’t. It’s no surprise that when AI entered the boardroom, transparency became the rallying cry. Explain the model. Show the logic. Reveal the black box.

Yet in practice, trust rarely behaves the way the textbooks promise.

Most people don’t trust systems because they understand them. They trust them because they behave consistently.

A car’s braking system is opaque to 99% of drivers, yet people trust it every day. Your smartphone is a maze of circuitry and code, yet you hand it your banking details without hesitation.

Trust is an experience

Trust, for most of us, is an experience — not a technical insight. And that’s where AI introduces a tricky paradox. When companies show customers how AI actually works, trust doesn’t always increase. Sometimes it erodes. 

The logic behind modern models — probabilities, embeddings, non-linear decision pathways — can feel alien and arbitrary. The more you reveal, the more the system can look unpredictable, even when it isn’t.

Meanwhile, the technologies we trust the most are often the ones we understand the least. Apple built an empire on black boxes wrapped in polished simplicity. Users don’t trust Apple because they understand it. 

They trust it because it behaves predictably and protects them from complexity.

This puts leaders in a difficult position

How transparent is transparent enough? How much detail builds trust — and how much destroys it? When is “showing your work” responsible, and when does it simply confuse the people you’re trying to reassure?

The tension becomes even sharper in regulated industries.

Banks, insurers, and healthcare organizations don’t have the luxury of mystery. They must demonstrate fairness, explainability, and auditability. But even if the regulator is satisfied, the customer might not be. There is a gap between regulatory trust and human trust, and AI now lives in the middle of that gap.

Perhaps the conversation isn’t really about transparency at all.

Perhaps it’s about predictability

A perfectly explainable AI that behaves inconsistently will never earn trust. A highly opaque AI that behaves reliably just might. Customers don’t wake up wanting explainability reports. They wake up wanting systems that don’t surprise them.

This doesn’t mean companies should hide their models. But it does suggest that trust in AI may come less from technical transparency and more from clarity about boundaries, reversibility, guardrails, and intent.

Tell people what the AI can do. Tell them what it won’t do. Tell them how they can stay in control.

Trust is built long before someone asks how the model works.

In the end, the trust tradeoff is really a leadership dilemma. It challenges our instinct to equate transparency with virtue, and invites us to consider a more uncomfortable truth: in the age of AI, trust may depend less on how much we reveal and more on how reliably we deliver.

Or?

Joachim Cronquist is a strategic AI advisor and founder of Cronquist AI. He helps business leaders turn AI into business clarity and measurable results.