Trust & Reliance

Trust is the invisible infrastructure of every AI deployment. Too much trust and people follow flawed recommendations off a cliff. Too little and they ignore genuinely useful tools, wasting the investment entirely. The challenge is that trust in AI rarely settles at the right level on its own. People tend to start with either wide-eyed faith or deep scepticism, and their trust often shifts in irrational ways - a single dramatic failure can destroy confidence in a system that's been reliable 99% of the time, while a slick interface can inspire unwarranted confidence in something fundamentally unreliable. Unlike trust between people, which builds through shared experience and social cues, trust in AI is shaped by factors most organisations never consciously design for: how outputs are presented, whether uncertainty is communicated, how errors are handled, and whether users feel they can override the system without penalty. Getting trust right isn't about making people trust AI more - it's about helping them trust it accurately, in the right situations, for the right reasons. That's a design problem, not a marketing one.