Why our leadership isn’t ready for AI (Part 7)

Trust is a crucial factor in the successful implementation of artificial intelligence (AI), as it influences whether people effectively use AI or merely use it to look busy. Trust plays a significant role in people's willingness to engage with AI honestly and responsibly. When people trust AI, they believe it won't be used against them, that admitting AI assistance won't lower their work's perceived value, and that leaders will openly discuss both successful and unsuccessful AI applications.

This trust enables individuals to exercise judgment when overriding AI suggestions and fosters a culture of trust and open communication within the organization.

Low trust, on the other hand, can lead to disengagement or people resorting to "performing" AI usage rather than embracing it genuinely. When trust-related signals are missing, people may engage minimally with AI tools, using them just enough to show up favorably on dashboards while relying on their regular methods for the actual work. These trust-related signals are early indicators of the success of an AI rollout, as they often appear before the return on investment becomes evident.

Organizations frequently overlook trust as a critical factor in AI adoption, focusing instead on efficiency metrics such as adoption rates, usage frequency, and cost savings. Many AI scorecards do not report whether people feel safe to express their opinions or concerns about AI usage. As a result, organizations may experience a plateau in adoption or find that employees use AI for performative purposes, ultimately failing to create lasting change.

Successful AI implementations prioritize trust by evaluating whether individuals feel safe to challenge AI outputs, admit uncertainty, and trust that increased productivity won't lead to increased workload. Building trust is an iterative process that involves responding to concerns with genuine curiosity, acknowledging the value of AI-assisted work, and treating AI-related mistakes as opportunities for process improvement rather than personal blame.

These trust-building moments, though not reflected in efficiency reports, are vital for ensuring long-term success and sustainable AI adoption.