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

Efficiency is not the only factor in successful AI adoption. Trust plays an equally crucial role, as it impacts whether people genuinely use AI effectively or merely use it to appear productive. People engage honestly with AI when they believe it won't be used against them, when admitting its use won't diminish the value of their work, and when leaders demonstrate transparency in their own AI usage.

Additionally, people trust to make judgments about overriding AI suggestions and not having to justify each deviation. When these aspects are missing, people disengage or manipulate metrics. If usage is tracked but trust is low, individuals engage in "performative" AI use, generating content only to meet metrics while relying on traditional methods for actual work.

These trust-related signals appear early and signal whether an AI rollout will endure. Contrary to common reporting, adoption-related signals are not shown on dashboards such as adoption rates, usage frequency, or cost savings. Organizations often become surprised when adoption plateaus or when half of AI usage reported was merely performative.

The underlying reasons are present but unmeasured due to lack of a method to quantify trust. Successful AI adoption requires attention to trust, including whether people feel safe disagreeing with AI outputs and admitting uncertainties. Sustainable adoption depends on answers to questions about trust. Trust is a leading indicator that emerges from small, specific moments, such as leaders responding genuinely to concerns about AI mistakes or admitting reliance on AI without negative judgment.

These moments determine the longevity of efficiency gains rather than being reflected in efficiency reports. The ultimate measure of successful AI adoption lies in whether people trust the environment to use AI honestly, question it when needed, and exercise their own judgment when crucial.