Business leaders often assume their teams are independently adopting AI. In reality, employees are hesitant to admit they don't know how to use it effectively and are waiting for formal training and a clear strategy. The responsibility falls on leadership to initiate AI education.

Many employees secretly use AI for huge efficiency gains. To harness this, leaders must create programs that reward sharing these methods, rather than making workers fear punishment or layoffs. This allows innovative, bottom-up AI usage to be scaled across the organization.

The best test of knowledge is the ability to teach it. By having employees explain a new AI tool or workflow to their peers, they are forced to solidify their own understanding and identify knowledge gaps. This process turns passive learning into active expertise.

To drive genuine AI transformation, leaders cannot just delegate. Zapier's executive team holds "AI show and tell" sessions where each member presents their own hands-on AI use cases. This demonstrates commitment, builds practical knowledge of AI's limits, and ensures leadership's vision is authentic.

Instead of explaining AI's potential, show it. Identify the most magical, jaw-dropping internal application of an AI tool and demo it live for your leadership team. This visceral experience is far more effective at driving organizational change than any presentation.

To get teams to embrace AI, leaders should ditch generic mandates like "use more AI." Instead, focus on specific business transformations and highlight the customer value they create. Using company-wide forums for "show and tell" sessions where teams demonstrate unarguable successes makes adoption organic and outcome-driven, not a top-down chore.

Leadership often imposes AI automation on processes without understanding the nuances. The employees executing daily tasks are best positioned to identify high-impact opportunities. A bottom-up approach ensures AI solves real problems and delivers meaningful impact, avoiding top-down miscalculations.

Instead of monitoring private AI chats to ensure best practices, leaders should focus on providing the right inputs. Create centralized, AI-ready artifacts like customer research, business strategy, and outcome documents. This ensures teams connect their AI-accelerated work to the correct context, allowing leaders to monitor outcomes, not activity.

To combat CEO "AI psychosis," operations teams should be vocal about their AI projects. By publicly sharing wins while also detailing the data cleanup, process building, and integrations required, they can build leadership confidence and educate them on the real effort involved.