Mike Lewis explains an L0–L3 framework for AI proficiency, emphasizing that employees need different levels of knowledge depending on their roles. The goal is not for everyone to become technical, but to help more people progress toward becoming capable non-technical builders who can create useful solutions within their areas of expertise. The discussion addresses resistance to AI, including fears of job displacement, concerns about quality, and frustration with poorly designed tools. Adoption works best when AI is integrated into existing workflows and presented as support rather than coercion. Durable solutions should be built by employees who understand the organization’s processes and culture, supported by less experienced users and guided by technical leaders responsible for scalability, accuracy, and compliance. The speakers also highlight the importance of documenting tacit knowledge so it can become reusable organizational capability. AI success should be measured through concrete business outcomes, not usage metrics alone. As repetitive work shifts to AI, people can focus more on orchestration, strategy, creativity, and broader results. Organizations should therefore encourage governed experimentation and act on practical opportunities while remaining adaptable to future change.