Advancements in artificial intelligence (AI) over the past decade have secured its role in and beyond the radiology reading room. AI is increasingly embedded in imaging workflows, either directly by influencing practice with provision of AI results to radiologists for interpretive use cases or indirectly by aiding in noninterpretive use cases for image optimization or workflow efficiency. Although AI solutions show potential to change paradigms for how radiologists practice, the ability to effectively leverage AI will depend on radiologists' AI literacy. Historically, AI implementation has been facilitated by those with prior technical experience, while radiologists accommodate newly deployed tools in existing imaging workflows. However, investing in AI literacy enables wider implementation by empowering radiologists to identify practical limitations that may be overlooked, ensuring safe and effective integration in practice. This article is the first in a primer series providing a foundation in AI literacy for radiologists. "Looking under the hood" of an AI algorithm may be daunting, but understanding how it works at a foundational level is key for making informed decisions using AI as a tool, enabling an understanding of when AI may fail or be prone to bias. This article focuses on practical considerations at each stage of AI development. The following articles in this series will build on this content by examining paradigms for delivery to end users and their interaction with AI results, explaining barriers to AI integration from the perspective of various imaging workflow stakeholders (eg, radiologists, technologists, picture archiving and communication system administrators, imaging informaticists, patients, staff in administrative and executive roles, and others), and detailing postdeployment considerations for AI monitoring and regulation after model procurement and deployment in practice.