How is AI generating content and outputs in firms? What are common misconceptions? How can AI support design, documentation, communications, and daily operations? Drawing from the AIA AI Firm Toolkit, this course covers accountability, review, appropriate use, confidentiality, and AI-ready data. By recognizing AI as a tool rather than a solution, you’ll understand the foundational concepts of AI and learn how to leverage it for appropriate workflows.

Rather than focusing solely on what AI can or cannot do today, this course emphasizes durable practices: understanding how AI contributes to work, recognizing when AI outputs may influence decisions or deliverables, and applying professional review before using AI-assisted work. You’ll engage with AI as a changing set of tools and capabilities and understand the importance of prompts, patterns, and professionally reviewing AI-generated work before relying on it.

Using AI responsibly depends on professional judgement, firm policies, approved tools, client expectations, and documentation practices. By understanding AI-related risks, you’ll examine misleading outputs, confidentiality concerns, bias, authorship and attribution issues, and overreliance.

The course connects responsible AI use to familiar professional responsibilities, including accuracy, competence, confidentiality, professional oversight, truthful communication, use of sources, client trust, and public welfare. You'll practice identifying AI-related risks, reviewing AI-generated outputs, applying ethical frameworks, and implementing practical guardrails before AI-assisted work informs project communications, decisions, deliverables, or submissions.

The course emphasizes suitable workflows, safe prompting, human-in-the-loop review, and practical application. You’ll practice identifying tasks that are appropriate for AI support, such as summarizing meeting notes or drafting internal communications, using simple and structured prompts when useful, comparing AI outputs, applying follow-up prompts, and reviewing drafts for accuracy, completeness, tone, confidentiality, and project context

AI tools depend on the quality, context, and reliability of the information they are given. In architectural practice, that information may include drawings, models, specifications, schedules, meeting notes, emails, photos, firm standards, templates, proposals, lessons learned, and other project or firm knowledge.

This course introduces the 5 Cs of AI-ready information as a practical framework for evaluating whether information is complete, consistent, current, credible, and contextualized for a specific AI-supported task. The 5 Cs are not a pass/fail checklist; they help teams assess whether information is sufficiently ready for the intended task, tool, and level of risk. You’ll explore common data risks, consider how poor information quality can affect outputs, and identify steps to support responsible AI u