This module builds foundational AI literacy by orienting employees in the current landscape: what tools exist, what they can realistically do, and why this skill matters for every role. Learners explore core workplace AI tools, understand myths versus realities, and examine the broader AI tools landscape including popular models. They compare tools using a job-focused matrix, clarify realistic expectations about what AI can and cannot do, and complete a practical lab rewriting an email for a different audience. The module sets expectations for time savings, introduces the overall specialization roadmap, and motivates learners with real workplace use cases.

This module teaches employees how to communicate business intent to AI, translating real work tasks into effective requests without needing to learn formal frameworks yet. Learners reframe AI as a smart intern they are briefing rather than a search engine they are querying. They apply the RACE Framework for prompting, learn to think like an AI collaborator, and study the 5 most common mistakes employees make with AI. The module introduces the Anatomy of a Strong Prompt and the Business Context Method (Goal, Audience, Constraints, Format), illustrated with before-and-after examples of real workplace prompts. Learners also review prompt patterns that always work and complete a lab where they transform their own work request into a strong AI prompt.

This module focuses on applying AI to role-specific, high-impact tasks within each department. Learners explore tailored use cases for Marketing & Content, Sales & Customer Success, HR & People Operations, Finance & Analytics, Operations & Project Management, and Legal, Compliance & Administration. For each function, they review concrete scenarios such as drafting briefs, generating campaign assets, accelerating sales pipelines, streamlining employee lifecycle documentation, turning data into decisions, tightening project operations, and reducing administrative paperwork. Learners receive department prompt starter packs as downloadable PDFs, each containing ready-to-use prompt templates. The module culminates in a knowledge check to reinforce understanding of how AI can be embedded in their day-to-day responsibilities.

This module builds the critical thinking muscle that separates responsible AI users from reckless ones. Learners discover how to verify, refine, and make judgment calls on AI-generated content before it goes anywhere. They apply a structured evaluation checklist to AI outputs, learn when to trust, verify, or reject results, and spot hallucinations, bias, and overconfident nonsense. A fast, repeatable 60-second output review routine helps them quickly assess whether content is factually checkable, matches their expertise, and is something they would sign their name to. The module also covers responsible AI use at work, including what to share, what to keep out, and how to handle sensitive information. Learners use an AI Output Evaluation Checklist reading and complete a knowledge check to consolidate their skills.

This module guides learners to create a personalized 30-day AI adoption plan that identifies their top automatable tasks, selects appropriate tools, and establishes a sustainable AI workflow. Learners conduct a task audit to find their top 5 AI opportunities, using a guided exercise that maps weekly tasks on a 2x2 matrix of time spent versus AI suitability, with a focus on the high time / high AI-fit quadrant. They then explore how to find AI opportunities in practice, build a multi-tool AI strategy, and plan how to talk about AI with their team and manager. A fillable worksheet supports the creation of a concrete 30-day AI adoption plan. Finally, a role-play scenario helps learners practice pitching an AI workflow to their manager, building confidence in advocating for AI-powered changes at work.

The final module consolidates everything learners have covered and helps them articulate their next steps in the specialization. Learners review what they have learned about core AI tools, business prompting, departmental use cases, evaluation practices, and personal action planning. A scenario-based final assessment allows them to demonstrate foundational AI workplace literacy by choosing appropriate actions in realistic workplace situations. The course concludes with a wrap-up that reinforces key takeaways and encourages learners to continue experimenting with AI responsibly and strategically in their roles.