In this module, learners move from choosing a problem to designing a responsible AI-assisted workflow. They will map the user journey, define clear inputs and outputs, and structure prompt sequences that support consistency rather than guesswork. The module also emphasizes human approval points, fallback plans, risk registers, and test criteria so that learners design workflows people can trust in real business settings. By the end, learners will have a practical blueprint for a draft-and-review process that stays grounded in business needs and responsible use.

This module turns a workflow design into a small working prototype that can be tested with confidence. Learners will build a minimum viable prototype, use prompt templates and synthetic data where appropriate, and evaluate outputs against representative test cases. They will also examine edge cases, document failures, and maintain a revision log that shows how evidence informed improvements. The result is not just a first draft of a solution, but a tested prototype supported by validation steps and responsible safeguards.

This module helps learners determine whether their prototype actually creates value. Rather than assuming AI is helpful, learners will compare the prototype against a baseline using quality rubrics, time savings, error rates, and risk considerations. They will also learn to identify limitations, qualify claims, and translate evidence into a recommendation that stakeholders can act on. By combining measurement with clear business communication, learners will be prepared to recommend whether a pilot should proceed, be revised, or stop.

In the final module, learners bring their work together into a concise, defensible proposal and portfolio artifact. They will synthesize the problem, users, solution demonstration, method, evidence, risk controls, outcomes, and next steps into an executive pitch that shows both practical AI skill and sound judgment. This module is designed to help learners communicate their project with credibility to managers, review panels, or future employers. By the end, learners will have a portfolio-ready case that demonstrates how they can frame, test, evaluate, and present a low-risk AI-assisted business solution from end to end.