Level 2 taught you to use AI on one task at a time with verification built in. Level 3 teaches you to design the workflow around the task: where the trigger comes from, what the model sees, who approves what, where the record lands, and how you know the whole thing is working a month later. It is written for the people in a small or mid-size manufacturer who own processes rather than tasks: maintenance planners, purchasing leads, production planners, quality managers, continuous-improvement staff, and the engineers and owners who decide which workflows get built.
The course moves through five areas. First, workflow design itself: mapping a process from trigger to approved record, assigning a risk tier to each AI use so the controls match the consequence, and writing the escalation paths and stop conditions that route safety, export-control, customer-specification, and equipment questions to the right person. Second, maintenance records and reliability: structuring work-order histories so a model can summarise them honestly, drafting preventive-maintenance checklists from OEM documents while keeping energy isolation where the OSHA standard puts it, and triaging condition-monitoring alerts without ever letting a model command a machine. Third, supply chain, procurement, and scheduling: supplier communication that protects customer drawings, shortage and alternate-part analysis that stops at engineering approval, and scheduling scenario support that leaves the schedule in the planner's hands. Fourth, knowledge bases and retrieval: building a controlled-document knowledge base that contains only released revisions, writing retrieval prompts that cite document and revision, and connecting AI tools to ERP, MES, QMS, and CMMS on a read-only basis that respects the boundary between information technology and the control network. Fifth, quality assurance and measurement: testing a workflow against a fictional golden set before release, measuring cycle time, error rate, and review burden after release, and working through three fictional plants as extended case studies.
The capstone asks you to design, document, test, and pilot one AI-assisted workflow in your own plant or a fictional one, and to report the measurements. It is reviewed against a published rubric. The final examination is scenario-based and drawn at random from the question bank.
This is an independent educational course offered by AI Coalition Network. It does not replace engineering judgment, an approved procedure, a manufacturer's instruction, a quality management system, an operational-technology security programme, or the laws and regulations that apply to your plant.