Building an AI-Enabled Engineering Practice (Timed and Monitored)
- Course Number: G1038T
- Credits: 3 hours
- Instructor: Ellen Huang, PE
- Price: $30
Course Outline
Artificial intelligence is rapidly becoming a practical toolset for engineering firms—improving how teams search standards and specifications, draft and review technical documents, manage RFIs and submittals, and strengthen QA/QC consistency. This course provides a structured, engineering-focused guide to adopting AI responsibly. Participants will learn core AI concepts (LLMs, RAG, agents, predictive analytics), effective operating models (practice champions, integrated product teams, and an AI enablement function), data and platform requirements (document governance, BIM/CAD integration, security, and evaluation), and a maturity roadmap for scaling from pilots to enterprise capabilities. Emphasis is placed on professional accountability, confidentiality, traceability, risk-tiered controls, and measurable business outcomes across the project lifecycle.
At the end of this course, there will be a multiple-choice, open-book quiz designed to enhance your understanding of the course material.
Learning Objectives
At the conclusion of this course, the student will:
- Be able to explain key AI concepts and patterns relevant to engineering practice, including generative AI, LLMs, RAG, embeddings, and AI agents.
- Be able to identify and prioritize high-value AI use cases across the engineering project lifecycle, and define success metrics for pilots and deployments.
- Be able to apply responsible AI principles—risk tiering, human review, grounding in authoritative sources, and auditability—to protect quality, safety, and confidentiality.
- Be able to outline the data, technology, and organizational structures needed to scale AI capability, using a maturity model to plan next-step investments.