A fun, no-jargon introduction for people who are brand new to AI, and a solid refresher for everyone else. Starts from things the audience already uses every day (spam filters, autocorrect, movie recommendations) to show they have been working with AI for years, then builds the plain-language family tree: rules, classic machine learning, and generative AI, with a live demo and some myth-busting along the way. No math, no acronym soup. Then the question on everyone's mind: what does this mean for my job? Real published examples show the consistent pattern of people being repurposed rather than replaced, and why the process knowledge in this room becomes more valuable, not less. Closes with a plain-language first look at automation risks and guardrails: where AI gets things wrong, why over-trusting it is the real hazard, and the simple safeguards (a human in the loop, clear rules for what goes into public tools) that the rest of the series builds on.
Governance thread. Automation risks and guardrails, introduced in plain language, plus acceptable use: simple ground rules for what should and should not go into public AI tools.
Vocabulary. AI vs. machine learning vs. generative AI · large language model (LLM) · prompt · hallucination · model · training vs. inference · copilot / assistant · agent · automation vs. augmentation · human-in-the-loop · guardrails