The Operator's AI Roadmap: From Prompts to Prediction Engines - Presented by 4 Elements Hospitality

Most attraction operators sit in one of two camps: either AI is something they have not touched yet, or they use it every day and quietly wonder whether they built on a shaky foundation or a foundation that will last. This session serves both.

We start with the story of AI, which runs longer than most people think: decades of slow progress, then a sudden arrival in everyday life. That pattern is exactly why the pace is not slowing down now

Then the tool map by level: Beginner level: knowing which tool to open on a Monday morning for what job Advanced users: getting the piece that most people skip, which is the model landscape itself: language models, reasoning models, vision models, speech models, and forecasting models. Each solves a different problem, and the most common expensive mistake in this industry is pointing a chatbot to a math problem.

Then we go inside a real build. Chef Nicole walks through an AI forecasting and SOP system she guided a development team to build for an F&B operation. Accurate forecasting is the difference between guessing at an order and prep and knowing it. This session shows a forecasting model doing that work in a real operation, giving management more time to take care of their staff.

Attendees leave with a model selection map, a prompt framework, and a realistic picture of what implementation actually takes

Learning Objectives:

1. Identify what exists beyond chatbots, including vision models, embedding models, and forecasting models, and recognize which operating problem each one solves.

2. Apply a repeatable prompt framework to everyday work so output comes back usable the first time instead of needing to rewrite it.

3. Outline what it takes to move AI from occasional use into an operating system, including the data it depends on, where human stays in the loop, and the checkpoints that keep output trustworthy.