Stop guessing where AI fits. Start investing with confidence.

A pilot proves AI can do something. An investment decision proves it should, based on whether the business case clears the bar before any tool gets evaluated. As one of our speakers put it, the technology decision comes after the business case, never before. We walk through the exact test for that in the session.

Most companies jump straight to buying, but that's usually the third option, not the first. Before anything else, the real question is whether you're underusing capabilities you already own. The session breaks down all four paths and how to tell which one actually fits your situation.

Not perfect data, but you do need to know if it's accessible where decisions get made, accurate, and consistent across your ERP, CRM, and production systems. We've sat in rooms where "committed cost" meant two completely different numbers to two different teams, and nobody noticed until AI reported both with total confidence. We show you how to catch that before it becomes a problem.

It depends entirely on where your data actually gets processed, and that's a real decision, not a default. If somebody on your team is already pasting company data into a free chatbot, that's worth knowing about before you formalize anything. The session covers the two deployment paths and how to know which one your data requires.

Because most pilots skip two things they actually need: a hard time limit and a measured baseline. A pilot with no end date just becomes a hobby. We show a real example where a three-and-a-half-hour process dropped to ten minutes, but that number only means anything because the baseline was measured first.

Start with whether you can state the business problem in one sentence. If you can't, it's probably not a use case yet, it's still exploration. We walk through this stairstep approach using a real engineer-to-order example, from a messy takeoff all the way to an approved job cost budget.