AI demos are easy. Knowing which AI ideas are worth chasing is the hard part.

Right now, a lot of organizations feel pressure to “do something with AI.” So teams launch pilots, build chatbots, test tools, and run impressive demos. Some of them look great in a meeting. Fewer survive contact with real users, messy data, unclear workflows, and business pressure.

This talk introduces the AI Decision Map, a practical framework for slowing down just enough to make better AI choices. It helps leaders, product teams, and data professionals ask the questions that should come before the build:
- What problem are we solving?
- Does this actually need AI?
- Where should it live in the business?
- How much should the system be allowed to decide?

Drawing from real implementation stories, doctoral research on technology adoption, and years of leading enterprise data and AI work, Vin Mitty will show how teams can move from scattered AI experiments to clearer, more disciplined decisions.

Attendees will learn how to spot problems that are truly worth solving with AI, avoid common pilot traps, think through risk in plain language, and connect AI work to outcomes the business actually cares about.

This is not a technical session about model architecture. It is a practical session for people who need AI to create value, earn trust, and make it past the demo.